'commit'

Apps/TeLaiDian/Crawler.py

@@ -80,8 +80,8 @@ class TeLaiDianCrawler(BaseCrawler):
             if os.path.exists(ad_screen): os.remove(ad_screen)
 
     async def crawl_list_logic(self, d):
-        # 进入列表页先清理广告
+        # [临时禁用] 广告清理逻辑误触严重，先解决主流程问题
-        await self.clear_ads(d)
+        # await self.clear_ads(d)
         
         # [优化] 向下滚动以刷新/校准地理位置
         # 使用更加显式的 swipe 方式：从屏幕 30% 划到 80%

Apps/TeLaiDian/Kit.py

@@ -151,11 +151,11 @@ def detect_price_click_point_cv(image_path):
     
     return [center_x, center_y]
 
-def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
+def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
-    """
+    """
-    使用计算机图形学 (OpenCV) 检测列表中的场站卡片。
+    使用计算机图形学 (OpenCV) 检测列表中的场站卡片。
-    """
+    """
-    from Apps.TeLaiDian.Config.Setting import MIN_CARD_HEIGHT
+    from Apps.TeLaiDian.Config.Setting import MIN_CARD_HEIGHT
     
     img = read_image(image_path)
     if img is None:
@@ -226,14 +226,46 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
                 min(w, x + cw - padding), 
                 min(bottom_limit, y + ch - padding)
             ])
-            
+            
-    return cards
+    return cards
-
+
-def draw_rectangles(image_path, bboxes=None, click_points=None):
+def detect_wide_rounded_card_cv(image_path, min_width_ratio=0.8, min_y_ratio=0.5):
-    """
+    img = read_image(image_path)
-    使用 OpenCV 在图片上绘制矩形框和点击点，生成 _vl.jpg 和 _flag.jpg
+    if img is None:
-    - _vl.jpg: 仅包含矩形框，供视觉模型参考
+        return []
-    - _flag.jpg: 包含矩形框和点击点，供人工调试
+    h, w = img.shape[:2]
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blur = cv2.GaussianBlur(gray, (7, 7), 0)
+    edges = cv2.Canny(blur, 60, 180)
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15, 15))
+    closed = cv2.morphologyEx(edges, cv2.MORPH_CLOSE, kernel)
+    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    results = []
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
+        center_y = y + ch // 2
+        if cw < int(w * min_width_ratio):
+            continue
+        if center_y < int(h * min_y_ratio):
+            continue
+        rect_area = cw * ch
+        cnt_area = cv2.contourArea(cnt)
+        if rect_area <= 0:
+            continue
+        extent = cnt_area / rect_area
+        approx = cv2.approxPolyDP(cnt, 0.02 * cv2.arcLength(cnt, True), True)
+        if extent < 0.7:
+            continue
+        if len(approx) >= 6 or (len(approx) == 4 and extent > 0.85):
+            results.append([x, y, x + cw, y + ch])
+    results.sort(key=lambda b: b[1])
+    return results
+
+def draw_rectangles(image_path, bboxes=None, click_points=None):
+    """
+    使用 OpenCV 在图片上绘制矩形框和点击点，生成 _vl.jpg 和 _flag.jpg
+    - _vl.jpg: 仅包含矩形框，供视觉模型参考
+    - _flag.jpg: 包含矩形框和点击点，供人工调试
     """
     try:
         DEBUG_BOX_COLOR = (0, 255, 0)      # 绿色矩形

Apps/TeLaiDian/ReadImageKit.py

@@ -10,7 +10,7 @@ if project_root not in sys.path:
     sys.path.append(project_root)
 
 from Util.VLMKit import VLMKit
-from Apps.TeLaiDian.Kit import draw_rectangles, detect_cards_cv, setup_logger, read_image
+from Apps.TeLaiDian.Kit import draw_rectangles, detect_cards_cv, setup_logger, read_image, detect_wide_rounded_card_cv
 from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
 
 # 初始化日志
@@ -40,6 +40,17 @@ class ReadImageKit:
         }
         """
         try:
+            cv_bboxes = detect_wide_rounded_card_cv(image_path, min_width_ratio=0.8, min_y_ratio=0.5)
+            if cv_bboxes:
+                img = read_image(image_path)
+                if img is not None:
+                    h, w = img.shape[:2]
+                    bx1, by1, bx2, by2 = cv_bboxes[0]
+                    cx = (bx1 + bx2) // 2
+                    cy = (by1 + by2) // 2
+                    draw_rectangles(image_path, bboxes=[cv_bboxes[0]], click_points=[[cx, cy]])
+                    return {"found": True, "reason": "cv", "point": [int(cx * 1000 / w), int(cy * 1000 / h)], "type": "price_card"}
+            
             res_text = await self.vlm.analyze_image(image_path, prompt)
             json_str = self.vlm.extract_json(res_text)
             data = json.loads(json_str)