登录163邮箱的滑块验证

登录163网易邮箱取数功能，查阅了很多博客的代码都不太稳定或者已失效，下面是经过修改的一个方案，成功率大概在90%以上
bash 复制代码
def get_verify(self, name='处理滑块移动'):
    max_distance = self.handle_picture()
    reality_distance = int((max_distance - 64) / 1.45)
    sleep(0.5)
    track_list = self.handle_tracks(reality_distance)
    # 获取滑块元素
    slider_handle = self.driver.find_element(By.XPATH, '//*[@class="yidun_slider"]')
    ActionChains(self.driver).click_and_hold(slider_handle).perform()
    for i in track_list:
        ActionChains(self.driver).move_by_offset(i, 0).perform()
    sleep(1)
    ActionChains(self.driver).release().perform()
  
def handle_picture(self):
    bg_img = re.findall(r'alt="验证码背景".*?src="(.*?)"', self.driver.page_source)[0]
    hk_img = re.findall(r'alt="验证码滑块".*?src="(.*?)"', self.driver.page_source)[0]
    with open('./bg_img.jpg', 'wb') as f:
        f.write(requests.get(bg_img).content)
        f.close()
    with open('./hk_img.png', 'wb') as f:
        f.write(requests.get(hk_img).content)
        f.close()
    # 读取背景图片和缺口图片
    bg_img = cv2.imread('./bg_img.jpg')  # 背景图片
    tp_img = cv2.imread('./hk_img.png')  # 缺口图片
    # 识别图片边缘
    bg_edge = cv2.Canny(bg_img, 100, 200)
    tp_edge = cv2.Canny(tp_img, 100, 200)
    # 转换图片格式
    bg_pic = cv2.cvtColor(bg_edge, cv2.COLOR_GRAY2RGB)
    tp_pic = cv2.cvtColor(tp_edge, cv2.COLOR_GRAY2RGB)
    # 缺口匹配
    res = cv2.matchTemplate(bg_pic, tp_pic, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)  # 寻找最优匹配
    x = max_loc[0]  # 滑块在验证图片的x坐标（左边）
    # 绘制方框
    th, tw = tp_pic.shape[:2]
    tl = max_loc  # 左上角点的坐标
    br = (tl[0] + tw, tl[1] + th)  # 右下角点的坐标
    cv2.rectangle(bg_img, tl, br, (0, 0, 255), 2)  # 绘制矩形
    # cv2.imwrite(r'C:\test\out.jpg', bg_img)  # 保存在本地
    return int(br[0])
 
@staticmethod
def handle_tracks(distance):
    t = 0.1
    speed = 0
    current = 0
    mid = 3 / 5 * distance
    track_list = []
    while current < distance:
        if current < mid:
            # a = random.choice([1, 2, 3])
            a = 3
        else:
            # a = random.choice([-1, -2, -3])
            a = -4
        move_track = speed * t + 0.5 * a * t ** 2
        track_list.append(round(move_track))
        speed = speed + a * t
        current += move_track
    # 模拟人类来回移动了一小段
    end_track = [1, 0] * 10 + [0] * 10 + [-1, 0] * 10
    track_list.extend(end_track)
    offset = sum(track_list) - distance
    # 由于四舍五入带来的误差,这里需要补回来
    if offset > 0:
        track_list.extend(offset * [-1, 0])
    elif offset < 0:
        track_list.extend(offset * [1, 0])
    return track_list