'commit'

2026-01-13 21:37:14 +08:00
parent ddb63403b7
commit 46f17aed11
18 changed files with 712 additions and 98 deletions
--- a/Apps/YeLiTe/Kit.py
+++ b/Apps/YeLiTe/Kit.py
@@ -66,10 +66,7 @@ def get_image_content_md5(file_path, top_ratio=0.1, bottom_ratio=0.1):
    # 裁剪中间部分
    content = img[top:bottom, :]

-    # 将图片数据转换为字节流计算 MD5
-    success, encoded_img = cv2.imencode(".jpg", content)
-    if success:
-        return hashlib.md5(encoded_img.tobytes()).hexdigest()
+    # 直接使用原始像素数组计算 MD5，避免压缩损失
    return hashlib.md5(content.tobytes()).hexdigest()

 def read_image(path):
@@ -98,93 +95,117 @@ def save_image(path, img):
        logger.error(f"Error saving image {path}: {e}")
        return False

+def find_template(image_path, template_name, threshold=0.8):
+    """
+    在图片中查找模板位置
+    """
+    img = read_image(image_path)
+    if img is None:
+        return None
+        
+    template_path = os.path.join(os.path.dirname(__file__), "Template", template_name)
+    if not os.path.exists(template_path):
+        logger.warning(f"Template not found: {template_path}")
+        return None
+        
+    template = read_image(template_path)
+    if template is None:
+        return None
+        
+    res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
+    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
+    
+    if max_val >= threshold:
+        t_h, t_w = template.shape[:2]
+        center_x = max_loc[0] + t_w // 2
+        center_y = max_loc[1] + t_h // 2
+        return (center_x, center_y)
+    return None
+
 def detect_cards_cv(image_path, top_ratio=0.15, bottom_ratio=0.1):
    """
    使用计算机图形学 (OpenCV) 检测列表中的场站卡片
-    方案：以 nav.jpg 为锚点，向上/下探测白色卡片边界
+    方案：使用 Canny 边缘检测 + 轮廓查找，提取圆角矩形卡片
    """
    img = read_image(image_path)
    if img is None:
        return []
        
    h, w = img.shape[:2]
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    
-    # 1. 模板匹配查找锚点 (nav.jpg)
-    template_path = os.path.join(os.path.dirname(__file__), "Template", "nav.jpg")
-    anchors = []
-    if os.path.exists(template_path):
-        template = read_image(template_path)
-        if template is not None:
-            res = cv2.matchTemplate(img, template, cv2.TM_CCOEFF_NORMED)
-            threshold = 0.8
-            loc = np.where(res >= threshold)
-            
-            # 去重记录锚点中心
-            t_h, t_w = template.shape[:2]
-            matched_points = []
-            for pt in zip(*loc[::-1]): # (x, y)
-                is_duplicate = False
-                for mx, my in matched_points:
-                    if abs(mx - pt[0]) < 50 and abs(my - pt[1]) < 50:
-                        is_duplicate = True
-                        break
-                if not is_duplicate:
-                    matched_points.append(pt)
-                    anchors.append((pt[0] + t_w // 2, pt[1] + t_h // 2))
-                    
-    logger.info(f"通过模板匹配找到 {len(anchors)} 个锚点")
-
-    # 2. 基于锚点向上/下扩张探测边界
-    # 计算每一行的平均亮度，用于探测背景
-    row_means = np.mean(gray[:, int(w*0.2):int(w*0.8)], axis=1)
+    # 1. 预处理：转灰度 -> 高斯模糊去噪
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    
+    # 2. Canny 边缘检测
+    # 阈值设置：降低阈值以适应低对比度边缘 (如浅灰背景上的白卡片)
+    # 经过测试，(10, 50) 对于浅灰背景上的白卡片效果最佳
+    edges = cv2.Canny(blurred, 10, 50)
+    
+    # 3. 查找轮廓
+    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    
    cards = []
-    for ax, ay in anchors:
-        # 向上探测：直到亮度低于 245 (进入灰色背景)
-        y1 = ay
-        while y1 > h * top_ratio:
-            if row_means[int(y1)] < 245:
-                break
-            y1 -= 1
+    min_card_width = int(w * 0.8) # 卡片宽度通常占屏幕 80% 以上
+    min_card_height = 150         # 卡片高度至少 150px
+    top_limit = int(h * top_ratio)
+    bottom_limit = int(h * (1 - bottom_ratio))
+    
+    valid_contours = []
+    
+    for cnt in contours:
+        x, y, cw, ch = cv2.boundingRect(cnt)
        
-        # 向下探测：直到亮度低于 245
-        y2 = ay
-        while y2 < h * (1 - bottom_ratio):
-            if row_means[int(y2)] < 245:
-                break
-            y2 += 1
+        # 筛选条件：
+        # 1. 宽度足够大 (排除小图标、文字)
+        # 2. 高度适中
+        # 3. 位置在安全区域内 (排除顶部导航和底部 TabBar)
+        if (cw > min_card_width and 
+            ch > min_card_height and 
+            y > top_limit and 
+            y + ch < bottom_limit):
            
-        # 验证卡片高度是否合理
-        if y2 - y1 > 150:
-            # 给 y 坐标增加一点 padding
-            cards.append([int(w * 0.05), int(y1 + 5), int(w * 0.95), int(y2 - 5)])
-
-    # 3. 兜底方案：如果模板匹配没找到，使用之前的投影法
-    if not cards:
-        logger.warning("模板匹配未找到卡片，尝试投影法兜底...")
-        _, binary = cv2.threshold(gray, 250, 255, cv2.THRESH_BINARY)
-        top = int(h * top_ratio)
-        bottom = int(h * (1 - bottom_ratio))
-        roi_binary = binary[:, int(w*0.2):int(w*0.8)]
-        row_white_counts = np.sum(roi_binary == 255, axis=1)
-        threshold_count = (int(w*0.8) - int(w*0.2)) * 0.9
-        is_card_row = row_white_counts > threshold_count
-        
-        start_y = None
-        for y in range(top, bottom):
-            if is_card_row[y]:
-                if start_y is None: start_y = y
-            else:
-                if start_y is not None:
-                    if y - start_y > 150:
-                        cards.append([int(w * 0.05), start_y + 10, int(w * 0.95), y - 10])
-                    start_y = None
-        if start_y is not None and bottom - start_y > 150:
-            cards.append([int(w * 0.05), start_y + 10, int(w * 0.95), bottom - 10])
-
-    # 4. 排序
+            # 简单去重：检查是否与已有的卡片高度重叠
+            is_duplicate = False
+            center_y = y + ch // 2
+            for vx, vy, vcw, vch in valid_contours:
+                v_center_y = vy + vch // 2
+                if abs(center_y - v_center_y) < 50: # 中心点接近，视为同一个
+                    is_duplicate = True
+                    break
+            
+            if not is_duplicate:
+                valid_contours.append((x, y, cw, ch))
+                # 增加一点 padding，收缩一下边界，避免点到边缘
+                padding = 5
+                cards.append([
+                    max(0, x + padding), 
+                    max(0, y + padding), 
+                    min(w, x + cw - padding), 
+                    min(h, y + ch - padding)
+                ])
+    
+    # 按 y 坐标排序
    cards.sort(key=lambda c: c[1])
+    
+    # 如果没找到，尝试稍微放宽条件兜底 (比如宽度 > 60%)
+    if not cards:
+        logger.warning("严格轮廓检测未找到卡片，尝试放宽条件...")
+        for cnt in contours:
+            x, y, cw, ch = cv2.boundingRect(cnt)
+            if (cw > int(w * 0.6) and ch > 100 and y > top_limit and y + ch < bottom_limit):
+                 # 简单去重
+                center_y = y + ch // 2
+                is_duplicate = False
+                for existing in cards:
+                     ec_y = (existing[1] + existing[3]) // 2
+                     if abs(center_y - ec_y) < 50:
+                         is_duplicate = True
+                         break
+                if not is_duplicate:
+                    cards.append([x, y, x+cw, y+ch])
+        cards.sort(key=lambda c: c[1])
+
    logger.info(f"最终检测到 {len(cards)} 个场站卡片区域")
    return cards