import cv2
import os
from math import *
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
# 当前所在的目录
dir_path = os.path.dirname(os.path.realpath(__file__))
img = cv2.imread(dir_path + "/code_img/randomCode1.jpeg")
height, width = img.shape[:2]
degree = 15
# 旋转后的尺寸
heightNew = int(width*fabs(sin(radians(degree)))+height*fabs(cos(radians(degree))))
widthNew = int(height*fabs(sin(radians(degree)))+width*fabs(cos(radians(degree))))
matRotation = cv2.getRotationMatrix2D((width/2, height/2), degree, 1)
matRotation[0, 2] += (widthNew-width)/2 # 重点在这步，目前不懂为什么加这步
matRotation[1, 2] += (heightNew-height)/2 # 重点在这步
imgRotation = cv2.warpAffine(img, matRotation, (widthNew, heightNew), borderValue=(255, 255, 255))
cv2.imshow("img", img)
cv2.imshow("imgRotation", imgRotation)
cv2.waitKey(0)
#!/usr/local/python3
# -*- coding: utf-8 -*-
# @Date : 2018-04-15 09:00:00
# @Author : Canon
# @Link : https://www.python.org
# @Version : 3.6.1
"""
pip install Pillow
pip install pytesseract
pip install numpy
pip install matplotlib
pip install opencv-contrib-python / pip install opencv-python
将 cv2.cp36-win_amd64.pyd, cv.py 文件移入python包所在的文件夹 (python安装位置/lib/site-packages)
https://www.cnblogs.com/qqandfqr/p/7866650.html
http://www.360doc.com/content/17/0225/20/28294195_631980376.shtml
参考链接：
tesserocr GitHub：https://github.com/sirfz/tesserocr
tesserocr PyPI：https://pypi.python.org/pypi/tesserocr
pytesserocr GitHub：https://github.com/madmaze/pytesseract
pytesserocr PyPI：https://pypi.org/project/pytesseract/
tesseract下载地址：http://digi.bib.uni-mannheim.de/tesseract
tesseract GitHub：https://github.com/tesseract-ocr/tesseract
tesseract 语言包：https://github.com/tesseract-ocr/tessdata
tesseract文档：https://github.com/tesseract-ocr/tesseract/wiki/Documentation
"""
from PIL import Image
import pytesseract
from pytesseract import image_to_string
from fnmatch import fnmatch
from queue import Queue
import matplotlib.pyplot as plt
import cv2
import time
import os
# 当前所在的目录
dir_path = os.path.dirname(os.path.realpath(__file__))
def clear_border(img, img_name):
'''
去除边框
'''
filename = dir_path + '/out_img/' + img_name.split('.')[0] + '-clearBorder.jpg'
# 显示图像尺寸, 0: 图片宽度, 1: 图片高度, 2: 图片通道数
h, w = img.shape[:2]
# ！！！opencv矩阵点是反的
# 遍历像素点, 找到四个边框上的所有点, 把他们都改为白色
for y in range(0, w):
for x in range(0, h):
if y == 0 or y == w-1:
img[x, y] = 255
if x == 0 or x == h-1:
# 白色
img[x, y] = 255
cv2.imwrite(filename, img)
return img
def interference_line(img, img_name):
'''
干扰线降噪
'''
filename = dir_path + '/out_img/' + img_name.split('.')[0] + '-interferenceline.jpg'
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 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
cv2.imwrite(filename, img)
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:
"""
filename = dir_path + '/out_img/' + img_name.split('.')[0] + '-interferencePoint.jpg'
# todo 判断图片的长宽度下限
# 当前像素点的值
cur_pixel = img[x, y]
height, width = img.shape[:2]
for y in range(0, width - 1):
for x in range(0, height - 1):
# 第一行
if y == 0:
# 左上顶点, 4邻域
if x == 0:
# 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
# 右上顶点
elif x == height - 1:
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
# 最下面一行
elif y == width - 1:
# 左下顶点
if x == 0:
# 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
# 右下顶点
elif x == height - 1:
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
# 最下非顶点,6邻域
else:
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
# y不在边界
else:
# 左边非顶点
if x == 0:
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
# 右边非顶点
elif x == height - 1:
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
# 具备9领域条件的
else:
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
cv2.imwrite(filename, img)
return img
def _get_dynamic_binary_image(filedir, img_name):
'''
自适应阀值二值化
'''
filename = dir_path + '/out_img/' + img_name.split('.')[0] + '-binary.jpg'
filename1 = dir_path + '/out_img/' + img_name.split('.')[0] + '-rotate.jpg'
img_name = filedir + '/' + img_name
# 图片旋转10°
image = Image.open(img_name)
im2 = image.rotate(10)
# 将旋转后的黑色区域改为白色
img_array = im2.load()
w, h = im2.size
for y in range(0, h):
for x in range(0, w):
if img_array[x, y] == (0, 0, 0):
img_array[x, y] = (255, 255, 255)
im2.save(filename)
im2.save(filename1)
# 读取图片
im = cv2.imread(filename)
# 图片灰值化
im = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# 图片二值化
th1 = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
cv2.imwrite(filename, th1)
return th1
def _get_static_binary_image(img, threshold=140):
'''
手动二值化
'''
img = Image.open(img)
img = img.convert('L')
pixdata = img.load()
w, h = img.size
for y in range(h):
for x in range(w):
if pixdata[x, y] < threshold:
pixdata[x, y] = 0
else:
pixdata[x, y] = 255
return img
def cfs(im, x_fd, y_fd):
'''
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
'''
xaxis = []
yaxis = []
visited = set()
q = Queue()
q.put((x_fd, y_fd))
visited.add((x_fd, y_fd))
# 四邻域
offsets = [(1, 0), (0, 1), (-1, 0), (0, -1)]
while not q.empty():
x, y = q.get()
for xoffset, yoffset in offsets:
x_neighbor, y_neighbor = x + xoffset, y + yoffset
if (x_neighbor,y_neighbor) in (visited):
# 已经访问过了
continue
visited.add((x_neighbor, y_neighbor))
try:
if im[x_neighbor, y_neighbor] == 0:
xaxis.append(x_neighbor)
yaxis.append(y_neighbor)
q.put((x_neighbor,y_neighbor))
except IndexError:
pass
# print(xaxis)
if (len(xaxis) == 0 | len(yaxis) == 0):
xmax = x_fd + 1
xmin = x_fd
ymax = y_fd + 1
ymin = y_fd
else:
xmax = max(xaxis)
xmin = min(xaxis)
ymax = max(yaxis)
ymin = min(yaxis)
# ymin, ymax = sort(yaxis)
return ymax,ymin,xmax,xmin
def detectFgPix(im, xmax):
'''
搜索区块起点
'''
h, w = im.shape[:2]
for y_fd in range(xmax+1, w):
for x_fd in range(h):
if im[x_fd, y_fd] == 0:
return x_fd, y_fd
def CFS(im):
'''
切割字符位置
思路:
字符切割的思路就是找到一个黑色的点，然后在遍历与他相邻的黑色的点，
直到遍历完所有的连接起来的黑色的点，找出这些点中的最高的点、最低的点、最右边的点、最左边的点，
记录下这四个点，认为这是一个字符，然后在向后遍历点，直至找到黑色的点，继续以上的步骤
最后通过每个字符的四个点进行切割
'''
# 各区块长度L列表
zoneL = []
# 各区块的X轴 [起始，终点]列表
zoneWB = []
# 各区块的Y轴 [起始，终点]列表
zoneHB = []
# 上一区块结束黑点横坐标,这里是初始化
xmax = 0
for i in range(10):
try:
x_fd, y_fd = detectFgPix(im, xmax)
# print(y_fd, x_fd)
xmax, xmin, ymax, ymin = cfs(im, x_fd, y_fd)
L = xmax - xmin
H = ymax - ymin
zoneL.append(L)
zoneWB.append([xmin, xmax])
zoneHB.append([ymin, ymax])
except TypeError:
return zoneL, zoneWB, zoneHB
return zoneL, zoneWB, zoneHB
def cutting_img(im, im_position, img, xoffset=1, yoffset=1):
filename = dir_path + '/out_img/' + img.split('.')[0]
# 识别出的字符个数
im_number = len(im_position[1])
# 切割字符
for i in range(im_number):
im_start_X = im_position[1][i][0] - xoffset
im_end_X = im_position[1][i][1] + xoffset
im_start_Y = im_position[2][i][0] - yoffset
im_end_Y = im_position[2][i][1] + yoffset
cropped = im[im_start_Y:im_end_Y, im_start_X:im_end_X]
cv2.imwrite(filename + '-cutting-' + str(i) + '.jpg',cropped)
def main():
filedir = dir_path + '/code_img'
# pytesseract.pytesseract.tesseract_cmd='D:/Tesseract-OCR/tesseract.exe'
for file in os.listdir(filedir):
if fnmatch(file, '*.jpeg'):
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)
# 切割的位置
im_position = CFS(im)
maxL = max(im_position[0])
minL = min(im_position[0])
# 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
num_val = minL + minL * 0.7
if maxL > num_val:
maxL_index = im_position[0].index(maxL)
minL_index = im_position[0].index(minL)
# 设置字符的宽度
im_position[0][maxL_index] = maxL // 2
im_position[0].insert(maxL_index + 1, maxL // 2)
# 设置字符X轴 [起始，终点] 位置
im_position[1][maxL_index][1] = im_position[1][maxL_index][0] + maxL // 2
im_position[1].insert(maxL_index + 1,
[im_position[1][maxL_index][1] + 1,
im_position[1][maxL_index][1] + 1 + maxL // 2])
# 设置字符的Y轴 [起始，终点] 位置
im_position[2].insert(maxL_index + 1, im_position[2][maxL_index])
# 切割字符，要想切得好就得配置参数，通常 1 or 2 就可以
cutting_img(im, im_position,img_name, 1, 1)
# 识别验证码
cutting_img_num = 0
for file in os.listdir(dir_path + '/out_img'):
str_img = ''
if fnmatch(file, '%s-cutting-*.jpg' % img_name.split('.')[0]):
cutting_img_num += 1
for i in range(cutting_img_num):
try:
file = dir_path + '/out_img/%s-cutting-%s.jpg' % (img_name.split('.')[0], i)
# 识别验证码, 单个字符是10，一行文本是7
str_img = str_img + image_to_string(Image.open(file))
except Exception as err:
pass
print('切图：%s' % cutting_img_num)
print('识别为：%s' % str_img)
if __name__ == '__main__':
main()
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