'commit'

Apps/TeLaiDian/Kit.py

@@ -93,6 +93,48 @@ def save_image(path, img):
         logger.error(f"Error saving image {path}: {e}")
         return False
 
+def detect_list_price_blocks_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
+    img = read_image(image_path)
+    if img is None:
+        return []
+    h, w = img.shape[:2]
+    top_limit = int(h * top_ratio)
+    bottom_limit = int(h * (1 - bottom_ratio))
+    roi_bgr = img[top_limit:bottom_limit, :, :]
+    if roi_bgr.size == 0:
+        return []
+    b, g, r = cv2.split(roi_bgr)
+    mask_bgr = (r > 190) & (g > 80) & (r - g > 25) & (r - b > 25)
+    mask_bgr = (mask_bgr.astype(np.uint8)) * 255
+    if cv2.countNonZero(mask_bgr) < 50:
+        hsv = cv2.cvtColor(roi_bgr, cv2.COLOR_BGR2HSV)
+        lower_red1 = np.array([0, 100, 120])
+        upper_red1 = np.array([20, 255, 255])
+        lower_red2 = np.array([160, 100, 120])
+        upper_red2 = np.array([180, 255, 255])
+        mask1 = cv2.inRange(hsv, lower_red1, upper_red1)
+        mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
+        mask = cv2.bitwise_or(mask1, mask2)
+    else:
+        mask = mask_bgr
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
+    closed = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel)
+    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    blocks = []
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
+        y_global = y + top_limit
+        if ch < 30 or ch > 140:
+            continue
+        if cw < 80 or cw > int(w * 0.8):
+            continue
+        center_y = y_global + ch // 2
+        if center_y < int(h * 0.6) or center_y > bottom_limit:
+            continue
+        blocks.append([x, y_global, x + cw, y_global + ch])
+    blocks.sort(key=lambda b: b[1])
+    return blocks
+
 def detect_price_click_point_cv(image_path):
     """
     使用 HSV 颜色过滤定位详情页的橘红色价格区域，返回最左侧区域的中心点击点
@@ -162,7 +204,28 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
         return []
         
     h, w = img.shape[:2]
-    
+        
+    price_blocks = detect_list_price_blocks_cv(image_path, top_ratio=top_ratio, bottom_ratio=bottom_ratio)
+    cards = []
+    if price_blocks:
+        from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
+        top_limit = int(h * SAFE_EXCLUDE_RATIO)
+        bottom_limit = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
+        for b in price_blocks:
+            px1, py1, px2, py2 = b
+            center_y = (py1 + py2) // 2
+            card_height = MIN_CARD_HEIGHT
+            card_top = max(top_limit, center_y - int(card_height * 0.7))
+            card_bottom = card_top + card_height
+            if card_bottom > bottom_limit:
+                card_bottom = bottom_limit
+                card_top = max(top_limit, card_bottom - card_height)
+            card_left = 0
+            card_right = w
+            cards.append([card_left, card_top, card_right, card_bottom])
+        cards.sort(key=lambda b: b[1])
+        return cards
+
     # 转换为灰度图
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
     
@@ -205,7 +268,7 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
         
         ok_width = cw >= min_card_width
         ok_height = ch > MIN_CARD_HEIGHT * 0.8
-        ok_vertical = center_y >= int(h * 0.58) and y > top_limit and y + ch < bottom_limit
+        ok_vertical = center_y >= int(h * 0.58) and y > top_limit and center_y < bottom_limit
         ok_shape = (len(approx) >= 6 or extent > 0.85)
         ok_color = green_ratio < 0.25
         
@@ -222,24 +285,43 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
     # 按 Y 轴排序
     temp_boxes.sort(key=lambda b: b[1])
     
-    # 再次过滤和去重
+    # 再次过滤和去重，并根据高度拆分为多张卡片
     for i, box in enumerate(temp_boxes):
         x, y, cw, ch = box
         
-        # 检查是否与已有的框重叠
+        # 检查是否与已有的框重叠（按顶部 y 去重）
         is_duplicate = False
         for v in cards:
-            if abs(y - v[1]) < 100: # 增加去重间距
+            if abs(y - v[1]) < 100:
                 is_duplicate = True
                 break
-        if not is_duplicate:
-            padding = 2
-            cards.append([
-                max(0, x + padding), 
-                max(top_limit, y + padding), 
-                min(w, x + cw - padding), 
-                min(bottom_limit, y + ch - padding)
-            ])
+        if is_duplicate:
+            continue
+
+        padding = 2
+        x1 = max(0, x + padding)
+        x2 = min(w, x + cw - padding)
+        y1 = max(top_limit, y + padding)
+        y2 = min(bottom_limit, y + ch - padding)
+
+        if y2 <= y1:
+            continue
+
+        effective_h = y2 - y1
+        estimated_count = max(1, int(round(effective_h / float(MIN_CARD_HEIGHT))))
+
+        if estimated_count <= 1:
+            cards.append([x1, y1, x2, y2])
+        else:
+            seg_h = effective_h / float(estimated_count)
+            for k in range(estimated_count):
+                sy1 = int(round(y1 + k * seg_h))
+                sy2 = int(round(y1 + (k + 1) * seg_h))
+                if sy2 <= sy1:
+                    continue
+                if sy2 - sy1 < MIN_CARD_HEIGHT * 0.6:
+                    continue
+                cards.append([x1, sy1, x2, sy2])
             
     return cards