【分享】Python 多缺口滑块验证demo

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多缺口滑块验证demo

环境

• win10
• Python3.9

分享一下项目中碰到的一个多缺口滑块验证，先触发一下滑块抓包分析一下是用的哪一家滑块 经过分析发现不是用的顶象或数美，验证图片的接口地址是他自己的的一个接口，应该是自己写的验证，往有经验的大佬指点下。 抓到滑块图片地址，是经过加密的，通过canvas绘画至页面，始终没有找到真实的url，用最笨的方法直接在页面截图

    def get_v3(self):
        # 新开一个窗口，通过执行js来新开一个窗口（有奇效，可以不被检测到）
        js = 'window.open("https://www.vivo.com.cn/service/mobilePhoneAuthenticityCheck/query");'
        self.driver.execute_script(js)
        # 切换窗口
        self.driver.switch_to.window(self.driver.window_handles[1])
        IMEI = "862056063123458"
        # 定位输入框
        phone_IMEI = self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="phone_IMEI"]')))
        phone_IMEI.send_keys(IMEI)
        # 勾选复选框
        self.wait.until(EC.presence_of_element_located((By.XPATH, '/html/body/main/div[2]/div/p/span'))).click()
        # 点击立即查询
        self.wait.until(EC.presence_of_element_located((By.XPATH, '//*[@id="query_IMEI"]'))).click()
        # 定位滑块图片
        # 必须先遍历一遍页面所有元素，否则找不到新弹出的滑块元素
        self.web_driver_wait_ruishu(10, "class", 'dx_captcha dx_captcha_loading-style-popup dx_captcha_basic dx_captcha-type-basic dx_captcha_basic-style-popup')
        print("定位到滑块弹窗")
        dx_captcha = self.wait.until(EC.presence_of_all_elements_located((By.XPATH, '/html/body/div/div/div[2]/div[2]/div[1]/div[2]/div[2]')))
        print(len(dx_captcha))
        if len(dx_captcha) > 1:
            dx_captcha = dx_captcha[-1]
        else:
            dx_captcha = dx_captcha[0]
        # 截图
        dx_captcha.screenshot(self.bgImg_path)

剩下的就是识别缺口距离了，并生成移动轨迹

@staticmethod
    def clear_white(img):
        """清除图片的空白区域，这里主要清除滑块的空白"""
        img = cv2.imread(img)
        rows, cols, channel = img.shape
        min_x = 255
        min_y = 255
        max_x = 0
        max_y = 0
        for x in range(1, rows):
            for y in range(1, cols):
                t = set(img[x, y])
                if len(t) >= 2:
                    if x <= min_x:
                        min_x = x
                    elif x >= max_x:
                        max_x = x

                    if y <= min_y:
                        min_y = y
                    elif y >= max_y:
                        max_y = y
        img1 = img[min_x:max_x, min_y: max_y]
        return img1

    @staticmethod
    def template_match(tpl, target):
        th, tw = tpl.shape[:2]
        result = cv2.matchTemplate(target, tpl, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
        tl = max_loc
        br = (tl[0] + tw, tl[1] + th)
        cv2.rectangle(target, tl, br, (0, 0, 255), 2)
        return tl[0], tl[1]

    def calculate_distance(self, pic1_path, pic2_path):
        """
        计算滑块到缺口的距离
        """
        img1 = self.clear_white(pic1_path)
        img1 = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
        slide = cv2.Canny(img1, 100, 200)

        img2 = cv2.imread(pic2_path, 0)
        back = cv2.Canny(img2, 100, 200)

        slide_pic = cv2.cvtColor(slide, cv2.COLOR_GRAY2RGB)
        back_pic = cv2.cvtColor(back, cv2.COLOR_GRAY2RGB)
        # 滑块在图片上的位置
        x, y = self.template_match(slide_pic, back_pic)
        # 滑块到缺口的距离
        dis_x = int((x + 5) * (340 / 552))
        dis_y = int(y * (340 / 552))
        return dis_x, dis_y

    def get_tracks(self, distance, _y):
        """
        获取轨迹参数
        """
        tracks = list()
        y, v, t, current = 0, 0, 1, 0

        mid = distance * 3 / 4

        exceed = random.randint(40, 90)
        z = random.randint(30, 150)

        while current < (distance + exceed):
            if current < mid / 2:
                a = 2
            elif current < mid:
                a = 3
            else:
                a = -3
            a /= 2
            v0 = v
            s = v0 * t + 0.5 * a * (t * t)
            current += int(s)
            v = v0 + a * t

            y += random.randint(-3, 3)
            z = z + random.randint(5, 10)
            tracks.append([min(current, (distance + exceed)), y, z])

        while exceed > 0:
            exceed -= random.randint(0, 5)
            y += random.randint(-3, 3)
            z = z + random.randint(5, 9)
            tracks.append([min(current, (distance + exceed)), y, z])
        tr = []
        for i, x in enumerate(tracks):
            tr.append({
                'x': x[0],
                'y': _y,
                'relative_time': x[2]
            })
        return tr

效果

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