feat(人物管理/红框标记): 关键帧人脸标记 + 人物命名重命名回溯 + 人物管理页改版

1. fam-edge 新增 frame_marker.py: Edge 端人脸检测画红框+统计人脸数（NAS ARM 太弱只存图零计算），orchestrator 分析后回传前标记，新增 /api/edge/mark_frames 批量补标端点
2. fam-core 新增 tools/backfill_mark_frames.py: 存量关键帧批量补红框（幂等+备份 frames_orig）
3. db_layer.name_member 增强: 支持自动注册新标签/重命名回溯（旧真名一并替换）/多人组合字符串 REPLACE
4. fam-ui 成员命名页改为人物管理页: 所有人物照片墙+命名重命名+统计去重
5. event_receiver/member_manager 配套适配

fam-edge/src/fam_edge/ai_orchestrator/orchestrator.py

@@ -173,13 +173,20 @@ class AIOrchestrator:
 
     @staticmethod
     def _attach_frame_images(frame_details: List[dict], frame_paths: List[str]) -> None:
-        """把关键帧图片 base64 附加到 frame_details（按位置对齐视觉分析输入帧）"""
+        """把关键帧图片 base64 附加到 frame_details（按位置对齐视觉分析输入帧）
+
+        附带人脸红框标记与 face_count（NAS 落盘 meta.json，UI 据此挑有人像的头像）
+        """
+        from ..frame_marker import mark_jpeg
         for i, fd in enumerate(frame_details):
             if i >= len(frame_paths):
                 break
             try:
                 with open(frame_paths[i], 'rb') as f:
-                    fd['frame_image'] = base64.b64encode(f.read()).decode('ascii')
+                    raw = f.read()
+                marked, faces = mark_jpeg(raw)
+                fd['frame_image'] = base64.b64encode(marked).decode('ascii')
+                fd['face_count'] = faces
             except OSError as e:
                 logger.warning(f"关键帧图片读取失败: {frame_paths[i]}: {e}")
 

fam-edge/src/fam_edge/api_gateway/api_gateway.py

@@ -7,6 +7,7 @@ API-Gateway - Flask 蓝图，接收任务
 3. analyze (旧拉取模式, 兼容保留)
 """
 import os
+import base64
 import threading
 import requests
 from flask import Blueprint, request, jsonify
@@ -440,6 +441,34 @@ def receive_push_task():
             _currently_processing = False
 
 
+@api_bp.route('/api/edge/mark_frames', methods=['POST'])
+def mark_frames():
+    """NAS 存量关键帧批量补红框（检测计算在 Edge，NAS 只存图）"""
+    data = request.get_json(silent=True)
+    if not data:
+        return jsonify({"error": "Invalid JSON"}), 400
+    images = data.get('images')
+    if not isinstance(images, list) or not images or len(images) > 12:
+        return jsonify({"error": "images 需要 1-12 项 [{key, data}]"}), 400
+
+    from ..frame_marker import mark_jpeg
+    results = []
+    for item in images:
+        key = item.get('key', '')
+        b64 = item.get('data', '')
+        try:
+            marked, faces = mark_jpeg(base64.b64decode(b64))
+            results.append({
+                "key": key,
+                "data": base64.b64encode(marked).decode('ascii'),
+                "faces": faces
+            })
+        except Exception as e:
+            logger.warning(f"补标失败 {key}: {e}")
+            results.append({"key": key, "data": None, "faces": 0, "error": str(e)})
+    return jsonify({"results": results}), 200
+
+
 @api_bp.route('/health', methods=['GET'])
 def health():
     """健康检查"""

fam-edge/src/fam_edge/frame_marker.py

@@ -0,0 +1,71 @@
+"""
+Frame-Marker - 关键帧人脸红框标记
+
+Edge 端统一做检测计算（NAS ARM 太弱），NAS 只存图零计算:
+- orchestrator 分析后、回传前: 画红框 + 统计人脸数
+- /api/edge/mark_frames: NAS 存量帧批量补标
+"""
+import os
+import threading
+
+import cv2
+import numpy as np
+
+from .logger import setup_logger
+from .config_loader import load_config
+
+logger = setup_logger('fam-edge.frame_marker')
+
+_lock = threading.Lock()
+_detector = None
+
+DEFAULT_MODEL = '/opt/fam-edge/models/yunet.onnx'
+
+
+def _get_detector():
+    global _detector
+    if _detector is not None:
+        return _detector
+    with _lock:
+        if _detector is not None:
+            return _detector
+        path = load_config().get('frame_marker', {}).get('model_path', DEFAULT_MODEL)
+        if not os.path.isfile(path):
+            logger.warning(f"YuNet 模型不存在，跳过红框标记: {path}")
+            return None
+        det = cv2.FaceDetectorYN_create(path, '', (320, 320), score_threshold=0.6)
+        _detector = det
+        logger.info(f"YuNet 人脸检测器就绪: {path}")
+        return det
+
+
+def mark_jpeg(jpeg_bytes: bytes):
+    """在 JPEG 帧图上画人脸红框
+
+    返回 (标记后的 JPEG bytes, 人脸数)。检测失败/无模型时原样返回。
+    """
+    det = _get_detector()
+    if det is None:
+        return jpeg_bytes, 0
+    img = cv2.imdecode(np.frombuffer(jpeg_bytes, np.uint8), cv2.IMREAD_COLOR)
+    if img is None:
+        return jpeg_bytes, 0
+    h, w = img.shape[:2]
+    with _lock:
+        det.setInputSize((w, h))
+        _, faces = det.detect(img)
+    if faces is None or len(faces) == 0:
+        return jpeg_bytes, 0
+    for f in faces:
+        x, y, fw, fh = int(f[0]), int(f[1]), int(f[2]), int(f[3])
+        # 人脸框外扩 40%，远处小脸也能看清
+        pad_w, pad_h = int(fw * 0.4), int(fh * 0.4)
+        x1 = max(0, x - pad_w)
+        y1 = max(0, y - pad_h)
+        x2 = min(w, x + fw + pad_w)
+        y2 = min(h, y + fh + pad_h)
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
+    ok, buf = cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 85])
+    if not ok:
+        return jpeg_bytes, 0
+    return buf.tobytes(), len(faces)