Python爬虫笔记【一】模拟用户访问之验证码清理（4）

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