'commit'

Apps/XinDianTu/Kit.py

@@ -810,6 +810,46 @@ def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
 
         temp_valid_segments.append((y1, y2))
 
+    # 对过长的段进行内部切分，避免将两个卡片合并为一个
+    refined_segments = []
+    SPLIT_GAP_MIN = 8
+    SPLIT_MARGIN = 6
+    GAP_STD_STRICT = max(0.0, GAP_STD_MAX - 3.0)
+    for y1, y2 in temp_valid_segments:
+        split_points = []
+        run_len = 0
+        run_start = None
+        for yy in range(y1 + SPLIT_MARGIN, y2 - SPLIT_MARGIN):
+            rm = row_means[yy]
+            rs = row_stds[yy]
+            em = edge_means[yy]
+            is_gap_line = (GAP_MEAN_MIN <= rm <= GAP_MEAN_MAX) and (rs < GAP_STD_STRICT) and (BG_GRAY_MIN <= em <= BG_GRAY_MAX)
+            if is_gap_line:
+                if run_len == 0:
+                    run_start = yy
+                run_len += 1
+            else:
+                if run_len >= SPLIT_GAP_MIN:
+                    sp = (run_start + yy) // 2
+                    split_points.append(sp)
+                run_len = 0
+                run_start = None
+        if run_len >= SPLIT_GAP_MIN and run_start is not None:
+            sp = (run_start + (y2 - SPLIT_MARGIN)) // 2
+            split_points.append(sp)
+
+        if split_points:
+            prev = y1
+            for sp in split_points:
+                if sp - prev > 100:
+                    refined_segments.append((prev, sp))
+                prev = sp
+            if y2 - prev > 100:
+                refined_segments.append((prev, y2))
+        else:
+            refined_segments.append((y1, y2))
+    temp_valid_segments = refined_segments
+
     # 计算统一宽度
     if not candidate_x1:
         logger.info("  No valid width detected. Using default.")
@@ -877,67 +917,20 @@ def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
     # 准备 _vl.jpg (只画框，不画红点)
     vl_img = img.copy()
 
-    # 预先计算“列表浮动区域”的蓝色矩形坐标
-    rabbit_x1 = int(w * 0.04)
-    rabbit_x2 = int(w * 0.96)
-    rabbit_y1 = int(h * 0.74)
-    rabbit_y2 = int(h * 0.86)
-
-    # 在调试图上画出蓝色矩形
-    try:
-        cv2.rectangle(debug_img, (rabbit_x1, rabbit_y1), (rabbit_x2, rabbit_y2), (255, 0, 0), 3)
-        cv2.rectangle(vl_img, (rabbit_x1, rabbit_y1), (rabbit_x2, rabbit_y2), (255, 0, 0), 3)
-        logger.info(f"  标记列表浮动蓝色区域: X={rabbit_x1}-{rabbit_x2}, Y={rabbit_y1}-{rabbit_y2}")
-    except Exception as e:
-        logger.warning(f"标记兔子广告蓝色区域失败: {e}")
-
-    # 基于蓝色矩形过滤与列表浮动区域有交集的场站卡片
-    if final_cards:
-        logger.info(f"  [蓝框过滤] 初始卡片数量: {len(final_cards)}")
-        logger.info(
-            f"  [蓝框过滤] 蓝框坐标: X={rabbit_x1}-{rabbit_x2}, Y={rabbit_y1}-{rabbit_y2}"
-        )
-
-        def _intersects(card):
-            y1, y2, x1, x2 = card
-            cx1, cy1, cx2, cy2 = x1, y1, x2, y2
-            ix1 = max(cx1, rabbit_x1)
-            iy1 = max(cy1, rabbit_y1)
-            ix2 = min(cx2, rabbit_x2)
-            iy2 = min(cy2, rabbit_y2)
-            return ix1 < ix2 and iy1 < iy2
-
-        original_cards = list(final_cards)
-        filtered_cards = []
-
-        for idx, card in enumerate(original_cards, start=1):
-            y1, y2, x1, x2 = card
-            if _intersects(card):
-                logger.info(
-                    f"  [蓝框过滤] 丢弃卡片#{idx}: X={x1}-{x2}, Y={y1}-{y2} (与蓝框有交集)"
-                )
-            else:
-                logger.info(
-                    f"  [蓝框过滤] 保留卡片#{idx}: X={x1}-{x2}, Y={y1}-{y2} (与蓝框无交集)"
-                )
-                filtered_cards.append(card)
-
-        logger.info(
-            f"  [蓝框过滤] 最终保留 {len(filtered_cards)} 个卡片, 丢弃 {len(original_cards) - len(filtered_cards)} 个"
-        )
-        final_cards = filtered_cards
-    else:
-        logger.info("  [蓝框过滤] 没有可过滤的卡片。")
-
     logger.info(f"  Step [2.1/VL] 准备在 VL 图片上绘制 {len(final_cards)} 个场站的绿色方框...")
 
     for idx, (y1, y2, x1, x2) in enumerate(final_cards):
+        # 轻微向上扩展卡片上边界，避免漏掉标题区域
+        PAD_TOP = 5
+        draw_y1 = max(0, y1 - PAD_TOP)
+        draw_y2 = y2
+
         # 计算点击点 (左上角，避免被底部按钮遮挡)
         # 策略：X偏移 15%, Y偏移 20%
         w_card = x2 - x1
-        h_card = y2 - y1
+        h_card = draw_y2 - draw_y1
         click_x = int(x1 + w_card * 0.15)
-        click_y = int(y1 + h_card * 0.20)
+        click_y = int(draw_y1 + h_card * 0.20)
 
         # [修改] 不再保存单张子图，只记录元数据
         # card = img[y1:y2, x1:x2]
@@ -952,28 +945,28 @@ def crop_cards_from_image(img_path, output_dir=None, save_debug=True):
         # 在标记图上画红点 (实心圆, 半径10, 红色BGR)
         cv2.circle(debug_img, (click_x, click_y), 10, (0, 0, 255), -1)
         # [修改] 必须画绿框，因为后续视觉模型依赖这个框来识别范围
-        cv2.rectangle(debug_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.rectangle(debug_img, (x1, draw_y1), (x2, draw_y2), (0, 255, 0), 2)
 
         # 在 _vl 图上只画绿框
-        cv2.rectangle(vl_img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.rectangle(vl_img, (x1, draw_y1), (x2, draw_y2), (0, 255, 0), 2)
 
         # 收集 JSON 数据
         card_info = {
             "id": idx + 1,
-            "rect": [x1, y1, x2, y2],
+            "rect": [x1, draw_y1, x2, draw_y2],
             "bounds_norm": {
                 "left": x1 / w,
-                "top": y1 / h,
+                "top": draw_y1 / h,
                 "right": x2 / w,
-                "bottom": y2 / h
+                "bottom": draw_y2 / h
             },
             "click_point": [click_x, click_y]
         }
         json_data["cards"].append(card_info)
 
         # 记录区域信息供调用者使用 (如果需要)
-        # 格式: (None, (click_x, click_y), (x1, y1, x2, y2))
-        results.append((None, (click_x, click_y), (x1, y1, x2, y2)))
+        # 格式: (None, (click_x, click_y), (x1, draw_y1, x2, draw_y2))
+        results.append((None, (click_x, click_y), (x1, draw_y1, x2, draw_y2)))
 
         # [删除] 之前生成的单张 _for_vl.jpg 逻辑已移除