python之w3c滑块验证.zip

# -*- coding: utf-8 -*- # __author__ = "zok" [email protected] # Date: 2019-10-10 Python: 3.7 import requests import re import json from PIL import Image # 图像处理模块 from io import BytesIO # io 加载 class W3C: """ 滑块验证码识别与破解 w3c 网站滑块网址 https://www.w3cschool.cn/register """ def __init__(self): self.headers = { 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/77.0.3865.90 Safari/537.36', 'Host': 'www.w3cschool.cn' } self.local = 'https://www.w3cschool.cn' self.check = 'https://www.w3cschool.cn/dragcheck' self.session = requests.session() self.height = 58 # 碎片高 self.width = 13 # 碎片宽 def get_img(self): """获取页面滑块图片""" response = self.session.get('https://www.w3cschool.cn/register', headers=self.headers) results = re.findall(r'background-image: url\("(.*?)"\);', response.text) bg_img = self.local + results[0] # 背景图 hk_img = self.local + results[1] # 滑块图 # print(bg_img, hk_img) # 取 css css_list = re.findall(r'background-position:(.*?);">', response.text) # 缺口滑块 ret = self.session.get(hk_img) with open('hk.png', 'wb') as f: f.write(ret.content) # 图片对象 img_cha = self.split_img(bg_img, css_list) xy = None # 识别结果 for n in range(4): full_image = Image.open('img/'+str(n)+'.png') # 获取缺口位置 xy = self.get_distance(img_cha, full_image) if xy and xy > 0: break return xy def get_distance(self, bg_image, full_image): """ 计算滑块移动距离 :param bg_image: (Image) 有缺口的整图 :param full_image: (Image) 完整原图 :return: (Int)缺口离滑块的距离 """ # 滑块的初始位置 distance = 0 # 遍历像素点横坐标 for i in range(distance, full_image.size[0]): # 遍历像素点纵坐标 for j in range(full_image.size[1]): # 如果不是相同像素 if not self.is_pixel_equal(bg_image, full_image, i, j): # 返回此时横轴坐标就是滑块需要移动的距离 return i @staticmethod def is_pixel_equal(bg_image, full_image, x, y): """ 判断像素是否相同算法 :param bg_image: (Image)缺口图片 :param full_image: (Image)完整图片 :param x: (Int)位置x :param y: (Int)位置y :return: (Boolean)像素是否相同 """ # 获取缺口图片的像素点(按照RGB格式) bg_pixel = bg_image.load()[x, y] # 获取完整图片的像素点(按照RGB格式) full_pixel = full_image.load()[x, y] # 设置一个判定值，像素值之差超过判定值则认为该像素不相同 threshold = 1 # 可根据识别效果调整 # 判断像素的各个颜色之差，abs()用于取绝对值 if (abs(bg_pixel[0] - full_pixel[0] < threshold) and abs(bg_pixel[1] - full_pixel[1] < threshold) and abs( bg_pixel[2] - full_pixel[2] < threshold)): # 如果差值在判断值之内，返回是相同像素 return True else: # 如果差值在判断值之外，返回不是相同像素 return False def split_img(self, bg_img, css_list): """图片还原""" # 背景 ret = self.session.get(bg_img) bytes_io = BytesIO() bytes_io.write(ret.content) img = Image.open(bytes_io) img_cha = Image.open('chache.png') img.save('bg.png') # 记录一下原始图片 for index, i in enumerate(css_list): x, y = [abs(int(xy[:-2])) for xy in i.split(' ')] # 将 css 坐标抽离出来 box = [x, y, x + self.width, y + self.height] im_crop = img.crop(box) if index < 20: # 第一行 img_cha.paste(im_crop, (index*self.width, 0)) else: # 第二行 img_cha.paste(im_crop, ((index-20)*self.width, self.height)) img_cha.save('chache.png') # 储存 return img_cha def check_code(self): """滑块认证""" point = self.get_img() if point: print('\033[1;34m识别成功: 缺口位置 x: %s \033[0m' % point) print('\033[1;36m提交数据中...\033[0m') result = self.session.post(self.check, data={'point': point}, headers=self.headers) print(json.loads(result.text).get('message')) else: print('滑块识别失败。。运气不佳。。重试即可。') if __name__ == '__main__': wc = W3C() wc.check_code()