python滑块验证码的破解实现

def get_image1(self,driver):
  """
  获取滑块验证缺口小图片
  :param driver:chrome对象
  :return:缺口小图片
  """
  canvas = driver.find_element_by_xpath("//div[@id='xy_img']").get_attribute("style")
  image_data=re.findall("data:image/jpg;base64,(.*?)\"\)",canvas)[0]
  # print(image_data)
  binary_image_data=base64.b64decode(image_data,'-_')
  file_like=BytesIO(binary_image_data)
  image=Image.open(file_like)
  return image

def get_image2(self,driver):
  """
  获取滑块验证码背景图片
  :param driver:chrome对象
  :return:背景图片
   """
  driver.save_screenshot('yanzhengma.png')
  # 通过图片元素节点获取坐标值
  # element = driver.find_element_by_id("bgImg")
  # left = element.location['x']
  # top = element.location['y']
  # right = element.location['x'] + element.size['width']
  # bottom = element.location['y'] + element.size['height']
  # 通过画图软件直接获取相应图片的坐标值
  left=359
  top=238
  right=658
  bottom=437
  # print((left, top, right, bottom))
  im = Image.open('yanzhengma.png')
  im = im.crop((left, top, right, bottom))
  return im

def get_track(self, distance):
  """
  根据偏移量获取移动轨迹
  :param distance:偏移量
  :return:移动轨迹
  """
  # 移动轨迹
  track = []
  # 当前位移
  current = 0
  # 减速阈值
  mid = distance * 4 / 5
  # 计算间隔
  t = 0.2
  # 初速度
  v = 0

  while current < distance:
    if current < mid:
      # 加速度为正2
      a = 2
    else:
      # 加速度为负3
      a = -3
    # 初速度v0
    v0 = v
    # 当前速度v = v0 + at
    v = v0 + a * t
    # 移动距离x = v0t + 1/2 * a * t^2
    move = v0 * t + 1 / 2 * a * t * t
    # 当前位移
    current += move
    # 加入轨迹
    track.append(round(move))
  return track

 def slider_verification_code(self,driver,cnt):
  """
  破解滑块验证主程序
  :param driver:chrome对象；cnt：已验证次数
  :return:已验证次数
  """
  print("出现滑块验证，验证中")
  # 1、出现滑块验证，获取验证小图片
  picture1 = self.get_image1(driver)
  picture1.save("./picture1.png")
  # 2、获取有缺口验证图片
  picture2 = self.get_image2(driver)
  picture2.save("./picture2.png")
  #二值化图片,进行对比，输出匹配的坐标系
  target_rgb=cv2.imread("./picture2.png")
  target_gray=cv2.cvtColor(target_rgb,cv2.COLOR_BGR2GRAY)
  template_rgb=cv2.imread("./picture1.png",0)
  res=cv2.matchTemplate(target_gray,template_rgb,cv2.TM_CCOEFF_NORMED)
  value=cv2.minMaxLoc(res)
  value = value[3][0]
  cnt += 1
  print("需要位移的距离为："+str(value)+",已验证"+str(cnt)+"次")
  #根据距离获取位移的轨迹路线
  track=self.get_track(value)
  time.sleep(1)
  ActionChains(driver).click_and_hold(driver.find_element_by_class_name("handler.handler_bg")).perform()
  for x in track:
    ActionChains(driver).move_by_offset(xoffset=x, yoffset=0).perform()
  time.sleep(0.5)
  ActionChains(driver).release().perform()
  return cnt