强智教务系统验证码识别 OpenCV
强智教务系统验证码识别 OpenCV
强智教务系统验证码验证码字符位置相对固定，比较好切割
找准切割位置，将其分为四部分，匹配自建库即可，识别率近乎100%，如果觉得不错，点个star吧
https://github.com/WindrunnerMax/SWVerifyCode 提供Java、PHP、Python、JavaScript版本
首先使用代码切割验证码，挑选出切割的比较好的验证码，制作比对库
由于使用matchTemplate函数，要求待匹配图必须比库图小，于是需要放大库图边界
TestImgCut.py切割图片并挑选合适切割位置
#!/usr/bin/python
# -*- coding: utf-8 -*-
from fnmatch import fnmatch
from queue import Queue
import matplotlib.pyplot as plt
import cv2
import time
import os
from Convert import Convert
import requests
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
'''
# print('**********')
xaxis=[]
yaxis=[]
visited =set()
q = Queue()
q.put((x_fd, y_fd))
visited.add((x_fd, y_fd))
offsets=[(1, 0), (0, 1), (-1, 0), (0, -1)]#四邻域
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):
continue # 已经访问过了
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
'''
zoneL=[]#各区块长度L列表
zoneWB=[]#各区块的X轴[起始，终点]列表
zoneHB=[]#各区块的Y轴[起始，终点]列表
xmax=0#上一区块结束黑点横坐标,这里是初始化
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,xoffset = 1,yoffset = 1):
# 识别出的字符个数
im_number = len(im_position[1])
if(im_number>=4): im_number = 4;
imgArr = []
# 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]
imgArr.append(cropped)
cv2.imwrite(str(i)+"v.jpg",cropped) # 查看切割效果
return im_number,imgArr
def main():
cvt = Convert()
req = requests.get("http://XXXXXXXXXXXXXXXXXXX/verifycode.servlet")
img = cvt.run(req.content)
cv2.imwrite("v.jpg",img)
#切割的位置
im_position = CFS(img) # Auto
print(im_position)
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
if(maxL > minL + minL * 0.7):
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_num,imgArr = cutting_img(img,im_position,1,1)
# # 直接使用库读取图片识别验证码
# result=""
# for i in range(cutting_img_num):
# try:
# template = imgArr[i]
# tempResult=""
# matchingDegree=0.0
# filedirWarehouse = '../../Warehouse/StrIntell/'
# for fileImg in os.listdir(filedirWarehouse):
# if fnmatch(fileImg, '*.jpg'):
# # print(file)
# img = cv2.imread(filedirWarehouse+fileImg,0)
# res = cv2.matchTemplate(img,template,3) #img原图 template模板 用模板匹配原图
# min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
# # print(str(i)+" "+file.split('.')[0]+" "+str(max_val))
# if(max_val>matchingDegree):
# tempResult=fileImg.split('.')[0]
# matchingDegree=max_val
# result+=tempResult
# matchingDegree=0.0
# except Exception as err:
# print("ERROR "+ str(err))
# pass
# print('切图：%s' % cutting_img_num)
# print('识别为：%s' % result)
if __name__ == '__main__':
main()
resize.py改变图片边界
import cv2
from fnmatch import fnmatch
import os
def main():
filedir = './StrIntell'
for file in os.listdir(filedir):
if fnmatch(file, '*.jpg'):
fileLoc=filedir+"/"+file
img=cv2.imread(fileLoc)
# img=cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_CONSTANT,value=[255,255,255]) # 扩大
# img = img[0:25, 0:25] # 裁剪 高*宽
print(img.shape)
cv2.imwrite(fileLoc, img)
if __name__ == '__main__':
main()
挑选好合适的库图片并将其resize
使用TestImgCut.py直接读库识别验证码
根据效果挑选合适的切割位置并保存起来
当觉得库文件与切割位置合适，将图片转为list并保存在变量
保存在变量的主要目的是可以直接读取到内存，避免频繁读硬盘造成时间浪费
binary.py转字符为变量
import cv2
import os
from fnmatch import fnmatch
import numpy as np
np.set_printoptions(threshold=np.inf) # 不省略输出
if __name__ == '__main__':
binary = ""
for fileImg in os.listdir("StrIntell/"):
if fnmatch(fileImg, '*.jpg'):
img = cv2.imread("StrIntell/"+fileImg,0)
binary = binary + "'" +fileImg.split(".")[0] + "'" + ":" + str(img.tolist()) + ","
# cv2.imwrite("test.jpg", np.array(img.tolist()))
binary = "charMap = {" + binary + "}"
with open("CharMap.py",'w+') as f:
f.write(binary)
CharMap.py字符变量
charMap = {'1':[[255, 255, 254, 254, 255, 251, 254, 255, 254, 253, 254, 255, 255], [252, 254, 251, 255, 255, 254, 255, 255, 254, 253, 254, 255, 255], [255, 249, 255, 252, 248, 255, 250, 255, 252, 252, 253, 254, 254], [253, 255, 250, 255, 249, 255, 1, 0, 251, 252, 253, 253, 254], [253, 255, 250, 253, 5, 1, 3, 0, 253, 254, 254, 254, 253], [254, 251, 255, 253, 0, 0, 5, 2, 253, 255, 255, 254, 253], [254, 254, 250, 255, 252, 254, 2, 0, 251, 253, 255, 255, 254], [254, 250, 255, 255, 255, 254, 3, 5, 250, 252, 254, 254, 254], [255, 255, 248, 255, 249, 254, 2, 0, 255, 255, 255, 253, 255], [252, 255, 251, 255, 255, 253, 1, 0, 254, 255, 253, 254, 255], [255, 251, 254, 255, 250, 254, 2, 0, 255, 255, 252, 253, 251], [253, 255, 252, 253, 248, 253, 0, 6, 255, 251, 254, 252, 251], [255, 250, 255, 249, 255, 255, 0, 2, 250, 255, 253, 255, 254], [254, 255, 253, 255, 0, 0, 2, 1, 1, 2, 254, 251, 255], [254, 254, 247, 255, 0, 3, 3, 0, 3, 3, 254, 251, 254], [252, 253, 255, 252, 255, 255, 251, 255, 254, 254, 253, 255, 252], [255, 255, 255, 249, 255, 253, 255, 252, 255, 255, 252, 251, 254]],'2':[[249, 255, 251, 254, 255, 253, 253, 253, 255, 255, 252, 251, 255], [255, 253, 255, 251, 249, 255, 254, 255, 252, 253, 255, 255, 253], [253, 254, 252, 255, 254, 253, 255, 253, 253, 255, 250, 252, 255], [254, 255, 252, 2, 0, 3, 1, 0, 255, 255, 253, 254, 255], [254, 252, 5, 0, 2, 0, 3, 1, 3, 249, 253, 255, 254], [254, 255, 254, 249, 251, 251, 253, 253, 4, 1, 254, 255, 251], [255, 254, 251, 255, 251, 255, 255, 250, 0, 4, 253, 251, 254], [255, 250, 251, 252, 255, 246, 253, 254, 9, 2, 252, 255, 251], [248, 255, 253, 252, 255, 255, 255, 5, 0, 254, 253, 254, 255], [253, 255, 251, 255, 252, 0, 0, 0, 255, 251, 255, 251, 255], [255, 250, 252, 255, 1, 2, 255, 253, 250, 255, 252, 255, 250], [254, 253, 255, 0, 6, 255, 247, 255, 252, 255, 252, 251, 255], [254, 255, 2, 2, 0, 250, 255, 253, 251, 254, 253, 252, 255], [254, 254, 3, 2, 1, 1, 5, 1, 3, 1, 255, 253, 252], [252, 251, 1, 3, 0, 3, 0, 4, 7, 1, 252, 254, 255], [254, 255, 255, 255, 255, 255, 255, 253, 252, 255, 254, 255, 253], [252, 255, 255, 255, 253, 255, 251, 253, 255, 255, 251, 255, 254]],'3':[[255, 253, 253, 255, 255, 253, 251, 255, 254, 253, 255, 255, 250], [255, 253, 251, 255, 255, 254, 253, 249, 255, 254, 253, 255, 254], [253, 251, 255, 252, 252, 255, 254, 255, 255, 254, 253, 252, 253], [255, 253, 249, 253, 255, 0, 4, 0, 0, 0, 255, 255, 252], [254, 255, 255, 250, 3, 3, 0, 6, 4, 0, 0, 255, 254], [254, 255, 255, 253, 252, 255, 252, 252, 253, 3, 0, 254, 255], [255, 250, 255, 255, 252, 253, 255, 253, 253, 254, 0, 248, 255], [255, 255, 254, 254, 253, 253, 253, 252, 255, 0, 2, 253, 253], [254, 255, 253, 255, 255, 251, 0, 1, 0, 3, 253, 255, 253], [254, 250, 255, 255, 255, 255, 0, 5, 4, 0, 0, 252, 254], [255, 255, 254, 252, 255, 254, 254, 255, 255, 0, 3, 254, 253], [254, 250, 255, 254, 254, 254, 253, 250, 251, 255, 0, 255, 255], [255, 255, 253, 255, 255, 252, 253, 255, 255, 4, 3, 251, 251], [255, 255, 252, 254, 254, 4, 0, 1, 4, 0, 2, 255, 254], [254, 254, 255, 252, 254, 0, 0, 2, 5, 0, 255, 250, 254], [255, 255, 254, 254, 255, 254, 255, 254, 255, 253, 253, 252, 251], [255, 255, 255, 255, 255, 255, 254, 254, 252, 255, 253, 255, 254]],'b':[[254, 255, 255, 255, 254, 255, 253, 255, 255, 254, 255, 255, 253], [255, 252, 251, 253, 252, 255, 254, 252, 255, 255, 255, 252, 255], [253, 255, 255, 252, 255, 255, 252, 255, 255, 250, 255, 255, 255], [255, 253, 0, 1, 252, 252, 255, 252, 253, 255, 253, 254, 255], [255, 251, 4, 0, 255, 252, 255, 254, 255, 253, 255, 253, 251], [253, 255, 0, 5, 254, 254, 255, 253, 249, 250, 255, 255, 253], [253, 255, 4, 1, 3, 0, 1, 0, 9, 254, 250, 249, 255], [254, 253, 2, 1, 0, 3, 0, 5, 0, 1, 255, 249, 253], [253, 251, 4, 0, 4, 255, 255, 252, 1, 0, 3, 255, 252], [255, 255, 0, 4, 250, 249, 255, 255, 255, 0, 0, 254, 254], [252, 254, 0, 5, 254, 255, 252, 252, 255, 5, 0, 255, 255], [254, 253, 4, 0, 255, 251, 250, 255, 254, 1, 2, 255, 254], [254, 255, 0, 0, 2, 255, 254, 252, 3, 0, 1, 253, 255], [248, 253, 1, 4, 2, 0, 2, 4, 1, 0, 255, 253, 255], [255, 255, 4, 1, 253, 2, 4, 0, 13, 249, 254, 255, 252], [249, 255, 254, 251, 255, 253, 254, 253, 254, 255, 253, 255, 251], [255, 254, 255, 251, 255, 255, 253, 252, 252, 255, 255, 255, 255]],'c':[[254, 255, 255, 255, 255, 254, 254, 255, 255, 255, 254, 255, 253], [255, 255, 251, 254, 255, 255, 255, 255, 254, 255, 254, 254, 254], [255, 255, 255, 252, 255, 251, 254, 254, 255, 253, 255, 254, 255], [254, 251, 255, 255, 254, 255, 251, 254, 253, 255, 254, 254, 255], [255, 255, 252, 254, 255, 250, 255, 253, 255, 248, 255, 255, 255], [255, 255, 255, 252, 251, 255, 255, 251, 255, 254, 255, 255, 250], [249, 255, 255, 252, 7, 0, 0, 2, 0, 255, 251, 255, 255], [255, 252, 253, 7, 0, 3, 0, 0, 0, 255, 255, 254, 254], [254, 255, 1, 5, 2, 254, 254, 254, 255, 249, 255, 255, 254], [252, 255, 0, 6, 247, 255, 252, 255, 253, 254, 254, 254, 255], [255, 250, 0, 0, 255, 255, 252, 255, 254, 255, 251, 253, 255], [254, 252, 4, 1, 252, 255, 252, 250, 251, 254, 255, 255, 255], [250, 255, 0, 4, 0, 250, 254, 255, 255, 250, 255, 254, 249], [255, 255, 254, 0, 1, 0, 2, 0, 0, 252, 254, 255, 255], [254, 255, 252, 255, 3, 0, 0, 3, 2, 255, 252, 255, 255], [248, 255, 252, 253, 254, 255, 255, 255, 253, 255, 255, 255, 250], [255, 255, 254, 251, 255, 253, 252, 254, 255, 253, 255, 255, 254]],'m':[[254, 253, 255, 252, 255, 252, 255, 255, 255, 255, 253, 255, 255], [255, 255, 252, 255, 252, 255, 253, 254, 252, 255, 255, 252, 255], [255, 255, 255, 253, 255, 254, 254, 255, 253, 255, 254, 254, 255], [254, 253, 254, 255, 255, 254, 251, 253, 255, 255, 253, 255, 253], [254, 255, 255, 251, 254, 254, 253, 253, 253, 252, 254, 253, 255], [255, 250, 255, 255, 255, 255, 252, 255, 254, 254, 255, 255, 255], [255, 255, 0, 8, 253, 0, 7, 0, 5, 251, 250, 255, 254], [254, 255, 1, 0, 2, 9, 1, 1, 1, 4, 1, 255, 255], [255, 253, 6, 0, 1, 254, 255, 255, 3, 0, 1, 255, 252], [255, 251, 1, 0, 255, 255, 249, 254, 0, 3, 255, 250, 255], [254, 253, 2, 1, 252, 254, 252, 255, 3, 0, 255, 254, 252], [255, 255, 0, 1, 255, 252, 255, 253, 0, 7, 253, 249, 255], [254, 251, 4, 0, 250, 254, 255, 254, 2, 0, 255, 255, 252], [255, 255, 2, 3, 254, 255, 254, 255, 4, 0, 255, 253, 255], [254, 255, 0, 0, 255, 253, 253, 255, 1, 0, 255, 254, 248], [255, 254, 255, 255, 253, 255, 255, 255, 253, 255, 253, 255, 255], [255, 253, 251, 252, 254, 254, 254, 255, 254, 255, 255, 254, 254]],'n':[[254, 255, 253, 252, 255, 255, 252, 255, 254, 255, 253, 255, 255], [255, 253, 255, 255, 252, 252, 255, 255, 255, 255, 255, 255, 254], [255, 254, 255, 255, 254, 255, 250, 253, 251, 255, 255, 254, 255], [255, 255, 253, 255, 253, 255, 255, 255, 255, 254, 250, 255, 252], [254, 255, 255, 252, 255, 254, 254, 253, 251, 255, 254, 255, 255], [255, 254, 255, 253, 253, 255, 254, 255, 255, 254, 254, 252, 253], [254, 254, 7, 0, 255, 254, 0, 0, 0, 5, 254, 255, 251], [253, 255, 0, 1, 1, 0, 8, 0, 4, 0, 1, 252, 254], [254, 255, 0, 4, 2, 0, 251, 255, 245, 0, 1, 250, 254], [253, 254, 0, 2, 0, 255, 254, 252, 252, 1, 0, 255, 252], [252, 251, 5, 0, 253, 254, 255, 251, 255, 2, 1, 253, 255], [255, 250, 2, 6, 250, 255, 250, 255, 250, 0, 2, 255, 249], [247, 255, 0, 0, 254, 253, 255, 254, 255, 2, 0, 255, 255], [250, 255, 3, 1, 255, 255, 252, 255, 250, 6, 1, 254, 253], [255, 252, 3, 0, 255, 254, 251, 253, 254, 0, 0, 255, 255], [253, 255, 253, 255, 253, 255, 255, 255, 253, 255, 255, 251, 253], [255, 253, 251, 251, 254, 251, 255, 254, 254, 255, 252, 253, 255]],'v':[[255, 255, 254, 255, 253, 255, 252, 255, 255, 254, 255, 255, 253], [255, 255, 254, 255, 253, 251, 255, 255, 254, 255, 254, 252, 255], [255, 254, 255, 255, 255, 254, 255, 254, 253, 253, 255, 255, 254], [253, 255, 254, 252, 254, 255, 251, 255, 254, 255, 254, 254, 254], [255, 253, 255, 254, 255, 255, 254, 254, 255, 255, 254, 255, 255], [255, 255, 254, 248, 254, 250, 254, 255, 255, 250, 255, 252, 252], [252, 255, 252, 255, 254, 255, 252, 253, 255, 255, 1, 253, 253], [254, 255, 253, 253, 6, 0, 254, 250, 255, 0, 1, 255, 253], [253, 252, 255, 253, 1, 0, 255, 254, 251, 3, 0, 255, 252], [253, 252, 251, 255, 0, 3, 254, 251, 255, 3, 1, 252, 255], [255, 255, 254, 253, 255, 0, 0, 255, 0, 1, 255, 254, 251], [255, 251, 252, 251, 248, 1, 0, 4, 1, 2, 254, 254, 255], [255, 252, 254, 255, 255, 0, 3, 0, 3, 0, 255, 249, 253], [254, 254, 253, 255, 254, 254, 4, 0, 0, 255, 251, 255, 255], [255, 255, 255, 255, 255, 252, 2, 0, 1, 248, 255, 254, 248], [255, 253, 255, 254, 255, 255, 255, 252, 255, 255, 252, 252, 254], [255, 255, 254, 255, 255, 253, 255, 254, 254, 251, 255, 255, 252]],'x':[[255, 255, 255, 255, 253, 255, 253, 255, 255, 255, 255, 255, 254], [253, 255, 254, 255, 254, 255, 255, 255, 252, 255, 255, 251, 255], [255, 253, 255, 252, 255, 255, 250, 255, 255, 255, 255, 255, 252], [255, 254, 254, 253, 253, 255, 254, 255, 253, 253, 254, 254, 255], [254, 255, 253, 255, 254, 250, 252, 255, 255, 255, 253, 255, 255], [250, 255, 253, 252, 254, 255, 254, 254, 252, 255, 255, 251, 255], [255, 252, 0, 4, 254, 251, 255, 4, 2, 255, 250, 255, 253], [251, 252, 5, 0, 255, 254, 254, 0, 3, 255, 255, 251, 254], [255, 249, 255, 9, 0, 251, 14, 0, 255, 255, 254, 255, 255], [251, 255, 252, 253, 0, 4, 0, 255, 254, 253, 251, 251, 255], [252, 255, 254, 254, 5, 3, 5, 255, 248, 255, 255, 255, 255], [255, 252, 254, 252, 1, 4, 0, 255, 253, 255, 249, 251, 255], [254, 255, 252, 6, 1, 250, 1, 1, 255, 251, 255, 255, 253], [252, 255, 1, 0, 255, 255, 255, 3, 5, 251, 255, 252, 255], [255, 252, 4, 2, 254, 251, 253, 2, 0, 254, 255, 253, 253], [255, 251, 254, 255, 254, 255, 252, 255, 255, 255, 252, 254, 254], [255, 253, 252, 252, 253, 255, 253, 251, 255, 253, 254, 255, 251]],'z':[[255, 255, 255, 254, 255, 254, 255, 255, 255, 255, 255, 254, 255], [254, 254, 255, 255, 255, 254, 255, 253, 255, 255, 255, 254, 254], [255, 255, 252, 253, 252, 255, 255, 255, 255, 252, 255, 255, 255], [255, 255, 252, 255, 254, 248, 255, 250, 254, 255, 249, 255, 254], [255, 253, 255, 254, 255, 255, 255, 253, 253, 254, 254, 254, 254], [253, 253, 255, 252, 250, 250, 251, 253, 255, 254, 251, 255, 255], [255, 254, 0, 3, 6, 4, 9, 0, 0, 255, 252, 251, 254], [253, 253, 2, 0, 0, 3, 0, 1, 1, 250, 255, 253, 254], [253, 255, 254, 252, 255, 4, 0, 1, 255, 254, 251, 255, 255], [255, 255, 254, 253, 255, 2, 0, 254, 254, 252, 255, 253, 255], [253, 247, 255, 252, 4, 6, 252, 255, 255, 254, 255, 253, 252], [255, 255, 252, 9, 0, 254, 250, 250, 252, 254, 255, 255, 253], [255, 254, 0, 0, 5, 254, 255, 255, 255, 254, 253, 254, 251], [255, 252, 3, 4, 3, 0, 0, 1, 0, 254, 254, 254, 255], [248, 255, 3, 0, 2, 1, 1, 0, 1, 255, 252, 254, 255], [255, 250, 255, 254, 254, 255, 255, 255, 254, 253, 254, 255, 249], [252, 253, 255, 253, 254, 255, 252, 253, 255, 255, 255, 255, 255]],}
Convert.py转换为灰度图并降噪
import cv2
import numpy as np
class Convert(object):
"""docstring for Convert"""
def __init__(self):
super(Convert, self).__init__()
def _get_dynamic_binary_image(self,img):
'''
自适应阀值二值化
'''
img = cv2.imdecode(np.frombuffer(img, np.uint8), cv2.IMREAD_COLOR)
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
th1 = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 1)
return th1
def clear_border(self,img):
'''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(self,img):
'''
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 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(self,img, 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 run(self,img):
# 自适应阈值二值化
img = self._get_dynamic_binary_image(img)
# 去除边框
img = self.clear_border(img)
# 对图片进行干扰线降噪
img = self.interference_line(img)
# 对图片进行点降噪
img = self.interference_point(img)
return img
ImgMain.py识别代码
#!/usr/bin/python
# -*- coding: utf-8 -*-
from fnmatch import fnmatch
from queue import Queue
import matplotlib.pyplot as plt
import cv2
import time
import os
from Convert import Convert
from CharMap import charMap
import requests
import numpy as np
def cutting_img(im,im_position,xoffset = 1,yoffset = 1):
# 识别出的字符个数
im_number = len(im_position[1])
if(im_number>=4): im_number = 4;
imgArr = []
# 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]
imgArr.append(cropped)
# cv2.imwrite(str(i)+"v.jpg",cropped) # 查看切割效果
return im_number,imgArr
def main():
cvt = Convert()
req = requests.get("http://xxxxxxxxxxxxxxxx/verifycode.servlet")
# 注意有些教务加装了所谓云防护，没有请求头会拦截，导致获取不了验证码图片，报错可以打印req.content看看
img = cvt.run(req.content)
cv2.imwrite("v.jpg",img) # 查看验证码
#切割的位置
im_position = ([8, 7, 6, 9], [[4, 12], [14, 21], [24, 30], [34, 43]], [[7, 16], [7, 16], [7, 16], [7, 16]])
cutting_img_num,imgArr = cutting_img(img,im_position,1,1)
# 识别验证码
result=""
for i in range(cutting_img_num):
try:
template = imgArr[i]
tempResult=""
matchingDegree=0.0
for char in charMap:
img = np.asarray(charMap[char],dtype = np.uint8)
res = cv2.matchTemplate(img,template,3) #img原图 template模板 用模板匹配原图
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
if(max_val>matchingDegree):
tempResult=char
matchingDegree=max_val
result += tempResult
matchingDegree=0.0
except Exception as err:
raise Exception
# print("ERROR "+ str(err))
pass
print(result)
if __name__ == '__main__':
main()
提供全部代码
https://github.com/WindrunnerMax/SWVerifyCode
强智教务系统验证码识别 OpenCV的更多相关文章
- 强智教务系统验证码识别 Tensorflow CNN
强智教务系统验证码识别 Tensorflow CNN 一直都是使用API取得数据,但是API提供的数据较少,且为了防止API关闭,先把验证码问题解决 使用Tensorflow训练模型,强智教务系统的验 ...
- Java模拟登录带验证码的教务系统（原理详解）
一:原理 客户端访问服务器,服务器通过Session对象记录会话,服务器可以指定一个唯一的session ID作为cookie来代表每个客户端,用来识别这个客户端接下来的请求. 我们通过Chrome浏 ...
- uu云验证码识别平台,验证码,验证码识别,全自动验证码识别技术,优优云全自动打码,代答题系统,优优云远程打码平台,uu云打码
uu云验证码识别平台,验证码,验证码识别,全自动验证码识别技术,优优云全自动打码,代答题系统,优优云远程打码平台,uu云打码 优优云验证码识别答题平台介绍 优优云|UU云(中国公司)是全球唯一领先的智 ...
- 使用tensorflow搭建自己的验证码识别系统
目录 准备验证码数据 保存为tfrecords文件 验证码训练 学习tensorflow有一段时间了,想做点东西来练一下手.为了更有意思点,下面将搭建一个简单的验证码识别系统. 准备验证码数据 下面将 ...
- windows下简单验证码识别——完美验证码识别系统
此文已由作者徐迪授权网易云社区发布. 欢迎访问网易云社区,了解更多网易技术产品运营经验. 讲到验证码识别,大家第一个可能想到tesseract.诚然,对于OCR而言,tesseract确实很强大,自带 ...
- 字符型图片验证码识别完整过程及Python实现
字符型图片验证码识别完整过程及Python实现 1 摘要 验证码是目前互联网上非常常见也是非常重要的一个事物,充当着很多系统的 防火墙 功能,但是随时OCR技术的发展,验证码暴露出来的安全问题也越 ...
- 完整的验证码识别流程基于svm（若是想提升，可优化）
字符型图片验证码识别完整过程及Python实现 首先很感觉这篇文章的作者,将这篇文章写的这么好.我呢,也是拿来学习,觉得太好,所以忍不住就进行了转载. 因为我个人现在手上也有个验证码识别的项目,只是难 ...
- 字符识别Python实现 图片验证码识别
字符型图片验证码识别完整过程及Python实现 1 摘要 验证码是目前互联网上非常常见也是非常重要的一个事物,充当着很多系统的 防火墙 功能,但是随时OCR技术的发展,验证码暴露出来的安全问题也越 ...
- 基于tensorflow的‘端到端’的字符型验证码识别源码整理(github源码分享)
基于tensorflow的‘端到端’的字符型验证码识别 1 Abstract 验证码(CAPTCHA)的诞生本身是为了自动区分 自然人 和 机器人 的一套公开方法, 但是近几年的人工智能技术的发展 ...
随机推荐
- 前端页面设计常见的30个CSS选择器
1. * --> 通配符选择器 * { margin: 0; padding: 0; } 星号符会选择页面每个元素.很多开发者用它把所有margin和padding归零.这当然是快捷测试 ...
- Angular总结
angular关键核心点进行总结 1 2 angular中有很多知识点需要学习,学习成本是很大的,我通过平常开发中把一些 很重要知识点总结下来,不管是以后拿来用,或者跳槽面试需要,我都感觉是很有帮助的 ...
- CSS——NO.4(继承、层叠、特殊性、重要性)
*/ * Copyright (c) 2016,烟台大学计算机与控制工程学院 * All rights reserved. * 文件名:text.cpp * 作者:常轩 * 微信公众号:Worldhe ...
- AI：拿来主义——预训练网络（一）
我们已经训练过几个神经网络了,识别手写数字,房价预测或者是区分猫和狗,那随之而来就有一个问题,这些训练出的网络怎么用,每个问题我都需要重新去训练网络吗?因为程序员都不太喜欢做重复的事情,因此答案肯定是 ...
- Ribbon进行服务调用/负载均衡以及请求重试配置
Ribbon负载均衡 经过对Eureka的认识,及Eureka集群的搭建,已经基本可以入门Eureka的使用.之前对于服务调用者我们是直接获取注册列表后通过 get(0) 的方式来获取第一个注册信息. ...
- python画一颗拳头大的💗
用上turtle库后,各种画,今天画个拳头大的爱心@.@. 下面贴下代码: # -*- coding: utf-8 -*- # Nola import pygame import time impor ...
- mysql 存储过程 执行存储过程修改了表中所有行的信息
存储过程中的where条件语句,如果传入的参数和表字段名相同,存储过程就会把这个约束条件忽略.小结:存储过程中传递的参数名不要和字段名相同.特别是修改.删除等操作,可能会对整张表产生影响.后果会很严重 ...
- 全差分运算放大器ADA4930的分析(1)
AD转换芯片的模拟信号输入端方式为:全差分.伪差分.单端输入,其中全差分输入的效果最佳,现阶段ADC转换器为了提高其性能,建议用户使用全差分的输入方式.(AD7982.ADS8317等都能实现信号的全 ...
- 【Amaple教程】2. 模块
正如它的名字,模块用于amaplejs单页应用的页面分割,所有的跳转更新和代码编写都是以模块为单位的. 定义一个模块 一个模块由<module>标签对包含,内部分为template模板.J ...
- 基础JavaScript练习（三）总结
任务目的 实践JavaScript数组.字符串相关操作 任务描述 基于任务四进行升级 将新元素输入框从input改为textarea 允许一次批量输入多个内容,格式可以为数字.中文.英文等,可以通过用 ...