Tunm
3 years ago
committed by
GitHub
commit
7ae4d385e1
@ -1,9 +1,15 @@
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# Editor
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*~
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*.swp
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.vscode
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# Build files
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Prj-Linux/lpr/TEST_*
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Prj-Linux/*build*/
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*.pyc
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/Prj-PHP/build
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/Prj-ROS/build
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/Prj-ROS/devel
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/Prj-ROS/logs
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/Prj-ROS/.catkin_tools
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Binary file not shown.
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After Width: | Height: | Size: 43 KiB |
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cmake_minimum_required(VERSION 3.0.2)
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project(hyperlpr)
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set(SRC_DETECTION src/PlateDetection.cpp src/util.h include/PlateDetection.h)
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set(SRC_FINEMAPPING src/FineMapping.cpp )
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set(SRC_FASTDESKEW src/FastDeskew.cpp )
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set(SRC_SEGMENTATION src/PlateSegmentation.cpp )
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set(SRC_RECOGNIZE src/Recognizer.cpp src/CNNRecognizer.cpp)
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set(SRC_PIPLINE src/Pipeline.cpp)
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set(SRC_SEGMENTATIONFREE src/SegmentationFreeRecognizer.cpp )
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## Find catkin macros and libraries
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## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
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## is used, also find other catkin packages
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find_package(OpenCV REQUIRED)
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find_package(catkin REQUIRED COMPONENTS
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cmake_modules
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roscpp
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rospy
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std_msgs
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sensor_msgs
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geometry_msgs
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cv_bridge
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image_transport
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)
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catkin_package(
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)
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include_directories(
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include
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${catkin_INCLUDE_DIRS}
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${OpenCV_INCLUDE_DIRS}
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)
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add_executable(${PROJECT_NAME}_node ${SRC_DETECTION} ${SRC_FINEMAPPING} ${SRC_FASTDESKEW} ${SRC_SEGMENTATION} ${SRC_RECOGNIZE} ${SRC_PIPLINE} ${SRC_SEGMENTATIONFREE} src/hyperlpr_node.cpp src/main.cpp)
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target_link_libraries(${PROJECT_NAME}_node
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${catkin_LIBRARIES}
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${OpenCV_LIBS}
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)
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//
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// Created by Jack Yu on 21/10/2017.
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//
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#ifndef HYPERPR_CNNRECOGNIZER_H
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#define HYPERPR_CNNRECOGNIZER_H
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#include "Recognizer.h"
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namespace pr {
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class CNNRecognizer : public GeneralRecognizer {
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public:
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const int CHAR_INPUT_W = 14;
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const int CHAR_INPUT_H = 30;
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CNNRecognizer(std::string prototxt, std::string caffemodel);
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label recognizeCharacter(cv::Mat character);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_CNNRECOGNIZER_H
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@ -0,0 +1,17 @@
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//
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// Created by Jack Yu on 22/09/2017.
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//
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#ifndef HYPERPR_FASTDESKEW_H
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#define HYPERPR_FASTDESKEW_H
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#include <math.h>
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#include <opencv2/opencv.hpp>
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namespace pr {
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cv::Mat fastdeskew(cv::Mat skewImage, int blockSize);
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// cv::Mat spatialTransformer(cv::Mat skewImage);
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} // namespace pr
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#endif // HYPERPR_FASTDESKEW_H
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@ -0,0 +1,29 @@
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//
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// Created by Jack Yu on 22/09/2017.
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//
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#ifndef HYPERPR_FINEMAPPING_H
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#define HYPERPR_FINEMAPPING_H
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#include <opencv2/dnn.hpp>
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#include <opencv2/opencv.hpp>
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#include <string>
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namespace pr {
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class FineMapping {
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public:
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FineMapping();
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FineMapping(std::string prototxt, std::string caffemodel);
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static cv::Mat FineMappingVertical(cv::Mat InputProposal, int sliceNum = 15,
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int upper = 0, int lower = -50,
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int windows_size = 17);
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cv::Mat FineMappingHorizon(cv::Mat FinedVertical, int leftPadding,
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int rightPadding);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_FINEMAPPING_H
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//
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// Created by Jack Yu on 22/10/2017.
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//
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#ifndef HYPERPR_PIPLINE_H
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#define HYPERPR_PIPLINE_H
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#include "CNNRecognizer.h"
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#include "FastDeskew.h"
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#include "FineMapping.h"
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#include "PlateDetection.h"
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#include "PlateInfo.h"
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#include "PlateSegmentation.h"
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#include "Recognizer.h"
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#include "SegmentationFreeRecognizer.h"
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namespace pr {
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const std::vector<std::string> CH_PLATE_CODE{
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"京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙",
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"皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵",
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"云", "藏", "陕", "甘", "青", "宁", "新", "0", "1", "2", "3", "4",
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"5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G",
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"H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U",
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"V", "W", "X", "Y", "Z", "港", "学", "使", "警", "澳", "挂", "军",
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"北", "南", "广", "沈", "兰", "成", "济", "海", "民", "航", "空"};
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const int SEGMENTATION_FREE_METHOD = 0;
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const int SEGMENTATION_BASED_METHOD = 1;
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class PipelinePR {
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public:
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GeneralRecognizer *generalRecognizer;
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PlateDetection *plateDetection;
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PlateSegmentation *plateSegmentation;
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FineMapping *fineMapping;
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SegmentationFreeRecognizer *segmentationFreeRecognizer;
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PipelinePR();
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~PipelinePR();
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void initialize(std::string detector_filename,
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std::string finemapping_prototxt,
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std::string finemapping_caffemodel,
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std::string segmentation_prototxt,
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std::string segmentation_caffemodel,
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std::string charRecognization_proto,
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std::string charRecognization_caffemodel,
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std::string segmentationfree_proto,
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std::string segmentationfree_caffemodel);
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std::vector<std::string> plateRes;
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std::vector<PlateInfo> RunPiplineAsImage(cv::Mat plateImage, int method);
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};
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} // namespace pr
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#endif // HYPERPR_PIPLINE_H
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//
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// Created by Jack Yu on 20/09/2017.
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//
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#ifndef HYPERPR_PLATEDETECTION_H
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#define HYPERPR_PLATEDETECTION_H
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#include <PlateInfo.h>
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#include <opencv2/opencv.hpp>
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#include <vector>
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namespace pr {
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class PlateDetection {
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public:
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PlateDetection(std::string filename_cascade);
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PlateDetection();
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void LoadModel(std::string filename_cascade);
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void plateDetectionRough(cv::Mat InputImage,
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std::vector<pr::PlateInfo> &plateInfos,
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int min_w = 36, int max_w = 800);
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// std::vector<pr::PlateInfo> plateDetectionRough(cv::Mat
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// InputImage,int min_w= 60,int max_h = 400);
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// std::vector<pr::PlateInfo>
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// plateDetectionRoughByMultiScaleEdge(cv::Mat InputImage);
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private:
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cv::CascadeClassifier cascade;
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};
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} // namespace pr
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#endif // HYPERPR_PLATEDETECTION_H
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//
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// Created by Jack Yu on 20/09/2017.
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//
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#ifndef HYPERPR_PLATEINFO_H
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#define HYPERPR_PLATEINFO_H
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#include <opencv2/opencv.hpp>
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namespace pr {
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typedef std::vector<cv::Mat> Character;
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enum PlateColor { BLUE, YELLOW, WHITE, GREEN, BLACK, UNKNOWN };
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enum CharType { CHINESE, LETTER, LETTER_NUMS, INVALID };
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class PlateInfo {
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public:
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std::vector<std::pair<CharType, cv::Mat>> plateChars;
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std::vector<std::pair<CharType, cv::Mat>> plateCoding;
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float confidence = 0;
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PlateInfo(const cv::Mat &plateData, std::string plateName, cv::Rect plateRect,
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PlateColor plateType) {
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licensePlate = plateData;
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name = plateName;
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ROI = plateRect;
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Type = plateType;
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}
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PlateInfo(const cv::Mat &plateData, cv::Rect plateRect,
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PlateColor plateType) {
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licensePlate = plateData;
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ROI = plateRect;
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Type = plateType;
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}
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PlateInfo(const cv::Mat &plateData, cv::Rect plateRect) {
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licensePlate = plateData;
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ROI = plateRect;
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}
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PlateInfo() {}
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cv::Mat getPlateImage() { return licensePlate; }
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void setPlateImage(cv::Mat plateImage) { licensePlate = plateImage; }
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cv::Rect getPlateRect() { return ROI; }
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void setPlateRect(cv::Rect plateRect) { ROI = plateRect; }
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cv::String getPlateName() { return name; }
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void setPlateName(cv::String plateName) { name = plateName; }
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int getPlateType() { return Type; }
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void appendPlateChar(const std::pair<CharType, cv::Mat> &plateChar) {
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plateChars.push_back(plateChar);
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}
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void appendPlateCoding(const std::pair<CharType, cv::Mat> &charProb) {
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plateCoding.push_back(charProb);
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}
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std::string decodePlateNormal(std::vector<std::string> mappingTable) {
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std::string decode;
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for (auto plate : plateCoding) {
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float *prob = (float *)plate.second.data;
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if (plate.first == CHINESE) {
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decode += mappingTable[std::max_element(prob, prob + 31) - prob];
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confidence += *std::max_element(prob, prob + 31);
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}
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else if (plate.first == LETTER) {
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decode += mappingTable[std::max_element(prob + 41, prob + 65) - prob];
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confidence += *std::max_element(prob + 41, prob + 65);
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}
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else if (plate.first == LETTER_NUMS) {
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decode += mappingTable[std::max_element(prob + 31, prob + 65) - prob];
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confidence += *std::max_element(prob + 31, prob + 65);
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} else if (plate.first == INVALID) {
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decode += '*';
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}
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}
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name = decode;
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confidence /= 7;
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return decode;
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}
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private:
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cv::Mat licensePlate;
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cv::Rect ROI;
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std::string name;
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PlateColor Type;
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};
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} // namespace pr
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#endif // HYPERPR_PLATEINFO_H
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#ifndef HYPERPR_PLATESEGMENTATION_H
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#define HYPERPR_PLATESEGMENTATION_H
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#include "PlateInfo.h"
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#include "opencv2/opencv.hpp"
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#include <opencv2/dnn.hpp>
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namespace pr {
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class PlateSegmentation {
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public:
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const int PLATE_NORMAL = 6;
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const int PLATE_NORMAL_GREEN = 7;
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const int DEFAULT_WIDTH = 20;
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PlateSegmentation(std::string phototxt, std::string caffemodel);
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PlateSegmentation() {}
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void segmentPlatePipline(PlateInfo &plateInfo, int stride,
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std::vector<cv::Rect> &Char_rects);
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void segmentPlateBySlidingWindows(cv::Mat &plateImage, int windowsWidth,
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int stride, cv::Mat &respones);
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void templateMatchFinding(const cv::Mat &respones, int windowsWidth,
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std::pair<float, std::vector<int>> &candidatePts);
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void refineRegion(cv::Mat &plateImage, const std::vector<int> &candidatePts,
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const int padding, std::vector<cv::Rect> &rects);
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void ExtractRegions(PlateInfo &plateInfo, std::vector<cv::Rect> &rects);
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cv::Mat classifyResponse(const cv::Mat &cropped);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_PLATESEGMENTATION_H
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@ -0,0 +1,22 @@
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//
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// Created by Jack Yu on 20/10/2017.
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//
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#ifndef HYPERPR_RECOGNIZER_H
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#define HYPERPR_RECOGNIZER_H
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#include "PlateInfo.h"
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#include "opencv2/dnn.hpp"
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namespace pr {
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typedef cv::Mat label;
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class GeneralRecognizer {
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public:
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virtual label recognizeCharacter(cv::Mat character) = 0;
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// virtual cv::Mat SegmentationFreeForSinglePlate(cv::Mat plate) =
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// 0;
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void SegmentBasedSequenceRecognition(PlateInfo &plateinfo);
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void SegmentationFreeSequenceRecognition(PlateInfo &plateInfo);
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};
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} // namespace pr
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#endif // HYPERPR_RECOGNIZER_H
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@ -0,0 +1,27 @@
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//
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// Created by Jack Yu on 28/11/2017.
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//
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#ifndef HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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#define HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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#include "Recognizer.h"
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namespace pr {
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class SegmentationFreeRecognizer {
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public:
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const int CHAR_INPUT_W = 14;
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const int CHAR_INPUT_H = 30;
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const int CHAR_LEN = 84;
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SegmentationFreeRecognizer(std::string prototxt, std::string caffemodel);
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std::pair<std::string, float>
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SegmentationFreeForSinglePlate(cv::Mat plate,
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std::vector<std::string> mapping_table);
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private:
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cv::dnn::Net net;
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};
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} // namespace pr
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#endif // HYPERPR_SEGMENTATIONFREERECOGNIZER_H
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@ -0,0 +1,107 @@
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//
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// Created by Jack Yu on 26/10/2017.
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//
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#ifndef HYPERPR_NIBLACKTHRESHOLD_H
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#define HYPERPR_NIBLACKTHRESHOLD_H
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#include <opencv2/opencv.hpp>
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#include <opencv2/imgproc/types_c.h>
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using namespace cv;
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enum LocalBinarizationMethods {
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BINARIZATION_NIBLACK =
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0, //!< Classic Niblack binarization. See @cite Niblack1985 .
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BINARIZATION_SAUVOLA = 1, //!< Sauvola's technique. See @cite Sauvola1997 .
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BINARIZATION_WOLF = 2, //!< Wolf's technique. See @cite Wolf2004 .
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BINARIZATION_NICK = 3 //!< NICK technique. See @cite Khurshid2009 .
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};
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void niBlackThreshold(InputArray _src, OutputArray _dst, double maxValue,
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int type, int blockSize, double k,
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int binarizationMethod) {
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// Input grayscale image
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Mat src = _src.getMat();
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CV_Assert(src.channels() == 1);
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CV_Assert(blockSize % 2 == 1 && blockSize > 1);
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if (binarizationMethod == BINARIZATION_SAUVOLA) {
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CV_Assert(src.depth() == CV_8U);
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}
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type &= THRESH_MASK;
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// Compute local threshold (T = mean + k * stddev)
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// using mean and standard deviation in the neighborhood of each pixel
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// (intermediate calculations are done with floating-point precision)
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||||
Mat test;
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Mat thresh;
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{
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||||
// note that: Var[X] = E[X^2] - E[X]^2
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Mat mean, sqmean, variance, stddev, sqrtVarianceMeanSum;
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||||
double srcMin, stddevMax;
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boxFilter(src, mean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
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true, BORDER_REPLICATE);
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sqrBoxFilter(src, sqmean, CV_32F, Size(blockSize, blockSize), Point(-1, -1),
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true, BORDER_REPLICATE);
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variance = sqmean - mean.mul(mean);
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sqrt(variance, stddev);
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switch (binarizationMethod) {
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case BINARIZATION_NIBLACK:
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thresh = mean + stddev * static_cast<float>(k);
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||||
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break;
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case BINARIZATION_SAUVOLA:
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thresh = mean.mul(1. + static_cast<float>(k) * (stddev / 128.0 - 1.));
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break;
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case BINARIZATION_WOLF:
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minMaxIdx(src, &srcMin, NULL);
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minMaxIdx(stddev, NULL, &stddevMax);
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thresh =
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mean - static_cast<float>(k) *
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(mean - srcMin - stddev.mul(mean - srcMin) / stddevMax);
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break;
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||||
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
|
||||
@ -0,0 +1,7 @@
|
||||
<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>
|
||||
@ -0,0 +1,133 @@
|
||||
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
|
||||
Focal Shape Size: 0.05000000074505806
|
||||
Invert Z Axis: false
|
||||
Name: Current View
|
||||
Near Clip Distance: 0.009999999776482582
|
||||
Pitch: 0.785398006439209
|
||||
Target Frame: <Fixed Frame>
|
||||
Yaw: 0.785398006439209
|
||||
Saved: ~
|
||||
Window Geometry:
|
||||
Displays:
|
||||
collapsed: false
|
||||
Height: 1600
|
||||
Hide Left Dock: false
|
||||
Hide Right Dock: false
|
||||
Image:
|
||||
collapsed: false
|
||||
QMainWindow State: 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
|
||||
Selection:
|
||||
collapsed: false
|
||||
Time:
|
||||
collapsed: false
|
||||
Tool Properties:
|
||||
collapsed: false
|
||||
Views:
|
||||
collapsed: false
|
||||
Width: 3072
|
||||
X: 904
|
||||
Y: 2934
|
||||
Binary file not shown.
@ -0,0 +1,123 @@
|
||||
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"
|
||||
}
|
||||
Binary file not shown.
@ -0,0 +1,95 @@
|
||||
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"
|
||||
}
|
||||
Binary file not shown.
@ -0,0 +1,454 @@
|
||||
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"
|
||||
}
|
||||
|
||||
Binary file not shown.
@ -0,0 +1,114 @@
|
||||
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 {
|
||||
num_output: 3
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
layer {
|
||||
name: "prob"
|
||||
type: "Softmax"
|
||||
bottom: "dense_10"
|
||||
top: "prob"
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,58 @@
|
||||
<?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>
|
||||
@ -0,0 +1,21 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,104 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,165 @@
|
||||
#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
|
||||
@ -0,0 +1,82 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,31 @@
|
||||
#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
|
||||
@ -0,0 +1,305 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,22 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,87 @@
|
||||
//
|
||||
// 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
|
||||
@ -0,0 +1,48 @@
|
||||
#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);
|
||||
}
|
||||
@ -0,0 +1,17 @@
|
||||
#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;
|
||||
}
|
||||
@ -0,0 +1,64 @@
|
||||
//
|
||||
// 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
|
||||
Loading…
Reference in New Issue