清理图片，对图片进行二值化，去边框，去干扰线，去点
from PIL import Image from pytesseract import * from fnmatch import fnmatch from queue import Queue import matplotlib.pyplot as plt import cv2 import time import os def clear_border(img,img_name): '''去除边框 ''' h, w = img.shape[:2] for y in range(0, w): for x in range(0, h): # if y ==0 or y == w -1 or y == w - 2: if y < 4 or y > w -4: img[x, y] = 255 # if x == 0 or x == h - 1 or x == h - 2: if x < 4 or x > h - 4: img[x, y] = 255 return img def interference_line(img, img_name): ''' 干扰线降噪 ''' h, w = img.shape[:2] # ！！！opencv矩阵点是反的 # img[1,2] 1:图片的高度，2：图片的宽度 for r in range(0,2): for y in range(1, w - 1): for x in range(1, h - 1): count = 0 if img[x, y - 1] > 245: count = count + 1 if img[x, y + 1] > 245: count = count + 1 if img[x - 1, y] > 245: count = count + 1 if img[x + 1, y] > 245: count = count + 1 if count > 2: img[x, y] = 255 return img def interference_point(img,img_name, x = 0, y = 0): """点降噪 9邻域框,以当前点为中心的田字框,黑点个数 :param x: :param y: :return: """ # todo 判断图片的长宽度下限 cur_pixel = img[x,y]# 当前像素点的值 height,width = img.shape[:2] for y in range(0, width - 1): for x in range(0, height - 1): if y == 0: # 第一行 if x == 0: # 左上顶点,4邻域 # 中心点旁边3个点 sum = int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x + 1, y]) \ + int(img[x + 1, y + 1]) if sum <= 2 * 245: img[x, y] = 0 elif x == height - 1: # 右上顶点 sum = int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x - 1, y]) \ + int(img[x - 1, y + 1]) if sum <= 2 * 245: img[x, y] = 0 else: # 最上非顶点,6邻域 sum = int(img[x - 1, y]) \ + int(img[x - 1, y + 1]) \ + int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x + 1, y]) \ + int(img[x + 1, y + 1]) if sum <= 3 * 245: img[x, y] = 0 elif y == width - 1: # 最下面一行 if x == 0: # 左下顶点 # 中心点旁边3个点 sum = int(cur_pixel) \ + int(img[x + 1, y]) \ + int(img[x + 1, y - 1]) \ + int(img[x, y - 1]) if sum <= 2 * 245: img[x, y] = 0 elif x == height - 1: # 右下顶点 sum = int(cur_pixel) \ + int(img[x, y - 1]) \ + int(img[x - 1, y]) \ + int(img[x - 1, y - 1]) if sum <= 2 * 245: img[x, y] = 0 else: # 最下非顶点,6邻域 sum = int(cur_pixel) \ + int(img[x - 1, y]) \ + int(img[x + 1, y]) \ + int(img[x, y - 1]) \ + int(img[x - 1, y - 1]) \ + int(img[x + 1, y - 1]) if sum <= 3 * 245: img[x, y] = 0 else: # y不在边界 if x == 0: # 左边非顶点 sum = int(img[x, y - 1]) \ + int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x + 1, y - 1]) \ + int(img[x + 1, y]) \ + int(img[x + 1, y + 1]) if sum <= 3 * 245: img[x, y] = 0 elif x == height - 1: # 右边非顶点 sum = int(img[x, y - 1]) \ + int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x - 1, y - 1]) \ + int(img[x - 1, y]) \ + int(img[x - 1, y + 1]) if sum <= 3 * 245: img[x, y] = 0 else: # 具备9领域条件的 sum = int(img[x - 1, y - 1]) \ + int(img[x - 1, y]) \ + int(img[x - 1, y + 1]) \ + int(img[x, y - 1]) \ + int(cur_pixel) \ + int(img[x, y + 1]) \ + int(img[x + 1, y - 1]) \ + int(img[x + 1, y]) \ + int(img[x + 1, y + 1]) if sum <= 4 * 245: img[x, y] = 0 return img def _get_dynamic_binary_image(filedir,img_name): ''' 自适应阀值二值化 ''' filename = './easy_code/' + img_name.split('.')[0] + '-binary.jpg' img_name = filedir + '/' + img_name im = cv2.imread(img_name) im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) th1 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1) return th1 def recognize():
filedir = './images' #验证码路径 for file in os.listdir(filedir): if fnmatch(file, '*.jpg'): img_name = file # 自适应阈值二值化 im = _get_dynamic_binary_image(filedir,img_name) # 去除边框 im = clear_border(im,img_name) # 对图片进行干扰线降噪 im = interference_line(im,img_name) # 对图片进行点降噪 im = interference_point(im,img_name) filename = './easy_code/' + img_name.split('.')[0] + '-interferencePoint.jpg' #easy_code为保存路径 cv2.imwrite(filename,im) #保存图片 recognize()
以上代码改自 老板丶鱼丸粗面 的 《python验证码识别》对于验证码识别大佬那还有跟详细的介绍。
附链接：https://www.cnblogs.com/qqandfqr/p/7866650.html