【2019.05】腾讯防水墙滑动验证码破解 python + selenium + OpenCV

刺骨的言语ヽ痛彻心扉 2022-02-14 04:02 824阅读 0赞

# captcha\_qq #

# 腾讯防水墙滑动验证码破解 #

* 使用OpenCV库
    * 成功率大概90%左右：在实际应用中，登录后可判断当前页面是否有登录成功才会出现的信息：比如用户名等。循环
    * 验证码地址：https://open.captcha.qq.com/online.html
    * 破解 腾讯滑动验证码
    * 腾讯防水墙
    * python + seleniuum + cv2

## 结果展示 ##

![在这里插入图片描述][20190425184353826.gif]

### 这里有一点很有意思 ###

*  如果滑动过快或者滑动的又精确又快的话，有时会解锁不成功，所以这里使用了 模拟人滑动的方法：
 *  加速度，多滑，划回来等 各种操作，各位也可以多试试。

def get_track(distance):
            """ 模拟轨迹 假装是人在操作 :param distance: :return: """
            # 初速度
            v = 0
            # 单位时间为0.2s来统计轨迹，轨迹即0.2内的位移
            t = 0.2
            # 位移/轨迹列表，列表内的一个元素代表0.2s的位移
            tracks = []
            # 当前的位移
            current = 0
            # 到达mid值开始减速
            mid = distance * 7 / 8
    
            distance += 10  # 先滑过一点，最后再反着滑动回来
            # a = random.randint(1,3)
            while current < distance:
                if current < mid:
                    # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细
                    a = random.randint(2, 4)  # 加速运动
                else:
                    a = -random.randint(3, 5)  # 减速运动
    
                # 初速度
                v0 = v
                # 0.2秒时间内的位移
                s = v0 * t + 0.5 * a * (t ** 2)
                # 当前的位置
                current += s
                # 添加到轨迹列表
                tracks.append(round(s))
    
                # 速度已经达到v,该速度作为下次的初速度
                v = v0 + a * t
    
            # 反着滑动到大概准确位置
            for i in range(4):
                tracks.append(-random.randint(2, 3))
            for i in range(4):
                tracks.append(-random.randint(1, 3))
            return tracks

## 代码在这里 ##

*  star 一下
 *  项目地址 [https://github.com/ybsdegit/captcha\_qq][https_github.com_ybsdegit_captcha_qq]
 *  入口代码地址 [https://github.com/ybsdegit/captcha\_qq/blob/master/captcha\_qq.py][https_github.com_ybsdegit_captcha_qq_blob_master_captcha_qq.py]

### 这里是代码 ###

#!/usr/bin/env python
    # encoding: utf-8
    
    # -*- coding: utf-8 -*-
    # @contact: ybsdeyx@foxmail.com
    # @software: PyCharm
    # @time: 2019/4/25 16:39
    # @author: Paulson●Wier
    # @file: captcha_qq.py
    # @desc:
    import numpy as np
    import random
    
    import requests
    from selenium.webdriver import ActionChains
    import time
    from selenium import webdriver
    from PIL import Image
    import os
    from selenium.webdriver.support.ui import WebDriverWait
    import cv2
    
    
    class Login(object):
        """ 腾讯防水墙滑动验证码破解 使用OpenCV库 成功率大概90%左右：在实际应用中，登录后可判断当前页面是否有登录成功才会出现的信息：比如用户名等。循环 https://open.captcha.qq.com/online.html 破解 腾讯滑动验证码 腾讯防水墙 python + seleniuum + cv2 """
        def __init__(self):
            # 如果是实际应用中，可在此处账号和密码
            self.url = "https://open.captcha.qq.com/online.html"
            self.driver = webdriver.Chrome()
    
        @staticmethod
        def show(name):
            cv2.imshow('Show', name)
            cv2.waitKey(0)
            cv2.destroyAllWindows()
    
        @staticmethod
        def webdriverwait_send_keys(dri, element, value):
            """ 显示等待输入 :param dri: driver :param element: :param value: :return: """
            WebDriverWait(dri, 10, 5).until(lambda dr: element).send_keys(value)
    
        @staticmethod
        def webdriverwait_click(dri, element):
            """ 显示等待 click :param dri: driver :param element: :return: """
            WebDriverWait(dri, 10, 5).until(lambda dr: element).click()
    
        @staticmethod
        def get_postion(chunk, canves):
            """ 判断缺口位置 :param chunk: 缺口图片是原图 :param canves: :return: 位置 x, y """
            otemp = chunk
            oblk = canves
            target = cv2.imread(otemp, 0)
            template = cv2.imread(oblk, 0)
            # w, h = target.shape[::-1]
            temp = 'temp.jpg'
            targ = 'targ.jpg'
            cv2.imwrite(temp, template)
            cv2.imwrite(targ, target)
            target = cv2.imread(targ)
            target = cv2.cvtColor(target, cv2.COLOR_BGR2GRAY)
            target = abs(255 - target)
            cv2.imwrite(targ, target)
            target = cv2.imread(targ)
            template = cv2.imread(temp)
            result = cv2.matchTemplate(target, template, cv2.TM_CCOEFF_NORMED)
            x, y = np.unravel_index(result.argmax(), result.shape)
            return x, y
            # # 展示圈出来的区域
            # cv2.rectangle(template, (y, x), (y + w, x + h), (7, 249, 151), 2)
            # cv2.imwrite("yuantu.jpg", template)
            # show(template)
    
        @staticmethod
        def get_track(distance):
            """ 模拟轨迹 假装是人在操作 :param distance: :return: """
            # 初速度
            v = 0
            # 单位时间为0.2s来统计轨迹，轨迹即0.2内的位移
            t = 0.2
            # 位移/轨迹列表，列表内的一个元素代表0.2s的位移
            tracks = []
            # 当前的位移
            current = 0
            # 到达mid值开始减速
            mid = distance * 7 / 8
    
            distance += 10  # 先滑过一点，最后再反着滑动回来
            # a = random.randint(1,3)
            while current < distance:
                if current < mid:
                    # 加速度越小，单位时间的位移越小,模拟的轨迹就越多越详细
                    a = random.randint(2, 4)  # 加速运动
                else:
                    a = -random.randint(3, 5)  # 减速运动
    
                # 初速度
                v0 = v
                # 0.2秒时间内的位移
                s = v0 * t + 0.5 * a * (t ** 2)
                # 当前的位置
                current += s
                # 添加到轨迹列表
                tracks.append(round(s))
    
                # 速度已经达到v,该速度作为下次的初速度
                v = v0 + a * t
    
            # 反着滑动到大概准确位置
            for i in range(4):
                tracks.append(-random.randint(2, 3))
            for i in range(4):
                tracks.append(-random.randint(1, 3))
            return tracks
    
        @staticmethod
        def urllib_download(imgurl, imgsavepath):
            """ 下载图片 :param imgurl: 图片url :param imgsavepath: 存放地址 :return: """
            from urllib.request import urlretrieve
            urlretrieve(imgurl, imgsavepath)
    
        def after_quit(self):
            """ 关闭浏览器 :return: """
            self.driver.quit()
    
        def login_main(self):
            # ssl._create_default_https_context = ssl._create_unverified_context
            driver = self.driver
            driver.maximize_window()
            driver.get(self.url)
    
            click_keyi_username = driver.find_element_by_xpath("//div[@class='wp-onb-tit']/a[text()='可疑用户']")
            self.webdriverwait_click(driver, click_keyi_username)
    
            login_button = driver.find_element_by_id('code')
            self.webdriverwait_click(driver, login_button)
            time.sleep(1)
    
            driver.switch_to.frame(driver.find_element_by_id('tcaptcha_iframe'))  # switch 到 滑块frame
            time.sleep(0.5)
            bk_block = driver.find_element_by_xpath('//img[@id="slideBg"]')  # 大图
            web_image_width = bk_block.size
            web_image_width = web_image_width['width']
            bk_block_x = bk_block.location['x']
    
            slide_block = driver.find_element_by_xpath('//img[@id="slideBlock"]')  # 小滑块
            slide_block_x = slide_block.location['x']
    
            bk_block = driver.find_element_by_xpath('//img[@id="slideBg"]').get_attribute('src')       # 大图 url
            slide_block = driver.find_element_by_xpath('//img[@id="slideBlock"]').get_attribute('src')  # 小滑块 图片url
            slid_ing = driver.find_element_by_xpath('//div[@id="tcaptcha_drag_thumb"]')  # 滑块
    
            os.makedirs('./image/', exist_ok=True)
            self.urllib_download(bk_block, './image/bkBlock.png')
            self.urllib_download(slide_block, './image/slideBlock.png')
            time.sleep(0.5)
            img_bkblock = Image.open('./image/bkBlock.png')
            real_width = img_bkblock.size[0]
            width_scale = float(real_width) / float(web_image_width)
            position = self.get_postion('./image/bkBlock.png', './image/slideBlock.png')
            real_position = position[1] / width_scale
            real_position = real_position - (slide_block_x - bk_block_x)
            track_list = self.get_track(real_position + 4)
    
            ActionChains(driver).click_and_hold(on_element=slid_ing).perform()  # 点击鼠标左键，按住不放
            time.sleep(0.2)
            # print('第二步,拖动元素')
            for track in track_list:
                ActionChains(driver).move_by_offset(xoffset=track, yoffset=0).perform()  # 鼠标移动到距离当前位置（x,y）
                time.sleep(0.002)
            # ActionChains(driver).move_by_offset(xoffset=-random.randint(0, 1), yoffset=0).perform() # 微调，根据实际情况微调
            time.sleep(1)
            # print('第三步,释放鼠标')
            ActionChains(driver).release(on_element=slid_ing).perform()
            time.sleep(1)
    
            print('登录成功')
            self.after_quit()
    
    
    if __name__ == '__main__':
        phone = "****"
        login = Login()
        login.login_main()

[20190425184353826.gif]: /images/20220214/a21e5c1b4805401db875e12ce3eb25bf.png
[https_github.com_ybsdegit_captcha_qq]: https://github.com/ybsdegit/captcha_qq
[https_github.com_ybsdegit_captcha_qq_blob_master_captcha_qq.py]: https://github.com/ybsdegit/captcha_qq/blob/master/captcha_qq.py