'commit'

Apps/AiTeJiYiChong/Crawler.py

@@ -69,6 +69,17 @@ async def get_station_list(d, service, max_scrolls=MAX_SCROLLS):
         image_uuid = str(uuid.uuid4())
         screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
         
+        # 1.5 检查是否存在广告 (VL)
+        logger.info("检查是否存在广告弹窗...")
+        ad_res = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
+        if ad_res:
+            logger.info(f"检测到广告弹窗，准备关闭: {ad_res}")
+            d.click(ad_res['x'], ad_res['y'])
+            await asyncio.sleep(1.5)
+            # 重新截图
+            if os.path.exists(screenshot_path): os.remove(screenshot_path)
+            screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
+
         # 检查是否已经滚动到底部 (排除状态栏后，内容与上次一致)
         current_md5 = Kit.get_image_content_md5(
             screenshot_path, 

Apps/AiTeJiYiChong/ReadImageKit.py

@@ -78,6 +78,76 @@ class ReadImageKit:
         "严格返回纯JSON格式。"
     )
 
+    @classmethod
+    async def detect_ad_popup(cls, image_path: str, device_info=None):
+        """
+        使用 VL 模型检测是否存在弹窗广告，并返回关闭按钮坐标
+        """
+        if not os.path.exists(image_path):
+            return None
+
+        with open(image_path, "rb") as image_file:
+            encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
+            img_input = {"url": f"data:image/jpeg;base64,{encoded_image}"}
+
+        # 构建提示词
+        prompt = (
+            "请仔细检查这张图片中是否存在**弹窗广告**或**悬浮广告**。\n"
+            "广告可能有以下几种形式：\n"
+            "1. **屏幕中央的大型弹窗广告**：通常遮挡了页面内容，内容多为优惠券、活动推广等。\n"
+            "2. **屏幕左侧或右侧的悬浮小广告**：例如带有'活动'、'红包'、'优惠'字样的小图标。\n"
+            "3. **底部的横幅广告**：带有明显的关闭按钮。\n\n"
+            "如果发现了上述任何一种广告，请务必找到该广告的**关闭按钮**（通常是一个'X'符号，或者'关闭'字样，或者位于广告下方/右上角的圆圈叉号）。\n"
+            "请返回该关闭按钮的**中心坐标**。\n\n"
+            "请以纯 JSON 格式输出：\n"
+            "{\n"
+            "  \"has_ad\": true/false,\n"
+            "  \"close_point\": [x, y]  // 0-1000 归一化坐标\n"
+            "}\n"
+            "如果没有广告，请返回 `{\"has_ad\": false}`。\n"
+            "只输出 JSON，不要包含任何其他文字。"
+        )
+
+        try:
+            resp = await cls._client.chat.completions.create(
+                model=VL_MODEL_NAME, # 使用默认 VL 模型即可，或者使用 AD 专用
+                timeout=30,
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": img_input},
+                            {"type": "text", "text": prompt},
+                        ],
+                    }
+                ],
+            )
+            content = resp.choices[0].message.content.strip()
+            raw_json = cls._extract_json(content)
+            result = json.loads(raw_json)
+
+            if result.get("has_ad") and result.get("close_point"):
+                # 转换坐标
+                norm_point = result["close_point"]
+                if len(norm_point) == 2:
+                    if not device_info:
+                        device_info = cls._FALLBACK_DEVICE_INFO
+                    
+                    w = device_info.get("displayWidth", FALLBACK_WIDTH)
+                    h = device_info.get("displayHeight", FALLBACK_HEIGHT)
+                    
+                    # 假设模型返回的是 0-1000 的坐标
+                    x = int(norm_point[0] / 1000 * w)
+                    y = int(norm_point[1] / 1000 * h)
+                    
+                    return {"x": x, "y": y}
+            
+            return None
+
+        except Exception as e:
+            logger.error(f"Failed to detect ad popup: {e}")
+            return None
+
     @staticmethod
     def _extract_json(text: str) -> str:
         if not text:
@@ -509,6 +579,10 @@ class ReadImageKit:
         """
         使用 VL 模型定位详情页中的 '分时价格' 按钮
         """
+        if device_info is None:
+            logger.warning("未提供动态设备信息，使用通用回退配置。")
+            device_info = cls._FALLBACK_DEVICE_INFO
+
         if not os.path.exists(image_path):
             return {}
 
@@ -517,12 +591,14 @@ class ReadImageKit:
             img_input = {"url": f"data:image/jpeg;base64,{encoded}"}
 
         prompt = (
-            "请识别图片中 '分时价格' 或 '查看分时电价' 按钮的中心坐标。\n"
-            "该按钮通常位于充电站详情页，可能是一个带有价格图标或箭头的小型矩形区域。\n"
-            "请直接以 JSON 格式输出该按钮的中心点坐标：\n"
-            "{\"uia_center_x\": 500, \"uia_center_y\": 500}\n"
-            "注意：坐标系为 0-1000 归一化坐标，其中 (0,0) 为左上角，(1000,1000) 为右下角。\n"
-            "只输出 JSON，不要有任何解释文字。"
+            "请找到图片中 '分时价格' 或 '查看分时电价' 按钮区域（通常在详情页，可能是一个带有价格图标或箭头的小型矩形区域）。\n"
+            "返回格式示例：\n"
+            "{\n"
+            '  "bounds": {"x1": 100, "y1": 200, "x2": 300, "y2": 400}, \n'
+            '  "bounds_norm": {"left": 0.1, "top": 0.2, "right": 0.3, "bottom": 0.4}\n'
+            "}\n"
+            "注意：bounds应使用0-1000的归一化坐标空间。\n"
+            "如果未找到，返回空JSON {}。"
         )
 
         try:
@@ -533,26 +609,32 @@ class ReadImageKit:
                     {
                         "role": "user",
                         "content": [
-                            {"type": "text", "text": prompt},
-                            {"type": "image_url", "image_url": img_input}
+                            {"type": "image_url", "image_url": img_input},
+                            {"type": "text", "text": json.dumps(device_info, ensure_ascii=False)},
+                            {"type": "text", "text": prompt}
                         ]
                     }
                 ],
-                max_tokens=100,
+                max_tokens=200,
                 temperature=0.01
             )
             content = resp.choices[0].message.content.strip()
             raw = cls._extract_json(content)
-            res = json.loads(raw)
             
-            # 转换 0-1000 坐标到实际像素坐标
-            if res.get("uia_center_x") is not None and device_info:
-                w = device_info.get("width", FALLBACK_WIDTH)
-                h = device_info.get("height", FALLBACK_HEIGHT)
-                res["uia_center_x"] = int(res["uia_center_x"] * w / 1000)
-                res["uia_center_y"] = int(res["uia_center_y"] * h / 1000)
+            try:
+                data = json.loads(raw)
+                if isinstance(data, dict) and (data.get("bounds") or data.get("bounds_norm")):
+                    data = cls._add_center(data, device_info)
+                    # 只返回中心坐标
+                    return {
+                        "uia_center_x": data.get("uia_center_x"),
+                        "uia_center_y": data.get("uia_center_y")
+                    }
+                return {}
+            except Exception as e:
+                logger.error(f"Error parsing JSON: {e}")
+                return {}
                 
-            return res
         except Exception as e:
             logger.error(f"VL find_time_price_button_coordinate error: {e}")
             return {}

Apps/XinDianTu/Crawler.py

@@ -163,6 +163,17 @@ async def get_station_list(d, service, uploader, max_scrolls=MAX_SCROLLS):
         # 1. 拍摄截图
         image_uuid = str(uuid.uuid4())
         screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
+
+        # 1.5 检查是否存在广告 (VL)
+        logger.info("检查是否存在广告弹窗...")
+        ad_res = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
+        if ad_res:
+            logger.info(f"检测到广告弹窗，准备关闭: {ad_res}")
+            d.click(ad_res['x'], ad_res['y'])
+            await asyncio.sleep(1.5)
+            # 重新截图
+            if os.path.exists(screenshot_path): os.remove(screenshot_path)
+            screenshot_path = take_screenshot(d, image_uuid, save_dir=TEMP_IMAGE_DIR)
         
         # 检查是否已经滚动到底部 (排除状态栏后，内容与上次一致)
         current_md5 = Kit.get_image_content_md5(

Apps/XinDianTu/Kit.py

@@ -2,7 +2,7 @@ import logging
 import os
 import cv2
 import numpy as np
-import time
+import time
 import hashlib
 from Apps.XinDianTu.Config.Setting import BOTTOM_SAFE_EXCLUDE_RATIO
 from Config.Config import TEMP_IMAGE_DIR
@@ -96,82 +96,7 @@ def clear_temp_dir(save_dir=TEMP_IMAGE_DIR):
             logger.error(f"无法删除文件 {file_path}: {e}")
 
 
-def click_image_template(d, template_path, timeout=5.0, threshold=0.8):
-    """
-    使用 OpenCV 模板匹配查找并点击图片
-    :param d: uiautomator2 设备对象
-    :param template_path: 模板图片路径
-    :param timeout: 超时时间（秒）
-    :param threshold: 匹配阈值 (0.0 - 1.0)
-    :return: 是否点击成功
-    """
-    if not os.path.exists(template_path):
-        logger.info(f"Template file not found: {template_path}")
-        return False
 
-    template = read_image(template_path)
-    if template is None:
-        logger.info(f"Failed to load template: {template_path}")
-        return False
-
-    t_h, t_w = template.shape[:2]
-
-    start_time = time.time()
-    best_val_overall = 0.0
-
-    while time.time() - start_time < timeout:
-        # 临时截图
-        temp_uuid = "temp_click_check"
-        screenshot_path = take_screenshot(d, temp_uuid, save_dir=TEMP_IMAGE_DIR)
-
-        target = read_image(screenshot_path)
-        if target is None:
-            time.sleep(0.5)
-            continue
-
-        # 多尺度匹配
-        found = None
-        # 缩放比例从 0.5 到 1.5，步长 0.1
-        for scale in np.linspace(0.5, 1.5, 11):
-            # 调整模板大小
-            resized_template = cv2.resize(template, (int(t_w * scale), int(t_h * scale)))
-            r_h, r_w = resized_template.shape[:2]
-
-            # 如果模板比目标还大，跳过
-            if r_h > target.shape[0] or r_w > target.shape[1]:
-                continue
-
-            result = cv2.matchTemplate(target, resized_template, cv2.TM_CCOEFF_NORMED)
-            min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
-
-            if max_val > best_val_overall:
-                best_val_overall = max_val
-
-            if max_val >= threshold:
-                found = (max_val, max_loc, r_w, r_h)
-                break  # 找到满足阈值的即停止当前截图的搜索
-
-        # 清理临时文件
-        try:
-            os.remove(screenshot_path)
-        except:
-            pass
-
-        if found:
-            max_val, max_loc, r_w, r_h = found
-            # 计算中心点
-            top_left = max_loc
-            center_x = top_left[0] + r_w // 2
-            center_y = top_left[1] + r_h // 2
-
-            logger.info(f"Found image at ({center_x}, {center_y}) with confidence {max_val:.2f}")
-            d.click(center_x, center_y)
-            return True
-
-        time.sleep(1.0)
-
-    logger.info(f"Image not found after {timeout}s (Best confidence: {best_val_overall:.2f})")
-    return False
 
 
 def is_background_dimmed(image_path, threshold=80):
@@ -711,29 +636,29 @@ def get_row_stats(gray):
     return row_means, row_stds, edge_means
 
 
-def clean_station_name(name):
-    """
-    清理场站名称，移除结尾的省略号及多余空格，方便比对
-    """
-    if not name: return ""
-    name = name.strip()
-    # 移除结尾的 . (通常是截断标识)
-    while name.endswith("."):
-        name = name[:-1]
-    return name.strip()
-
-
-def get_file_md5(path):
-    """计算文件的 MD5 值"""
-    if not os.path.exists(path):
-        return ""
-    hash_md5 = hashlib.md5()
-    with open(path, "rb") as f:
-        for chunk in iter(lambda: f.read(4096), b""):
-            hash_md5.update(chunk)
-    return hash_md5.hexdigest()
-
-
+def clean_station_name(name):
+    """
+    清理场站名称，移除结尾的省略号及多余空格，方便比对
+    """
+    if not name: return ""
+    name = name.strip()
+    # 移除结尾的 . (通常是截断标识)
+    while name.endswith("."):
+        name = name[:-1]
+    return name.strip()
+
+
+def get_file_md5(path):
+    """计算文件的 MD5 值"""
+    if not os.path.exists(path):
+        return ""
+    hash_md5 = hashlib.md5()
+    with open(path, "rb") as f:
+        for chunk in iter(lambda: f.read(4096), b""):
+            hash_md5.update(chunk)
+    return hash_md5.hexdigest()
+
+
 def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
     """
     从图片中裁剪场站卡片

Apps/XinDianTu/Opener.py

@@ -7,8 +7,7 @@ import uuid
 
 import uiautomator2 as u2
 
-from Apps.XinDianTu.Kit import take_screenshot, detect_black_agree_button, detect_any_ad_close, \
-    detect_bottom_close_circle, is_background_dimmed, click_image_template
+from Apps.XinDianTu.Kit import take_screenshot
 from Apps.XinDianTu.ReadImageKit import ReadImageKit
 from Config.Config import TEMP_IMAGE_DIR
 
@@ -25,9 +24,9 @@ BASE_DIR = os.path.dirname(os.path.abspath(__file__))
 
 async def check_and_close_ad(d):
     """
-    检测并关闭广告弹窗（优先使用计算机图形学方案，节省成本和时间）
+    检测并关闭广告弹窗（仅使用 VL 视觉模型）
     """
-    logger.info("开始检测广告弹窗...")
+    logger.info("开始检测广告弹窗 (VL方案)...")
     
     # 1. 拍摄截图
     t1 = time.time()
@@ -37,59 +36,7 @@ async def check_and_close_ad(d):
     screenshot_path = take_screenshot(d, image_uuid, save_dir=save_dir)
     logger.info(f"Step [广告检测截图] 耗时: {time.time() - t1:.4f}s")
 
-    # 1.1 特征预检：检测背景蒙板
-    is_dimmed = is_background_dimmed(screenshot_path)
-    if is_dimmed:
-        logger.info("检测到背景变暗（蒙板），极大概率存在广告弹窗。")
-    
-    # 2. 图形学检测方案 (Local CV)
-    t_cv = time.time()
-    
-    # 方案 A: 检测黑色的"同意"按钮 (隐私协议)
-    # 这一步不需要模板，直接通过颜色和形状特征检测
-    agree_pos = detect_black_agree_button(screenshot_path, debug_dir=save_dir)
-    if agree_pos:
-        x, y = agree_pos
-        logger.info(f"通过图形学算法检测到隐私协议'同意'按钮: ({x}, {y})")
-        d.click(x, y)
-        logger.info(f"Step [图形学检测-同意按钮] 耗时: {time.time() - t_cv:.4f}s")
-        # 清理截图
-        if os.path.exists(screenshot_path):
-            os.remove(screenshot_path)
-        return True
-        
-    # 方案 B: 多模板匹配检测 "关闭(X)" 按钮 (支持多种广告样式)
-    template_dir = os.path.join(BASE_DIR, "Templates")
-    
-    if os.path.exists(template_dir):
-        logger.info(f"正在尝试多模板匹配关闭按钮: {template_dir}")
-        # 如果检测到蒙板，可以适当调低匹配阈值
-        match_threshold = 0.6 if is_dimmed else 0.7
-        close_pos = detect_ad_close_x_with_threshold(screenshot_path, template_dir, save_dir, match_threshold)
-        
-        if close_pos:
-            x, y = close_pos
-            logger.info(f"通过多模板匹配检测到广告关闭按钮: ({x}, {y})")
-            d.click(x, y)
-            logger.info(f"Step [图形学检测-关闭按钮] 耗时: {time.time() - t_cv:.4f}s")
-            if os.path.exists(screenshot_path):
-                os.remove(screenshot_path)
-            return True
-            
-    # 方案 C: 底部圆形关闭按钮检测 (无模板，基于几何特征)
-    # 针对插屏广告底部中间的圆形关闭按钮
-    circle_pos = detect_bottom_close_circle(screenshot_path, debug_dir=save_dir)
-    if circle_pos:
-        x, y = circle_pos
-        logger.info(f"通过几何特征检测到底部圆形关闭按钮: ({x}, {y})")
-        d.click(x, y)
-        logger.info(f"Step [图形学检测-底部圆形按钮] 耗时: {time.time() - t_cv:.4f}s")
-        if os.path.exists(screenshot_path):
-            os.remove(screenshot_path)
-        return True
-    
-    # 3. 视觉大模型检测方案 (VL Model) - 作为兜底
-    logger.info("本地图形学检测未发现广告，尝试调用视觉大模型检测...")
+    # 2. 视觉大模型检测方案 (VL Model)
     try:
         window_size = d.window_size()
         device_info = {
@@ -98,9 +45,10 @@ async def check_and_close_ad(d):
             "productName": d.info.get('productName', 'unknown')
         }
         
-        ad_result = await ReadImageKit.detect_ad(screenshot_path, device_info=device_info)
-        if ad_result.get("has_ad") and ad_result.get("uia_center_x") is not None:
-            x, y = ad_result["uia_center_x"], ad_result["uia_center_y"]
+        # 使用最新的 detect_ad_popup 方法
+        ad_result = await ReadImageKit.detect_ad_popup(screenshot_path, device_info=device_info)
+        if ad_result:
+            x, y = ad_result["x"], ad_result["y"]
             logger.info(f"通过视觉大模型检测到广告关闭按钮: ({x}, {y})")
             d.click(x, y)
             if os.path.exists(screenshot_path):
@@ -109,7 +57,7 @@ async def check_and_close_ad(d):
     except Exception as e:
         logger.error(f"视觉大模型广告检测异常: {e}")
 
-    logger.info(f"本地图形学和 VL 检测完成，未发现已知广告。")
+    logger.info(f"VL 检测完成，未发现已知广告。")
     
     # 清理本地截图
     if os.path.exists(screenshot_path):
@@ -117,18 +65,6 @@ async def check_and_close_ad(d):
 
     return False
 
-def detect_ad_close_x_with_threshold(screenshot_path, template_dir, debug_dir, threshold):
-    """
-    带自定义阈值的多模板匹配
-    """
-    for filename in os.listdir(template_dir):
-        if filename.startswith("ad_close") and filename.endswith(".jpg"):
-            template_path = os.path.join(template_dir, filename)
-            from Apps.XinDianTu.Kit import detect_ad_close_x
-            pos = detect_ad_close_x(screenshot_path, template_path, debug_dir=debug_dir, threshold=threshold)
-            if pos:
-                return pos
-    return None
 
 
 async def open_mini_program():

Apps/XinDianTu/ReadImageKit.py

@@ -74,6 +74,76 @@ class ReadImageKit:
         "严格返回纯JSON格式。"
     )
 
+    @classmethod
+    async def detect_ad_popup(cls, image_path: str, device_info=None):
+        """
+        使用 VL 模型检测是否存在弹窗广告，并返回关闭按钮坐标
+        """
+        if not os.path.exists(image_path):
+            return None
+
+        with open(image_path, "rb") as image_file:
+            encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
+            img_input = {"url": f"data:image/jpeg;base64,{encoded_image}"}
+
+        # 构建提示词
+        prompt = (
+            "请仔细检查这张图片中是否存在**弹窗广告**或**悬浮广告**。\n"
+            "广告可能有以下几种形式：\n"
+            "1. **屏幕中央的大型弹窗广告**：通常遮挡了页面内容，内容多为优惠券、活动推广等。\n"
+            "2. **屏幕左侧或右侧的悬浮小广告**：例如一只兔子形状的图标、红包图标、或者带有'活动'字样的小图标。\n"
+            "3. **底部的横幅广告**：带有明显的关闭按钮。\n\n"
+            "如果发现了上述任何一种广告，请务必找到该广告的**关闭按钮**（通常是一个'X'符号，或者'关闭'字样，或者位于广告下方/右上角的圆圈叉号）。\n"
+            "请返回该关闭按钮的**中心坐标**。\n\n"
+            "请以纯 JSON 格式输出：\n"
+            "{\n"
+            "  \"has_ad\": true/false,\n"
+            "  \"close_point\": [x, y]  // 0-1000 归一化坐标\n"
+            "}\n"
+            "如果没有广告，请返回 `{\"has_ad\": false}`。\n"
+            "只输出 JSON，不要包含任何其他文字。"
+        )
+
+        try:
+            resp = await cls._client.chat.completions.create(
+                model=VL_MODEL_NAME, # 使用默认 VL 模型即可，或者使用 AD 专用
+                timeout=30,
+                messages=[
+                    {
+                        "role": "user",
+                        "content": [
+                            {"type": "image_url", "image_url": img_input},
+                            {"type": "text", "text": prompt},
+                        ],
+                    }
+                ],
+            )
+            content = resp.choices[0].message.content.strip()
+            raw_json = cls._extract_json(content)
+            result = json.loads(raw_json)
+
+            if result.get("has_ad") and result.get("close_point"):
+                # 转换坐标
+                norm_point = result["close_point"]
+                if len(norm_point) == 2:
+                    if not device_info:
+                        device_info = cls._FALLBACK_DEVICE_INFO
+                    
+                    w = device_info.get("displayWidth", FALLBACK_WIDTH)
+                    h = device_info.get("displayHeight", FALLBACK_HEIGHT)
+                    
+                    # 假设模型返回的是 0-1000 的坐标
+                    x = int(norm_point[0] / 1000 * w)
+                    y = int(norm_point[1] / 1000 * h)
+                    
+                    return {"x": x, "y": y}
+            
+            return None
+
+        except Exception as e:
+            logger.error(f"Failed to detect ad popup: {e}")
+            return None
+
     @staticmethod
     def _extract_json(text: str) -> str:
         if not text:

Apps/XinDianTu/verify_rabbit.py

@@ -0,0 +1,46 @@
+
+import os
+import sys
+
+# 将项目根目录添加到 sys.path
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+from Apps.XinDianTu import Kit
+
+def verify():
+    target_path = r"d:\dsWork\aiData\Output\Screenshot_20260112_215026.jpg"
+    template_path = r"d:\dsWork\aiData\Apps\XinDianTu\Templates\TuZi.jpg"
+
+    if not os.path.exists(target_path):
+        print(f"Target not found: {target_path}")
+        return
+
+    if not os.path.exists(template_path):
+        print(f"Template not found: {template_path}")
+        return
+
+    match = Kit.find_template_match(target_path, template_path, threshold=0.7)
+    
+    if match:
+        cx, cy, w, h, val = match
+        print(f"SUCCESS: Found rabbit at ({cx}, {cy}) with size {w}x{h}, confidence {val:.2f}")
+        
+        # Calculate close button position (below the rabbit)
+        # Based on visual estimation from user description, the close button is "below it".
+        # Let's assume a safe offset. Usually ad close buttons are attached to the ad image or slightly below.
+        # If the rabbit image includes the whole ad, the close button might be part of it or below.
+        # If TuZi.jpg is just the rabbit head, the close button is below.
+        
+        close_offset_y = h // 2 + 30  # Start from center, go down half height + 30px
+        close_x = cx
+        close_y = cy + h // 2 + 20 # Try to aim below the image bottom edge
+        
+        print(f"Proposed close button click at: ({close_x}, {close_y})")
+        
+    else:
+        print("FAILED: Rabbit not found in the screenshot.")
+
+if __name__ == "__main__":
+    verify()

Apps/XinDianTu/verify_vl_ad.py

@@ -0,0 +1,32 @@
+
+import asyncio
+import os
+import sys
+
+# 将项目根目录添加到 sys.path
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+from Apps.XinDianTu.ReadImageKit import ReadImageKit
+
+async def verify_vl_ad_check():
+    target_path = r"d:\dsWork\aiData\Output\Screenshot_20260112_215026.jpg"
+
+    if not os.path.exists(target_path):
+        print(f"Target not found: {target_path}")
+        return
+
+    print("Checking for ads using VL model...")
+    # Mock device info for verification script
+    device_info = {"displayWidth": 1080, "displayHeight": 2244}
+    
+    res = await ReadImageKit.detect_ad_popup(target_path, device_info=device_info)
+    
+    if res:
+        print(f"SUCCESS: Ad detected! Close button at ({res['x']}, {res['y']})")
+    else:
+        print("No ad detected.")
+
+if __name__ == "__main__":
+    asyncio.run(verify_vl_ad_check())