feat: 新增视频分析流水线、Web界面、孤儿清理工具；修复健壮性问题
- 新增 analysis/ 模块：MOG2活动检测、YOLO人物跟踪、MediaPipe摔倒检测、Ollama活动打标、缩略图抽取 - 新增 webapp/：Flask事件浏览界面，按需HEVC→H264转码缓存 - 新增 analyze.py：7阶段状态机流水线（scan→backfill→motion→person→fall→activity→thumbnail） - 新增 cleanup_orphans.py：清理文件已删除的数据库孤儿记录 - 修复：motion/person/activity三阶段加单视频异常兜底，避免批次中断 - 修复：ollama_client加健康检查和3次重试，num_ctx从8192降到2048 - 修复：transcode_cache加fcntl文件锁，防止并发转码同一视频 - 修复：SQLite开WAL模式+busy_timeout，支持读写并发 - 修复：sync.py ffmpeg路径改为shutil.which查找，删除automate.py死代码PYTHON - 修复：thumbnail失败加3次重试上限，避免无限重试损坏文件 - 新增：requirements.lock锁定版本、README文档 - 更新：.gitignore排除.playwright-mcp、截图、备份文件
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yuming
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"""在 motion_filter 找出的活动窗口内跑 YOLO11n 检测 + ByteTrack 跟踪，
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统计人物出现次数，写入 events 表（event_type='person_presence'）。
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一个 track（同一个人从出现到消失的连续跟踪片段）对应一条 person_presence
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事件。只在活动窗口内跑，空场景（motion_windows_json 为空）直接跳过，不跑模型。
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"""
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import json
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from datetime import datetime, timedelta
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import cv2
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from analysis.config import (
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PERSON_MODEL_NAME,
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PERSON_DEVICE,
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PERSON_CONF_THRESHOLD,
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PERSON_SAMPLE_STRIDE,
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PERSON_MIN_TRACK_DURATION_SEC,
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PROJECT_ROOT,
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)
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from analysis.db import get_all_videos, insert_event, update_video_status
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_model = None
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def _get_model():
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"""YOLO模型只在真正用到时才加载（避免只跑无活动视频时也触发权重下载/加载）。"""
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global _model
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if _model is None:
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from ultralytics import YOLO
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_model = YOLO(PERSON_MODEL_NAME)
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return _model
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def _track_windows(file_path, windows, fps):
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"""在给定的活动窗口内跑检测+跟踪，返回 [(track_id, start_sec, end_sec, max_conf), ...]。"""
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model = _get_model()
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cap = cv2.VideoCapture(str(file_path))
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if not cap.isOpened():
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return []
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spans = {} # (window_index, track_id) -> [min_t, max_t, max_conf]
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for window_index, (start_sec, end_sec) in enumerate(windows):
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start_frame = int(start_sec * fps)
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end_frame = int(end_sec * fps)
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cap.set(cv2.CAP_PROP_POS_FRAMES, start_frame)
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frame_idx = start_frame
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first_call = True
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while frame_idx <= end_frame:
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ret, frame = cap.read()
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if not ret:
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break
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if (frame_idx - start_frame) % PERSON_SAMPLE_STRIDE == 0:
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# persist=False 强制在每个窗口开头重置跟踪器状态，跟踪器内部的track_id计数器
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# 也会跟着从1重新开始——不同窗口间同一个数字的track_id并不代表同一个人，
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# 所以下面的 spans key 必须带上 window_index，否则会把不同窗口的片段错误合并
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results = model.track(
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frame, persist=not first_call, classes=[0], conf=PERSON_CONF_THRESHOLD,
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tracker="bytetrack.yaml", device=PERSON_DEVICE, verbose=False,
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)
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first_call = False
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boxes = results[0].boxes
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if boxes is not None and boxes.id is not None:
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t = frame_idx / fps
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for track_id, conf in zip(boxes.id.tolist(), boxes.conf.tolist()):
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key = (window_index, int(track_id))
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if key not in spans:
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spans[key] = [t, t, conf]
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else:
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spans[key][1] = t
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spans[key][2] = max(spans[key][2], conf)
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frame_idx += 1
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cap.release()
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return [
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(track_id, start, end, conf)
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for (_window_index, track_id), (start, end, conf) in spans.items()
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if (end - start) >= PERSON_MIN_TRACK_DURATION_SEC
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]
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def process_video(video):
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"""返回 (track_count, failed)。failed=True 表示该视频处理异常已标记 person_failed。"""
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try:
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windows = json.loads(video["motion_windows_json"] or "[]")
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except (json.JSONDecodeError, TypeError):
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windows = []
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if not windows:
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update_video_status(video["id"], "person_done")
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return 0, False
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file_path = PROJECT_ROOT / video["file_path"]
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if not file_path.exists():
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update_video_status(video["id"], "person_failed", error_message="文件不存在")
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return 0, True
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try:
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fps = video["fps"] or 15.0
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tracks = _track_windows(file_path, windows, fps)
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except Exception as e:
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update_video_status(video["id"], "person_failed", error_message=str(e))
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return 0, True
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start_time = None
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if video["start_time"]:
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try:
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start_time = datetime.strptime(video["start_time"], "%Y-%m-%d %H:%M:%S")
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except ValueError:
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pass # 只精确到天的粗粒度值（"YYYY-MM-DD"），无法算出绝对时间戳，timestamp_abs留空
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for track_id, start_sec, end_sec, conf in tracks:
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timestamp_abs = (start_time + timedelta(seconds=start_sec)).isoformat() if start_time else None
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insert_event({
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"video_id": video["id"],
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"event_type": "person_presence",
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"label": None,
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"start_offset_sec": start_sec,
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"end_offset_sec": end_sec,
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"timestamp_abs": timestamp_abs,
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"confidence": conf,
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"track_id": track_id,
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"thumbnail_path": None,
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"extra_json": None,
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"created_at": datetime.now().isoformat(),
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})
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update_video_status(video["id"], "person_done")
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return len(tracks), False
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def process_all(limit=None):
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videos = get_all_videos(status="motion_done")
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if limit:
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videos = videos[:limit]
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processed = 0
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total_tracks = 0
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failed = 0
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for video in videos:
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tracks, is_failed = process_video(video)
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total_tracks += tracks
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if is_failed:
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failed += 1
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processed += 1
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return {"processed": processed, "total_tracks": total_tracks, "failed": failed}
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if __name__ == "__main__":
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print("===== 开始统计人物出现次数 =====")
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result = process_all()
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print(f"→ 处理 {result['processed']} 个视频，共识别出 {result['total_tracks']} 段人物出现")
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print("===== 统计完成 =====")
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