标签：position spl pytho pes ... 矩阵 sse visit gray
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
标签：position spl pytho pes ... 矩阵 sse visit gray
原文地址：https://www.cnblogs.com/bwling/p/9067128.html
