'commit'

Apps/TeLaiDian/Kit.py

@@ -183,24 +183,38 @@ def detect_cards_cv(image_path, top_ratio=0.40, bottom_ratio=0.12):
     contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
     
     cards = []
-    # 降低最小宽度要求，有些卡片边缘可能没完全闭合
+    # 更严格的最小宽度要求：列表卡片通常接近整宽
-    min_card_width = int(w * 0.6)
+    min_card_width = int(w * 0.8)
     
     temp_boxes = []
     for cnt in contours:
         x, y, cw, ch = cv2.boundingRect(cnt)
+        center_y = y + ch // 2
         
-        # 增加最小高度验证 MIN_CARD_HEIGHT
+        # 绿色营销券区域排除
-        if (cw > min_card_width and 
+        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-            ch > MIN_CARD_HEIGHT * 0.8 and # 稍微放宽高度限制
+        roi = hsv[max(0,y):min(h,y+ch), max(0,x):min(w,x+cw)]
-            y > top_limit and 
+        green_mask = cv2.inRange(roi, np.array([35, 80, 80]), np.array([85, 255, 255]))
-            y + ch < bottom_limit):
+        green_ratio = float(cv2.countNonZero(green_mask)) / (roi.shape[0]*roi.shape[1]) if roi.size > 0 else 0.0
         
-            logger.info(f"✅ 找到候选卡片: y={y}, h={ch}, w={cw}")
+        # 圆角矩形判断：面积占比与顶点数量
+        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
+        ok_shape = (len(approx) >= 6 or extent > 0.85)
+        ok_color = green_ratio < 0.25
+        
+        if ok_width and ok_height and ok_vertical and ok_shape and ok_color:
+            logger.info(f"✅ 找到候选卡片: y={y}, h={ch}, w={cw}, extent={extent:.2f}, verts={len(approx)}, green={green_ratio:.2f}")
             temp_boxes.append((x, y, cw, ch))
         else:
-            if cw > min_card_width:
+            if ok_width:
-                logger.debug(f"❌ 排除候选(不符条件): y={y}, h={ch}, w={cw} (limit: {top_limit}-{bottom_limit}, min_h: {MIN_CARD_HEIGHT*0.8})")
+                logger.debug(f"❌ 排除候选: y={y}, h={ch}, w={cw}, extent={extent:.2f}, verts={len(approx)}, green={green_ratio:.2f} (limit: {top_limit}-{bottom_limit})")
     
     if not temp_boxes:
         logger.warning(f"⚠️ CV 未能在指定范围 ({top_limit}-{bottom_limit}) 内检测到任何场站卡片")

Apps/TeLaiDian/ReadImageKit.py

@@ -245,12 +245,14 @@ class ReadImageKit:
         cv_bboxes = detect_cards_cv(image_path, top_ratio=SAFE_EXCLUDE_RATIO, bottom_ratio=BOTTOM_SAFE_EXCLUDE_RATIO)
         
         if cv_bboxes:
-            draw_rectangles(image_path, bboxes=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"""
-            图片中已经用绿色矩形框标记了 {len(cv_bboxes)} 个可能的充电站卡片。
+            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。
-            请按从上到下的顺序，识别每个绿色框内的场站信息。
             
-            输出格式为 JSON 数组，长度必须为 {len(cv_bboxes)}。
+            输出格式为 JSON 数组。
             每个对象包含：
             - "name": 场站名称
             - "address": 场站地址
@@ -281,22 +283,50 @@ class ReadImageKit:
                 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))
-                for i, res in enumerate(vlm_results):
+                use_bboxes = []
-                    if i < len(cv_bboxes):
+                for b in cv_bboxes:
-                        bbox = cv_bboxes[i]
+                    cy = (b[1] + b[3]) // 2
-                        # 物理坐标硬过滤：即使 CV 识别到了，y 坐标在 58% 以上的也统统干掉
+                    if cy >= y_threshold:
-                        center_y = (bbox[1] + bbox[3]) // 2
+                        use_bboxes.append(b)
-                        if center_y < y_threshold:
+                for res in vlm_results:
-                            logger.warning(f"CV 误报过滤 (y={center_y} < {y_threshold}): 可能是顶部营销卡片")
+                    if not res:
                         continue
-                            
+                    p = res.get("point")
-                        if res and (res.get("is_valid") is True or (res.get("name") and res.get("is_valid") is not False)):
+                    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": [(bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2],
+                        "point": actual_p,
-                                "bbox": bbox
+                        "bbox": matched_bbox
                     })
             elif not cv_bboxes:
                 vlm_list = vlm_results if isinstance(vlm_results, list) else []

T4_TeLaiDian_Simple.py

@@ -0,0 +1,42 @@
+# coding=utf-8
+import sys
+import os
+import asyncio
+import time
+import logging
+import uiautomator2 as u2
+
+project_root = os.path.dirname(os.path.abspath(__file__))
+if project_root not in sys.path:
+    sys.path.append(project_root)
+
+from Apps.TeLaiDian import Kit
+from Apps.TeLaiDian.ReadImageKit import ReadImageKit
+
+logger = Kit.setup_logger("T4_TeLaiDian_Simple", clear_old_log=True)
+
+async def run_simple():
+    Kit.clear_temp_dir()
+    d = u2.connect()
+    w, h = d.window_size()
+    logger.info(f"开始简单流程，当前窗口: {w}x{h}")
+    logger.info("执行显式下拉刷新以校准位置")
+    d.swipe(w // 2, int(h * 0.3), w // 2, int(h * 0.8), duration=0.5)
+    await asyncio.sleep(2.5)
+
+    screenshot_path = Kit.take_screenshot(d, f"tld_list_{int(time.time())}.jpg")
+    logger.info(f"列表页截图: {screenshot_path}")
+
+    rik = ReadImageKit()
+    stations = await rik.analyze_station_list(screenshot_path)
+    logger.info(f"识别到场站数量: {len(stations)}")
+    for i, s in enumerate(stations[:10]):
+        logger.info(f"[{i+1}] {s.get('name')} | point={s.get('point')} | bbox={s.get('bbox')}")
+
+    logger.info("简单流程结束")
+
+if __name__ == "__main__":
+    try:
+        asyncio.run(run_simple())
+    except KeyboardInterrupt:
+        logger.info("用户中断")