python 验证码处理

Posted 2020-11-07 mohanling

一、

灰度处理，就是把彩色的验证码图片转为灰色的图片。

二值化，是将图片处理为只有黑白两色的图片，利于后面的图像处理和识别

 1   # 自适应阀值二值化 
 2   def _get_dynamic_binary_image(filedir, img_name):
 3       filename =   ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-binary.jpg‘
 4       img_name = filedir + ‘/‘ + img_name
 5       print(‘.....‘ + img_name)
 6       im =dz.imread(img_name)
 7       im = dz.cvtColor(im,dz.COLOR_BGR2GRAY) #灰值化
 8       # 二值化
 9       th1 = dz.adaptiveThreshold(im, 255, dz.ADAPTIVE_THRESH_GAUSSIAN_C, dz.THRESH_BINARY, 21, 1)
10      
11      dz.imwrite(filename,th1)
12      return th1

二、去除边框

 1 # 去除边框
 2 def clear_border(img,img_name):
 3   filename = ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-clearBorder.jpg‘
 4   h, w = img.shape[:2]
 5   for y in range(0, w):
 6     for x in range(0, h):
 7       if y < 2 or y > w - 2:
 8         img[x, y] = 255
 9       if x < 2 or x > h -2:
10         img[x, y] = 255
11 
12   cv2.imwrite(filename,img)
13   return img

在用OpenCV时，图片的矩阵点是反的，就是长和宽是颠倒的

三、降噪

降噪是验证码处理中比较重要的一个步骤，我这里使用了点降噪和线降噪，，，只能去除细的干扰线

 1 # 干扰线降噪
 2 def interference_line(img, img_name):
 3     filename =  ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-interferenceline.jpg‘
 4     h, w = img.shape[:2]
 5     # ！！opencv矩阵点是反的
 6     # img[1,2] 1:图片的高度，2：图片的宽度
 7     for y in range(1, w - 1):
 8         for x in range(1, h - 1):
 9             count = 0
10             if img[x, y - 1] > 245:
11                 count = count + 1
12             if img[x, y + 1] > 245:
13                 count = count + 1
14             if img[x - 1, y] > 245:
15                 count = count + 1
16             if img[x + 1, y] > 245:
17                 count = count + 1
18             if count > 2:
19                  img[x, y] = 255
20 cv2.imwrite(filename,img)
21 return img

  1 # 点降噪
  2 def interference_point(img,img_name, x = 0, y = 0):
  3     """
  4     9邻域框,以当前点为中心的田字框,黑点个数
  5     :param x:
  6     :param y:
  7     :return:
  8     """
  9     filename =  ‘./out_img/‘ + img_name.split(‘.‘)[0] + ‘-interferencePoint.jpg‘
 10     # todo 判断图片的长宽度下限
 11     cur_pixel = img[x,y]# 当前像素点的值
 12     height,width = img.shape[:2]
 13 
 14     for y in range(0, width - 1):
 15       for x in range(0, height - 1):
 16         if y == 0:  # 第一行
 17             if x == 0:  # 左上顶点,4邻域
 18                 # 中心点旁边3个点
 19                 sum = int(cur_pixel)  20                       + int(img[x, y + 1])  21                       + int(img[x + 1, y])  22                       + int(img[x + 1, y + 1])
 23                 if sum <= 2 * 245:
 24                   img[x, y] = 0
 25             elif x == height - 1:  # 右上顶点
 26                 sum = int(cur_pixel)  27                       + int(img[x, y + 1])  28                       + int(img[x - 1, y])  29                       + int(img[x - 1, y + 1])
 30                 if sum <= 2 * 245:
 31                   img[x, y] = 0
 32             else:  # 最上非顶点,6邻域
 33                 sum = int(img[x - 1, y])  34                       + int(img[x - 1, y + 1])  35                       + int(cur_pixel)  36                       + int(img[x, y + 1])  37                       + int(img[x + 1, y])  38                       + int(img[x + 1, y + 1])
 39                 if sum <= 3 * 245:
 40                   img[x, y] = 0
 41         elif y == width - 1:  # 最下面一行
 42             if x == 0:  # 左下顶点
 43                 # 中心点旁边3个点
 44                 sum = int(cur_pixel)  45                       + int(img[x + 1, y])  46                       + int(img[x + 1, y - 1])  47                       + int(img[x, y - 1])
 48                 if sum <= 2 * 245:
 49                   img[x, y] = 0
 50             elif x == height - 1:  # 右下顶点
 51                 sum = int(cur_pixel)  52                       + int(img[x, y - 1])  53                       + int(img[x - 1, y])  54                       + int(img[x - 1, y - 1])
 55 
 56                 if sum <= 2 * 245:
 57                   img[x, y] = 0
 58             else:  # 最下非顶点,6邻域
 59                 sum = int(cur_pixel)  60                       + int(img[x - 1, y])  61                       + int(img[x + 1, y])  62                       + int(img[x, y - 1])  63                       + int(img[x - 1, y - 1])  64                       + int(img[x + 1, y - 1])
 65                 if sum <= 3 * 245:
 66                   img[x, y] = 0
 67         else:  # y不在边界
 68             if x == 0:  # 左边非顶点
 69                 sum = int(img[x, y - 1])  70                       + int(cur_pixel)  71                       + int(img[x, y + 1])  72                       + int(img[x + 1, y - 1])  73                       + int(img[x + 1, y])  74                       + int(img[x + 1, y + 1])
 75 
 76                 if sum <= 3 * 245:
 77                   img[x, y] = 0
 78             elif x == height - 1:  # 右边非顶点
 79                 sum = int(img[x, y - 1])  80                       + int(cur_pixel)  81                       + int(img[x, y + 1])  82                       + int(img[x - 1, y - 1])  83                       + int(img[x - 1, y])  84                       + int(img[x - 1, y + 1])
 85 
 86                 if sum <= 3 * 245:
 87                   img[x, y] = 0
 88             else:  # 具备9领域条件的
 89                 sum = int(img[x - 1, y - 1])  90                       + int(img[x - 1, y])  91                       + int(img[x - 1, y + 1])  92                       + int(img[x, y - 1])  93                       + int(cur_pixel)  94                       + int(img[x, y + 1])  95                       + int(img[x + 1, y - 1])  96                       + int(img[x + 1, y])  97                       + int(img[x + 1, y + 1])
 98                 if sum <= 4 * 245:
 99                   img[x, y] = 0
100     cv2.imwrite(filename,img)
101     return img

五、字符切割

 1 def cfs(im,x_fd,y_fd):
 2   ‘‘‘用队列和集合记录遍历过的像素坐标代替单纯递归以解决cfs访问过深问题
 3   ‘‘‘
 4 
 5   # print(‘**********‘)
 6 
 7   xaxis=[]
 8   yaxis=[]
 9   visited =set()
10   q = Queue()
11   q.put((x_fd, y_fd))
12   visited.add((x_fd, y_fd))
13   offsets=[(1, 0), (0, 1), (-1, 0), (0, -1)]#四邻域
14 
15   while not q.empty():
16       x,y=q.get()
17 
18       for xoffset,yoffset in offsets:
19           x_neighbor,y_neighbor = x+xoffset,y+yoffset
20 
21           if (x_neighbor,y_neighbor) in (visited):
22               continue  # 已经访问过了
23 
24           visited.add((x_neighbor, y_neighbor))
25 
26           try:
27               if im[x_neighbor, y_neighbor] == 0:
28                   xaxis.append(x_neighbor)
29                   yaxis.append(y_neighbor)
30                   q.put((x_neighbor,y_neighbor))
31 
32           except IndexError:
33               pass
34   # print(xaxis)
35   if (len(xaxis) == 0 | len(yaxis) == 0):
36     xmax = x_fd + 1
37     xmin = x_fd
38     ymax = y_fd + 1
39     ymin = y_fd
40 
41   else:
42     xmax = max(xaxis)
43     xmin = min(xaxis)
44     ymax = max(yaxis)
45     ymin = min(yaxis)
46     #ymin,ymax=sort(yaxis)
47 
48   return ymax,ymin,xmax,xmin
49 
50 def detectFgPix(im,xmax):
51   ‘‘‘搜索区块起点
52   ‘‘‘
53 
54   h,w = im.shape[:2]
55   for y_fd in range(xmax+1,w):
56       for x_fd in range(h):
57           if im[x_fd,y_fd] == 0:
58               return x_fd,y_fd
59 
60 def CFS(im):
61   ‘‘‘切割字符位置
62   ‘‘‘
63 
64   zoneL=[]#各区块长度L列表
65   zoneWB=[]#各区块的X轴[起始，终点]列表
66   zoneHB=[]#各区块的Y轴[起始，终点]列表
67 
68   xmax=0#上一区块结束黑点横坐标,这里是初始化
69   for i in range(10):
70 
71       try:
72           x_fd,y_fd = detectFgPix(im,xmax)
73           # print(y_fd,x_fd)
74           xmax,xmin,ymax,ymin=cfs(im,x_fd,y_fd)
75           L = xmax - xmin
76           H = ymax - ymin
77           zoneL.append(L)
78           zoneWB.append([xmin,xmax])
79           zoneHB.append([ymin,ymax])
80 
81       except TypeError:
82           return zoneL,zoneWB,zoneHB
83 
84   return zoneL,zoneWB,zoneHB

切割粘连字符代码

 1       # 切割的位置
 2       im_position = CFS(im)
 3 
 4       maxL = max(im_position[0])
 5       minL = min(im_position[0])
 6 
 7       # 如果有粘连字符，如果一个字符的长度过长就认为是粘连字符，并从中间进行切割
 8       if(maxL > minL + minL * 0.7):
 9         maxL_index = im_position[0].index(maxL)
10         minL_index = im_position[0].index(minL)
11         # 设置字符的宽度
12         im_position[0][maxL_index] = maxL // 2
13         im_position[0].insert(maxL_index + 1, maxL // 2)
14         # 设置字符X轴[起始，终点]位置
15         im_position[1][maxL_index][1] = im_position[1][maxL_index][0] + maxL // 2
16         im_position[1].insert(maxL_index + 1, [im_position[1][maxL_index][1] + 1, im_position[1][maxL_index][1] + 1 + maxL // 2])
17         # 设置字符的Y轴[起始，终点]位置
18         im_position[2].insert(maxL_index + 1, im_position[2][maxL_index])
19 
20       # 切割字符，要想切得好就得配置参数，通常 1 or 2 就可以
21       cutting_img(im,im_position,img_name,1,1

切割粘连字符代码

 1 def cutting_img(im,im_position,img,xoffset = 1,yoffset = 1):
 2   filename =  ‘./out_img/‘ + img.split(‘.‘)[0]
 3   # 识别出的字符个数
 4   im_number = len(im_position[1])
 5   # 切割字符
 6   for i in range(im_number):
 7     im_start_X = im_position[1][i][0] - xoffset
 8     im_end_X = im_position[1][i][1] + xoffset
 9     im_start_Y = im_position[2][i][0] - yoffset
10     im_end_Y = im_position[2][i][1] + yoffset
11     cropped = im[im_start_Y:im_end_Y, im_start_X:im_end_X]
12     cv2.imwrite(filename + ‘-cutting-‘ + str(i) + ‘.jpg‘,cropped)

六、识别：

识别用的是typesseract库，主要识别一行字符和单个字符时的参数设置，识别中英文的参数设置，代码很简单就一行，我这里大多是filter文件的操作

 1       # 识别验证码
 2       cutting_img_num = 0
 3       for file in os.listdir(‘./out_img‘):
 4         str_img = ‘‘
 5         if fnmatch(file, ‘%s-cutting-*.jpg‘ % img_name.split(‘.‘)[0]):
 6           cutting_img_num += 1
 7       for i in range(cutting_img_num):
 8         try:
 9           file = ‘./out_img/%s-cutting-%s.jpg‘ % (img_name.split(‘.‘)[0], i)
10           # 识别字符
11           str_img = str_img + image_to_string(Image.open(file),lang = ‘eng‘, config=‘-psm 10‘) #单个字符是10，一行文本是7
12         except Exception as err:
13           pass
14       print(‘切图：%s‘ % cutting_img_num)
15       print(‘识别为：%s‘ % str_img