init

README.md

@@ -1,2 +1,24 @@
-# hpyerlpr
 # 车牌识别
+
+### Pipeline
+
+step1. 使用opencv 的 HAAR cascade 检测车牌大致位置 
+
+step2. Extend 检测到的大致位置的矩形区域
+
+step3. 使用类似于mser的方式的多级二值化+ransac拟合车牌的上下边界
+
+step4. 使用CNN regression回归车牌左右边界
+
+step5. 使用CNN滑动窗切割字符
+
+step6. 使用CNN识别字符
+
+### 简单测试方式
+
+```python
+from hyperlpr import  pipline as  pp
+import cv2
+image = cv2.imread("filename")
+image,res  = pp.SimpleRecognizePlate(image)
+```

batch.py

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+#coding=utf-8
+import os
+from hyperlpr import  pipline as  pp
+
+import cv2
+
+parent= "/Users/yujinke/车牌图片/收费站_完成标注"
+
+for filename in os.listdir(parent):
+    path = os.path.join(parent,filename)
+    print path
+    if path.endswith(".jpg") or path.endswith(".png"):
+        image =  cv2.imread(path)
+
+        image,res  = pp.SimpleRecognizePlate(image)
+        cv2.imshow("image",image)
+        cv2.waitKey(0)

benchmark.py

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+#coding=utf-8
+import os
+import numpy as np
+import cv2
+import json
+from hyperlpr import  pipline as  pp
+import sys
+from Levenshtein import StringMatcher as sm
+
+reload(sys)
+sys.setdefaultencoding("utf-8")
+# parent= "/Users/yujinke/车牌图片/云南车牌"
+parent= "/Users/yujinke/车牌图片/收费站_完成标注"
+
+# parent= "./cache/bad2"
+
+
+
+
+def comparestring(a,b):
+    g = 0
+    if len(a) == len(b):
+        for x,y in zip(a,b):
+            if x!=y:
+                g+=1
+    return g
+
+
+count = 0 ;
+
+count_p = 0
+count_d = 0
+count_lev = 0
+count_undetected = 0
+
+roi = [470,400,650,580]
+
+for filename in os.listdir(parent):
+
+    path = os.path.join(parent,filename)
+    print path
+
+    if path.endswith(".jpg") or path.endswith(".png"):
+        ics,name = os.path.split(path)
+
+
+        name,ext = name.split(".")
+
+        image =  cv2.imread(path)
+        image = image[roi[1]:roi[1]+roi[3],roi[0]:roi[0]+roi[2]]
+        # cv2.imshow("test",image)
+        # cv2.waitKey(0)
+        info,dataset = pp.SimpleRecognizePlate(image)
+        ext = ext.strip()
+        name = name.strip()
+
+        if len(dataset)==0:
+            count_undetected +=1
+
+            # cv2.imwrite("./cache/bad2/" + name + ".png", image)
+
+        for one in dataset:
+
+            # p = sm.StringMatcher(seq1=one.encode("utf-8"),seq2=name.encode("utf-8"))
+            A = one.encode("utf-8")
+            B = name.encode("utf-8")
+            print one.encode("utf-8"),"<->",name.encode("utf-8"),"编辑距离:",comparestring(A,B)
+            if  comparestring(A,B)<2:
+                count_lev+=1
+            if one.encode("utf-8") == name.encode("utf-8"):
+                count_p+=1
+                break
+            else:
+                print "error",one.encode("utf-8"), name.encode("utf-8")
+                count_d+=1
+                # cv2.imshow("image",image)
+                # cv2.waitKey(0)
+                cv2.imwrite("./cache/bad2/"+name+".png",image)
+                break
+
+
+
+
+
+        count+=1
+        print count_p / float(count),"编辑距离[1]:",count_lev/float(count),u"识出",count_p,u"总数",count,u"未识出",count_d,u"未检测出",count_undetected
+
+        if count_p+count_d+count_undetected!=count:
+            print dataset,len(dataset)
+            exit(0)
+
+
+        #
+        # cv2.imshow("image",image)
+        # cv2.waitKey(0)
+
+# print count_p/float(count)