幾種滑動驗證碼處理

思路：

1 找到距離 
2 生成滑動軌跡  
3 模擬事件

兩張圖片 一個缺口圖片 一個帶缺口的完整圖

參考博客

向右滑到底

1 首先獲取滑動的距離 末端 - 小圖標 中心點 適當多點無所謂
2 生成軌跡

#生成直線軌跡
  def get_tracks_2( distance, seconds, ease_func):
        tracks = [0]
        offsets = [0]
        for t in np.arange(0.0, seconds, 0.1):
            ease = ease_func
            offset = round(ease(t / seconds) * distance)
            tracks.append(offset - offsets[-1])
            offsets.append(offset)
        return tracks

3 最后一步 模擬事件 滑到位置

 def move_to_gap(self, track):
        self._driver.web_driver_wait(2, self._driver.XPATH, 'xpath 路徑')
        slider = self._driver.find_element_by_xpath('xpath 路徑')
        action = ActionChains(self._driver)
        action.click_and_hold(slider)
        while track:
            x = track.pop(0)
            y = x % 2 * random.choice([4, 5, 2])
            action.move_by_offset(xoffset=x, yoffset=y)
        time.sleep(0.5)  # 這里不加延時會導致滑塊失敗
        action.release().perform()

另一種情況 籃球入框

圖片是一個小籃球 和 一個籃球框

1 計算籃球和框的坐標

  self._driver.web_driver_wait(1, self._driver.XPATH, '//div[@class="rds-drag-basket"]')
                    basket_style = self._driver.find_element_by_xpath('xpath 路徑').get_attribute("style")
                    ball_style = self._driver.find_element_by_xpath('xpath 路徑').get_attribute('style')
                    basket_left = re.findall(r"left: (.+?)px", basket_style)[0]
                    basket_top = re.findall(r"top: (.+?)px", basket_style)[0]
                    ball_left = re.findall(r"left: (.+?)px", ball_style)[0]
                    ball_top = re.findall(r"top: (.+?)px", ball_style)[0]
                    x = int(basket_left.split('.')[0]) - int(ball_left.split('.')[0]) + 23
                    y = int(basket_top.split('.')[0]) - int(ball_top.split('.')[0]) + 23
                    self.move_to_basket(x, y)

2 生成軌跡 並移動

 def move_to_basket(self, x, y):
        slider = self._driver.find_element_by_xpath('xpath 路徑')
        action = ActionChains(self._driver)
        action.click_and_hold(slider)
        track1 = self.get_tracks_2(x + random.choice([-1, -2, -3, -4, -5, 1, 2, 3, 4, 5]), random.randint(2, 4), self.ease_out_quart)
        track2 = self.get_tracks_2(y + random.choice([-1, -2, -3, -4, -5, 1, 2, 3, 4, 5]), random.randint(2, 4), self.ease_out_quart)
        while track1 or track2:
            try:
                x = track1.pop(0)
            except:
                x = 0
            try:
                y = track2.pop(0)
            except:
                y = 0
            action.move_by_offset(xoffset=x, yoffset=y)
        time.sleep(0.5)  # 這里不加延時會導致滑塊失敗
        action.release().perform()

最后一種 三張圖片 完整的 完整帶缺口的 缺口圖片

1 比較兩張圖片 然后灰度值 算出缺口位置

 def compute_gap(self, img1, img2):
        """計算缺口偏移 這種方式成功率很高"""
        # 將圖片修改為RGB模式
        img1 = img1.convert("RGB")
        img2 = img2.convert("RGB")

        # 計算差值
        diff = ImageChops.difference(img1, img2)

        # 灰度圖
        diff = diff.convert("L")
        # print(self.otsu_threshold(diff))

        table = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
                 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
                 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        # 二值化 閥值table提前計算好的
        diff = diff.point(table, '1')
        left = 43
        for w in range(diff.size[0] - 1, left, -1):
            lis = []
            for h in range(diff.size[1] - 2, 0, -1):
                if diff.load()[w, h] == 1:
                    lis.append(w)
                if len(lis) > 15:
                    self.error = ErrorLog(img1, img2, diff, w)
                    return w - left

接下來就是常規操作 生成軌跡 移動記好了