357 lines
10 KiB
Python
357 lines
10 KiB
Python
# coding:utf-8
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from PIL import Image
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from pytesseract import *
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from fnmatch import fnmatch
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from queue import Queue
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import matplotlib.pyplot as plt
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import cv2.cv2 as cv2
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import time
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import os
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import sys
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reload(sys)
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sys.setdefaultencoding('utf-8')
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def clear_border(img,img_name):
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'''去除边框
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'''
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filename = './out_img/' + img_name.split('.')[0] + '-clearBorder.jpg'
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h, w = img.shape[:2]
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for y in range(0, w):
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for x in range(0, h):
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# if y ==0 or y == w -1 or y == w - 2:
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if y < 4 or y > w -4:
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img[x, y] = 255
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# if x == 0 or x == h - 1 or x == h - 2:
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if x < 4 or x > h - 4:
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img[x, y] = 255
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cv2.imwrite(filename,img)
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return img
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def interference_line(img, img_name):
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'''
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干扰线降噪
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'''
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filename = './out_img/' + img_name.split('.')[0] + '-interferenceline.jpg'
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h, w = img.shape[:2]
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# ！！！opencv矩阵点是反的
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# img[1,2] 1:图片的高度，2：图片的宽度
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for y in range(1, w - 1):
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for x in range(1, h - 1):
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count = 0
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if img[x, y - 1] > 245:
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count = count + 1
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if img[x, y + 1] > 245:
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count = count + 1
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if img[x - 1, y] > 245:
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count = count + 1
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if img[x + 1, y] > 245:
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count = count + 1
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if count > 2:
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img[x, y] = 255
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cv2.imwrite(filename,img)
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return img
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def interference_point(img,img_name, x = 0, y = 0):
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"""点降噪
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9邻域框,以当前点为中心的田字框,黑点个数
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:param x:
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:param y:
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:return:
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"""
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filename = './out_img/' + img_name.split('.')[0] + '-interferencePoint.jpg'
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# todo 判断图片的长宽度下限
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cur_pixel = img[x,y]# 当前像素点的值
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height,width = img.shape[:2]
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for y in range(0, width - 1):
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for x in range(0, height - 1):
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if y == 0: # 第一行
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if x == 0: # 左上顶点,4邻域
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# 中心点旁边3个点
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sum = int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x + 1, y]) \
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+ int(img[x + 1, y + 1])
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if sum <= 2 * 245:
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img[x, y] = 0
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elif x == height - 1: # 右上顶点
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sum = int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x - 1, y]) \
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+ int(img[x - 1, y + 1])
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if sum <= 2 * 245:
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img[x, y] = 0
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else: # 最上非顶点,6邻域
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sum = int(img[x - 1, y]) \
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+ int(img[x - 1, y + 1]) \
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+ int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x + 1, y]) \
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+ int(img[x + 1, y + 1])
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if sum <= 3 * 245:
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img[x, y] = 0
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elif y == width - 1: # 最下面一行
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if x == 0: # 左下顶点
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# 中心点旁边3个点
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sum = int(cur_pixel) \
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+ int(img[x + 1, y]) \
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+ int(img[x + 1, y - 1]) \
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+ int(img[x, y - 1])
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if sum <= 2 * 245:
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img[x, y] = 0
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elif x == height - 1: # 右下顶点
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sum = int(cur_pixel) \
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+ int(img[x, y - 1]) \
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+ int(img[x - 1, y]) \
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+ int(img[x - 1, y - 1])
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if sum <= 2 * 245:
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img[x, y] = 0
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else: # 最下非顶点,6邻域
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sum = int(cur_pixel) \
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+ int(img[x - 1, y]) \
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+ int(img[x + 1, y]) \
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+ int(img[x, y - 1]) \
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+ int(img[x - 1, y - 1]) \
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+ int(img[x + 1, y - 1])
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if sum <= 3 * 245:
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img[x, y] = 0
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else: # y不在边界
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if x == 0: # 左边非顶点
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sum = int(img[x, y - 1]) \
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+ int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x + 1, y - 1]) \
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+ int(img[x + 1, y]) \
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+ int(img[x + 1, y + 1])
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if sum <= 3 * 245:
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img[x, y] = 0
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elif x == height - 1: # 右边非顶点
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sum = int(img[x, y - 1]) \
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+ int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x - 1, y - 1]) \
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+ int(img[x - 1, y]) \
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+ int(img[x - 1, y + 1])
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if sum <= 3 * 245:
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img[x, y] = 0
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else: # 具备9领域条件的
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sum = int(img[x - 1, y - 1]) \
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+ int(img[x - 1, y]) \
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+ int(img[x - 1, y + 1]) \
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+ int(img[x, y - 1]) \
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+ int(cur_pixel) \
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+ int(img[x, y + 1]) \
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+ int(img[x + 1, y - 1]) \
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+ int(img[x + 1, y]) \
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+ int(img[x + 1, y + 1])
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if sum <= 4 * 245:
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img[x, y] = 0
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cv2.imwrite(filename,img)
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return img
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def _get_dynamic_binary_image(filedir, img_name):
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'''
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自适应阀值二值化
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'''
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filename = './out_img/' + img_name.split('.')[0] + '-binary.jpg'
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img_name = filedir + '/' + img_name
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print('.....' + img_name)
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im = cv2.imread(img_name)
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im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
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th1 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
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cv2.imwrite(filename,th1)
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return th1
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def _get_static_binary_image(img, threshold = 140):
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'''
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手动二值化
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'''
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img = Image.open(img)
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img = img.convert('L')
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pixdata = img.load()
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w, h = img.size
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for y in range(h):
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for x in range(w):
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if pixdata[x, y] < threshold:
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pixdata[x, y] = 0
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else:
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pixdata[x, y] = 255
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return img
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def cfs(im,x_fd,y_fd):
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'''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
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'''
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# print('**********')
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xaxis=[]
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yaxis=[]
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visited =set()
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q = Queue()
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q.put((x_fd, y_fd))
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visited.add((x_fd, y_fd))
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offsets=[(1, 0), (0, 1), (-1, 0), (0, -1)]#四邻域
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while not q.empty():
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x,y=q.get()
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for xoffset,yoffset in offsets:
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x_neighbor,y_neighbor = x+xoffset,y+yoffset
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if (x_neighbor,y_neighbor) in (visited):
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continue # 已经访问过了
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visited.add((x_neighbor, y_neighbor))
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try:
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if im[x_neighbor, y_neighbor] == 0:
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xaxis.append(x_neighbor)
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yaxis.append(y_neighbor)
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q.put((x_neighbor,y_neighbor))
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except IndexError:
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pass
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# print(xaxis)
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if (len(xaxis) == 0 | len(yaxis) == 0):
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xmax = x_fd + 1
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xmin = x_fd
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ymax = y_fd + 1
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ymin = y_fd
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else:
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xmax = max(xaxis)
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xmin = min(xaxis)
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ymax = max(yaxis)
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ymin = min(yaxis)
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#ymin,ymax=sort(yaxis)
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return ymax,ymin,xmax,xmin
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def detectFgPix(im,xmax):
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'''搜索区块起点
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'''
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h,w = im.shape[:2]
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for y_fd in range(xmax+1,w):
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for x_fd in range(h):
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if im[x_fd,y_fd] == 0:
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return x_fd,y_fd
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def CFS(im):
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'''切割字符位置
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'''
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zoneL=[]#各区块长度L列表
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zoneWB=[]#各区块的X轴[起始，终点]列表
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zoneHB=[]#各区块的Y轴[起始，终点]列表
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xmax=0#上一区块结束黑点横坐标,这里是初始化
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for i in range(10):
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try:
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x_fd,y_fd = detectFgPix(im,xmax)
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# print(y_fd,x_fd)
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xmax,xmin,ymax,ymin=cfs(im,x_fd,y_fd)
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L = xmax - xmin
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H = ymax - ymin
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zoneL.append(L)
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zoneWB.append([xmin,xmax])
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zoneHB.append([ymin,ymax])
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except TypeError:
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return zoneL,zoneWB,zoneHB
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return zoneL,zoneWB,zoneHB
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def cutting_img(im,im_position,img,xoffset = 1,yoffset = 1):
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filename = './out_img/' + img.split('.')[0]
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# 识别出的字符个数
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im_number = len(im_position[1])
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# 切割字符
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for i in range(im_number):
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im_start_X = im_position[1][i][0] - xoffset
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im_end_X = im_position[1][i][1] + xoffset
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im_start_Y = im_position[2][i][0] - yoffset
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im_end_Y = im_position[2][i][1] + yoffset
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cropped = im[im_start_Y:im_end_Y, im_start_X:im_end_X]
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cv2.imwrite(filename + '-cutting-' + str(i) + '.jpg',cropped)
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def main():
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filedir = './easy_img'
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for file in os.listdir(filedir):
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if fnmatch(file, '*.jpeg'):
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img_name = file
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# 自适应阈值二值化
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im = _get_dynamic_binary_image(filedir, img_name)
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# 去除边框
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im = clear_border(im,img_name)
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# 对图片进行干扰线降噪
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im = interference_line(im,img_name)
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# 对图片进行点降噪
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im = interference_point(im,img_name)
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# 切割的位置
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im_position = CFS(im)
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maxL = max(im_position[0])
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minL = min(im_position[0])
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# 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
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if(maxL > minL + minL * 0.7):
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maxL_index = im_position[0].index(maxL)
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minL_index = im_position[0].index(minL)
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# 设置字符的宽度
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im_position[0][maxL_index] = maxL // 2
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im_position[0].insert(maxL_index + 1, maxL // 2)
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# 设置字符X轴[起始，终点]位置
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im_position[1][maxL_index][1] = im_position[1][maxL_index][0] + maxL // 2
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im_position[1].insert(maxL_index + 1, [im_position[1][maxL_index][1] + 1, im_position[1][maxL_index][1] + 1 + maxL // 2])
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# 设置字符的Y轴[起始，终点]位置
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im_position[2].insert(maxL_index + 1, im_position[2][maxL_index])
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# 切割字符，要想切得好就得配置参数，通常 1 or 2 就可以
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cutting_img(im,im_position,img_name,1,1)
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# 识别验证码
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cutting_img_num = 0
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for file in os.listdir('./out_img'):
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str_img = ''
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if fnmatch(file, '%s-cutting-*.jpg' % img_name.split('.')[0]):
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cutting_img_num += 1
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for i in range(cutting_img_num):
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try:
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file = './out_img/%s-cutting-%s.jpg' % (img_name.split('.')[0], i)
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# 识别验证码
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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
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except Exception as err:
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pass
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print('切图：%s' % cutting_img_num)
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print('识别为：%s' % str_img)
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if __name__ == '__main__':
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main() |