基于python实现破解滑动验证码过程解析

 import time
 from selenium import webdriver
 from slideVerfication import SlideVerificationCode
 ​
 # 1、创建一个driver对象，访问qq登录页面
 browser = webdriver.Chrome()
 browser.get("https://qzone.qq.com/")
 ​
 # 2、输入账号密码
 # 2.0 点击切换到登录的iframe
 browser.switch_to.frame('login_frame')
 # 2.1 点击账号密码登录
 browser.find_element_by_id('switcher_plogin').click()
 # 2.2定位账号输入框，输入账号
 browser.find_element_by_id("u").send_keys("123292678")
 # 2.3定位密码输入输入密码
 browser.find_element_by_id("p").send_keys("PYTHON01")
 # 3、点击登录
 browser.find_element_by_id('login_button').click()
 time.sleep(3)
 ​
 # 4、模拟滑动验证
 # 4.1切换到滑动验证码的iframe中
 tcaptcha = browser.find_element_by_id("tcaptcha_iframe")
 browser.switch_to.frame(tcaptcha)
 # 4.2 获取滑动相关的元素
 # 选择拖动滑块的节点
 slide_element = browser.find_element_by_id('tcaptcha_drag_thumb')
 # 获取滑块图片的节点
 slideBlock_ele = browser.find_element_by_id('slideBlock')
 # 获取缺口背景图片节点
 slideBg = browser.find_element_by_id('slideBg')
 # 4.3计算滑动距离
 sc = SlideVerificationCode(save_image=True)
 distance = sc.get_element_slide_distance(slideBlock_ele,slideBg)
 # 滑动距离误差校正，滑动距离*图片在网页上显示的缩放比-滑块相对的初始位置
 distance = distance*(280/680) - 22
 print("校正后的滑动距离",distance)
 # 4.4、进行滑动
 sc.slide_verification(browser,slide_element,distance=100)

def get_element_slide_distance(self, slider_ele, background_ele, correct=0):
     """
     根据传入滑块，和背景的节点，计算滑块的距离
 ​
     该方法只能计算 滑块和背景图都是一张完整图片的场景，
     如果背景图是通过多张小图拼接起来的背景图，
     该方法不适用，请使用get_image_slide_distance这个方法
     :param slider_ele: 滑块图片的节点
     :type slider_ele: WebElement
     :param background_ele: 背景图的节点
     :type background_ele:WebElement
     :param correct:滑块缺口截图的修正值，默认为0,调试截图是否正确的情况下才会用
     :type: int
     :return: 背景图缺口位置的X轴坐标位置（缺口图片左边界位置）
     """
     # 获取验证码的图片
     slider_url = slider_ele.get_attribute("src")
     background_url = background_ele.get_attribute("src")
     # 下载验证码背景图,滑动图片
     slider = "slider.jpg"
     background = "background.jpg"
     self.onload_save_img(slider_url, slider)
     self.onload_save_img(background_url, background)
     # 读取进行色度图片，转换为numpy中的数组类型数据，
     slider_pic = cv2.imread(slider, 0)
     background_pic = cv2.imread(background, 0)
     # 获取缺口图数组的形状 -->缺口图的宽和高
     width, height = slider_pic.shape[::-1]
     # 将处理之后的图片另存
     slider01 = "slider01.jpg"
     background_01 = "background01.jpg"
     cv2.imwrite(background_01, background_pic)
     cv2.imwrite(slider01, slider_pic)
     # 读取另存的滑块图
     slider_pic = cv2.imread(slider01)
     # 进行色彩转换
     slider_pic = cv2.cvtColor(slider_pic, cv2.COLOR_BGR2GRAY)
     # 获取色差的绝对值
     slider_pic = abs(255 - slider_pic)
     # 保存图片
     cv2.imwrite(slider01, slider_pic)
     # 读取滑块
     slider_pic = cv2.imread(slider01)
     # 读取背景图
     background_pic = cv2.imread(background_01)
     # 比较两张图的重叠区域
     result = cv2.matchTemplate(slider_pic, background_pic, cv2.TM_CCOEFF_NORMED)
     # 获取图片的缺口位置
     top, left = np.unravel_index(result.argmax(), result.shape)
     # 背景图中的图片缺口坐标位置
     print("当前滑块的缺口位置：", (left, top, left + width, top + height))
     return left
   def slide_verification(self, driver, slide_element, distance):
     """
     滑动滑块进行验证
     
     :param driver: driver对象
     :type driver:webdriver.Chrome
     :param slide_element: 滑块的元组
     :type slider_ele: WebElement
     :param distance: 滑动的距离
     :type: int
     :return:
     """
     # 获取滑动前页面的url地址
     start_url = driver.current_url
     print("需要滑动的距离为：", distance)
     # 根据滑动距离生成滑动轨迹
     locus = self.get_slide_locus(distance)
     print("生成的滑动轨迹为:{}，轨迹的距离之和为{}".format(locus, distance))
     # 按下鼠标左键
     ActionChains(driver).click_and_hold(slide_element).perform()
     time.sleep(0.5)
     # 遍历轨迹进行滑动
     for loc in locus:
       time.sleep(0.01)
       ActionChains(driver).move_by_offset(loc, random.randint(-5, 5)).perform()
       ActionChains(driver).context_click(slide_element)
     # 释放鼠标
     ActionChains(driver).release(on_element=slide_element).perform()