'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
 

Apps/TeLaiDian/ReadImageKit.py

@@ -11,7 +11,7 @@ if project_root not in sys.path:
 
 from Util.VLMKit import VLMKit
 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
+from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO, MIN_CARD_HEIGHT
 
 # 初始化日志
 logger = setup_logger("ReadImageKit")
@@ -242,36 +242,30 @@ class ReadImageKit:
         """
         分析场站列表页图片，提取场站位置和基本信息
         """
-        cv_bboxes = detect_cards_cv(image_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO)
+        prompt = """
+        分析这张充电站列表截图，提取所有真实的充电站卡片。
         
-        if cv_bboxes:
-            img = read_image(image_path)
-            h, w = img.shape[:2] if img is not None else (2400, 1080)
-            y_threshold = h * 0.58
-            # 在最终绘制阶段再生成可视化，避免早期绘制产生误导
-            prompt = f"""
-            图片下半部分可能包含多个充电站卡片，请按从上到下识别这些卡片的名称与地址。
-            
-            输出格式为 JSON 数组。
-            每个对象包含：
-            - "name": 场站名称
-            - "address": 场站地址
-            - "is_valid": true/false (是否为真实的场站卡片)
-            """
-        else:
-            prompt = """
-            分析这张充电站列表截图，提取所有充电站卡片信息。
-            
-            **重要限制**：
-            1. 忽略页面上半部分（坐标 y < 500）的所有内容，包括顶部的搜索框、广告 Banner 和“PLUS会员/我的卡券”等图标。
-            2. 仅提取下方重复出现的、包含“充电站”名称的矩形卡片。
-            
-            输出格式为 JSON 数组，每个对象包含：
-            - "name": 场站名称
-            - "address": 场站地址
-            - "point": 场站卡片的中心点击坐标 [x, y]
-            - "bbox": 场站卡片的边界框 [x1, y1, x2, y2]
-            """
+        要求：
+        1. 忽略页面上半部分（如顶部导航栏、搜索框、广告 Banner、筛选标签等）。
+        2. 仅识别下半部分一条条“充电站卡片”，每张卡片通常包含：场站名称、评分、最近充电时间、距离、价格、快/慢空闲数量等。
+        3. 不要把同一张卡片拆成多块；每条场站只对应一个矩形框。
+        
+        对于每张卡片，请输出：
+        - name: 场站名称
+        - address: 场站地址（如果无法确定可置为 null）
+        - point: 卡片中心点击坐标 [x, y]，使用归一化坐标 [0-1000]（0 表示最左/最上，1000 表示最右/最下）
+        - bbox: 卡片外接矩形边界 [x1, y1, x2, y2]，同样使用归一化坐标 [0-1000]
+        
+        以 JSON 数组形式输出，例如：
+        [
+          {
+            "name": "某某充电站",
+            "address": "某某路 100 号",
+            "point": [500, 750],
+            "bbox": [50, 600, 950, 820]
+          }
+        ]
+        """
         
         try:
             res_text = await self.vlm.analyze_image(image_path, prompt)
@@ -279,82 +273,79 @@ class ReadImageKit:
             vlm_results = json.loads(json_str)
             
             final_stations = []
-            if cv_bboxes and isinstance(vlm_results, list):
-                img = read_image(image_path)
-                h, w = img.shape[:2] if img is not None else (2400, 1080)
-                y_threshold = h * 0.58
-                bottom_threshold = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
-                use_bboxes = []
-                for b in cv_bboxes:
-                    cy = (b[1] + b[3]) // 2
-                    if cy >= y_threshold:
-                        use_bboxes.append(b)
-                for res in vlm_results:
-                    if not res:
+            vlm_list = vlm_results if isinstance(vlm_results, list) else []
+            img = read_image(image_path)
+            h, w = img.shape[:2] if img is not None else (2400, 1080)
+            y_threshold = h * SAFE_EXCLUDE_RATIO
+            bottom_threshold = int(h * (1 - BOTTOM_SAFE_EXCLUDE_RATIO))
+
+            def to_pixel(v, max_len):
+                if v is None:
+                    return None
+                try:
+                    fv = float(v)
+                except:
+                    return None
+                if fv <= 1000.0:
+                    return int(fv * max_len / 1000.0)
+                return int(fv)
+
+            for res in vlm_list:
+                if not res:
+                    continue
+                p = res.get("point")
+                b = res.get("bbox")
+                actual_p = None
+                actual_bbox = None
+
+                if p and len(p) == 2:
+                    px = to_pixel(p[0], w)
+                    py = to_pixel(p[1], h)
+                    if px is not None and py is not None:
+                        actual_p = [px, py]
+
+                if b and len(b) == 4:
+                    x1 = to_pixel(b[0], w)
+                    y1 = to_pixel(b[1], h)
+                    x2 = to_pixel(b[2], w)
+                    y2 = to_pixel(b[3], h)
+                    if None not in (x1, y1, x2, y2):
+                        actual_bbox = [max(0, int(x1)), max(0, int(y1)), min(w, int(x2)), min(h, int(y2))]
+
+                if actual_p is None and actual_bbox:
+                    actual_p = [(actual_bbox[0] + actual_bbox[2]) // 2, (actual_bbox[1] + actual_bbox[3]) // 2]
+
+                if actual_p is None and actual_bbox is None:
+                    continue
+
+                if actual_p and (actual_p[1] < y_threshold or actual_p[1] > bottom_threshold):
+                    logger.warning(f"过滤掉可能的误点击点 (y={actual_p[1]}): {res.get('name')}")
+                    continue
+
+                if actual_bbox is None and actual_p:
+                    half_w = int(w * 0.4)
+                    half_h = max(MIN_CARD_HEIGHT // 2, 90)
+                    x1 = max(0, actual_p[0] - half_w)
+                    x2 = min(w, actual_p[0] + half_w)
+                    y1 = max(0, actual_p[1] - half_h)
+                    y2 = min(h, actual_p[1] + half_h)
+                    actual_bbox = [x1, y1, x2, y2]
+
+                if actual_bbox is not None:
+                    bx1, by1, bx2, by2 = actual_bbox
+                    current_h = by2 - by1
+                    if current_h < MIN_CARD_HEIGHT * 0.8 or by2 > bottom_threshold:
                         continue
-                    p = res.get("point")
-                    actual_p = None
-                    if p and len(p) == 2:
-                        actual_p = p
-                        if p[0] <= 1000 and p[1] <= 1000:
-                            actual_p = [int(p[0] * w / 1000), int(p[1] * h / 1000)]
-                    # 顶部与底部安全过滤
-                    if actual_p and (actual_p[1] < y_threshold or actual_p[1] > bottom_threshold):
-                        logger.warning(f"过滤掉可能的误点击点 (y={actual_p[1]} < {y_threshold}): {res.get('name')}")
-                        continue
-                    matched_bbox = None
-                    if actual_p:
-                        for b in use_bboxes:
-                            if b[0] <= actual_p[0] <= b[2] and b[1] <= actual_p[1] <= b[3]:
-                                matched_bbox = b
-                                break
-                    # 如果没有匹配到 CV 框，则根据点击点生成一个虚拟框
-                    if matched_bbox is None and actual_p is not None:
-                        half_w = int(w * 0.4)
-                        half_h = 90
-                        x1 = max(0, actual_p[0] - half_w)
-                        x2 = min(w, actual_p[0] + half_w)
-                        y1 = max(0, actual_p[1] - half_h)
-                        y2 = min(h, actual_p[1] + half_h)
-                        matched_bbox = [x1, y1, x2, y2]
-                    # 如果既没有 point 也没有匹配框，跳过
-                    if actual_p is None and matched_bbox is None:
-                        continue
-                    if actual_p is None and matched_bbox:
-                        actual_p = [(matched_bbox[0] + matched_bbox[2]) // 2, (matched_bbox[1] + matched_bbox[3]) // 2]
-                    final_stations.append({
-                        "name": res.get("name"),
-                        "address": res.get("address"),
-                        "point": actual_p,
-                        "bbox": matched_bbox
-                    })
-            elif not cv_bboxes:
-                vlm_list = vlm_results if isinstance(vlm_results, list) else []
-                w, h = 0, 0
-                for res in vlm_list:
-                    p = res.get("point")
-                    if p and len(p) == 2:
-                        # 1. 坐标转换逻辑
-                        actual_p = p
-                        if p[0] <= 1000 and p[1] <= 1000:
-                            if w == 0:
-                                img = read_image(image_path)
-                                if img is not None:
-                                    h, w = img.shape[:2]
-                                else:
-                                    w, h = 1080, 2400 # 兜底
-                            actual_p = [int(p[0] * w / 1000), int(p[1] * h / 1000)]
-                        
-                        # 2. 坐标安全过滤：忽略页面上半部分的误点击（通常是 Logo 或广告）
-                        # 即使 VLM 没听话，我们也在这里硬性过滤
-                        # 根据主屏幕截图，顶部广告区和功能图标区占用了约 55% 的空间
-                        y_threshold = h * 0.58 if h > 0 else 1400
-                        if actual_p[1] < y_threshold:
-                            logger.warning(f"过滤掉可能的误点击点 (y={actual_p[1]} < {y_threshold}): {res.get('name')}")
-                            continue
-                            
-                        res["point"] = actual_p
-                    final_stations.append(res)
+
+                final_stations.append({
+                    "name": res.get("name"),
+                    "address": res.get("address"),
+                    "point": actual_p,
+                    "bbox": actual_bbox
+                })
+            
+            if final_stations:
+                final_stations.sort(key=lambda s: (s.get("point")[1] if s.get("point") else (s.get("bbox")[1] if s.get("bbox") else 0)))
             
             # 保存诊断图片 (_vl.jpg, _flag.jpg)
             if final_stations:

Apps/TeLaiDian/TestCV.py

@@ -30,11 +30,25 @@ def test_cv_detection(image_path):
     for i, box in enumerate(bboxes):
         print(f"  卡片 {i+1}: {box}")
 
-    # 2. 生成 _vl.jpg (仅绿框)
+    # 1.5 检测红色价格块并打印
+    try:
+        price_blocks = Kit.detect_list_price_blocks_cv(image_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO)
+    except AttributeError:
+        price_blocks = []
+    print(f"检测到 {len(price_blocks)} 个红色价格块")
+    for i, pb in enumerate(price_blocks):
+        print(f"  价格块 {i+1}: {pb}")
+
+    # 2. 生成 _vl.jpg (绿框 + 蓝点标记红色价格行)
     vl_path = image_path.replace(".jpg", "_vl.jpg")
     img_vl = Kit.read_image(image_path)
     for box in bboxes:
         cv2.rectangle(img_vl, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 3)
+    for pb in price_blocks:
+        px1, py1, px2, py2 = pb
+        pcx = (px1 + px2) // 2
+        pcy = (py1 + py2) // 2
+        cv2.circle(img_vl, (pcx, pcy), 10, (255, 0, 0), -1)
     Kit.save_image(vl_path, img_vl)
     print(f"已生成 VLM 标注图: {vl_path}")
 
@@ -59,5 +73,5 @@ def test_cv_detection(image_path):
     print(f"已生成人工核对图: {flag_path}")
 
 if __name__ == "__main__":
-    target_image = r"d:\dsWork\aiData\Output\tld_list_1768347471.jpg"
+    target_image = r"d:\dsWork\aiData\Output\tld_list_1768359492.jpg"
     test_cv_detection(target_image)

debug_cv.py

@@ -0,0 +1,93 @@
+
+import sys
+import os
+import cv2
+import numpy as np
+
+sys.path.append(os.getcwd())
+from Apps.TeLaiDian.Kit import detect_cards_cv as real_detect_cards_cv
+from Apps.TeLaiDian.Config.Setting import SAFE_EXCLUDE_RATIO, BOTTOM_SAFE_EXCLUDE_RATIO
+
+def read_image(path):
+    if not path or not os.path.exists(path):
+        return None
+    try:
+        data = np.fromfile(path, dtype=np.uint8)
+        if data.size == 0:
+            return None
+        img = cv2.imdecode(data, -1)
+        return img
+    except Exception as e:
+        print(f"Error reading image {path}: {e}")
+        return None
+
+def detect_cards_cv(image_path, top_ratio=None, bottom_ratio=None):
+    if top_ratio is None:
+        top_ratio = SAFE_EXCLUDE_RATIO
+    if bottom_ratio is None:
+        bottom_ratio = BOTTOM_SAFE_EXCLUDE_RATIO
+    MIN_CARD_HEIGHT = 150 # Assuming default from Setting
+    
+    img = read_image(image_path)
+    if img is None:
+        print("Image not found or invalid")
+        return []
+        
+    h, w = img.shape[:2]
+    print(f"Image Size: {w}x{h}")
+    
+    # 转换为灰度图
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    
+    # 限制检测范围
+    top_limit = int(h * top_ratio)
+    bottom_limit = int(h * (1 - bottom_ratio))
+    print(f"CV limits: top={top_limit}, bottom={bottom_limit}, threshold_y={int(h * 0.58)}")
+    
+    # 使用自适应阈值
+    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+    
+    # 闭运算
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (w // 4, 3))
+    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+    
+    # 寻找轮廓
+    contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    min_card_width = int(w * 0.8)
+    
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
+        center_y = y + ch // 2
+        
+        rect_area = cw * ch
+        cnt_area = cv2.contourArea(cnt)
+        extent = cnt_area / rect_area if rect_area > 0 else 0
+        approx = cv2.approxPolyDP(cnt, 0.02 * cv2.arcLength(cnt, True), True)
+        
+        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
+        
+        # Check green ratio
+        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+        roi = hsv[max(0,y):min(h,y+ch), max(0,x):min(w,x+cw)]
+        green_mask = cv2.inRange(roi, np.array([35, 80, 80]), np.array([85, 255, 255]))
+        green_ratio = float(cv2.countNonZero(green_mask)) / (roi.shape[0]*roi.shape[1]) if roi.size > 0 else 0.0
+        
+        ok_color = green_ratio < 0.25
+        
+        if cw > w * 0.5: # Only print large enough boxes
+             print(f"Box: y={y}, h={ch}, w={cw}, center_y={center_y}, extent={extent:.2f}, green={green_ratio:.2f}")
+             print(f"  Checks: width={ok_width}, height={ok_height}, vertical={ok_vertical}, color={ok_color}")
+
+image_path = r"d:\dsWork\aiData\Output\tld_list_1768359492_flag.jpg"
+# Try the original if flag doesn't exist or is modified
+original_path = r"d:\dsWork\aiData\Output\tld_list_1768359492.jpg"
+
+if os.path.exists(original_path):
+    print(f"Testing original image: {original_path}")
+    detect_cards_cv(original_path)
+else:
+    print(f"Original image not found, trying flag: {image_path}")
+    detect_cards_cv(image_path)