'commit'

Apps/TeLaiDian/Kit.py

@@ -93,6 +93,64 @@ def save_image(path, img):
         logger.error(f"Error saving image {path}: {e}")
         return False
 
+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
+    """
+    img = read_image(image_path)
+    if img is None:
+        return None
+    
+    h, w = img.shape[:2]
+    # 1. 转换为 HSV 空间
+    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
+    
+    # 2. 橘红色的 HSV 范围 (适配特来电价格颜色)
+    lower_orange = np.array([0, 150, 150])
+    upper_orange = np.array([20, 255, 255])
+    mask = cv2.inRange(hsv, lower_orange, upper_orange)
+    
+    # 3. 对掩码进行膨胀，连接数字
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 20))
+    dilated = cv2.dilate(mask, kernel)
+    
+    # 4. 寻找轮廓
+    contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    detected_areas = []
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
+        # 1. 过滤掉宽度过大（可能是横幅广告）或过小（可能是杂点）的区域
+        # 2. 价格区域 P0 通常在屏幕的中部，且宽度约为屏幕的一半
+        if 200 < y < h * 0.8 and 100 < cw < w * 0.6 and ch > 30:
+            detected_areas.append([x, y, x + cw, y + ch])
+
+    if not detected_areas:
+        # 备选：如果 HSV 失败，尝试通过轮廓大小寻找
+        # 1.1556 这种大数字通常会有很明显的轮廓
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)
+        kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
+        closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+        contours, _ = cv2.findContours(closed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        for cnt in contours:
+            x, y, cw, ch = cv2.boundingRect(cnt)
+            if 200 < y < h * 0.6 and 150 < cw < 300 and 50 < ch < 150:
+                detected_areas.append([x, y, x + cw, y + ch])
+
+    if not detected_areas:
+        return None
+
+    # 5. 按 X 轴排序，取最左边的区域 (即用户确认的 P0)
+    # 但要排除掉可能在最左侧的导航栏返回按钮等小元素，所以前面加了宽度限制
+    detected_areas.sort(key=lambda b: b[0])
+    target = detected_areas[0]
+    
+    center_x = (target[0] + target[2]) // 2
+    center_y = (target[1] + target[3]) // 2
+    
+    return [center_x, center_y]
+
 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。