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
2026-09-18 15:52:49 +08:00
parent 203cca42a2
commit b3608f534e
31 changed files with 2792 additions and 27 deletions
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+"""MOG2背景建模检测每个视频里"有活动"的时间窗口，写回 videos.motion_windows_json。
+
+长视频不能逐帧跑重模型，所以先用轻量背景建模预筛出活动窗口，后续 person_tracker /
+fall_detector 只在窗口内跑，空场景直接跳过。
+
+之前用固定阈值帧差法（相邻两帧像素差），对"人物占画面小、动作幅度小"的场景
+（如老人在客厅整理东西，移动缓慢、遮挡画面比例低）容易漏检。改用 MOG2
+自适应背景建模：背景是随时间缓慢更新的统计模型而非"上一帧"，前景掩码对
+慢速/小幅度运动更敏感，且能自动适应光照的缓慢变化。
+"""
+import json
+from datetime import datetime
+
+import cv2
+
+from analysis.config import (
+    MOTION_RESIZE_WIDTH,
+    MOTION_HISTORY,
+    MOTION_VAR_THRESHOLD,
+    MOTION_WARMUP_FRAMES,
+    MOTION_SHORT_CLIP_SEC,
+    MOTION_PIXEL_RATIO,
+    MOTION_MERGE_GAP_SEC,
+    MOTION_MIN_DURATION_SEC,
+    MOTION_SAMPLE_STRIDE,
+    PROJECT_ROOT,
+)
+from analysis.db import get_all_videos, update_motion_windows
+
+
+def detect_motion_windows(file_path, duration_sec=None):
+    """返回该视频里的活动时间窗口列表 [[start_sec, end_sec], ...]。
+
+    duration_sec 由调用方传入（video_scanner用ffprobe测得，比cv2的帧数估算更准）；
+    不传时退化为用 cv2 的帧数/帧率估算，供独立调用/测试使用。
+    """
+    cap = cv2.VideoCapture(str(file_path))
+    if not cap.isOpened():
+        return []
+
+    fps = cap.get(cv2.CAP_PROP_FPS) or 15.0
+
+    if duration_sec is None:
+        frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
+        duration_sec = frame_count / fps if frame_count > 0 else None
+
+    # 触发即录的极短告警片段，帧数不够 MOG2 预热收敛，直接整段视为活动窗口
+    if duration_sec is not None and duration_sec < MOTION_SHORT_CLIP_SEC:
+        cap.release()
+        return [[0.0, duration_sec]]
+
+    subtractor = cv2.createBackgroundSubtractorMOG2(
+        history=MOTION_HISTORY, varThreshold=MOTION_VAR_THRESHOLD, detectShadows=True
+    )
+    motion_flags = []
+    frame_idx = 0
+    sample_idx = 0
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if frame_idx % MOTION_SAMPLE_STRIDE != 0:
+            frame_idx += 1
+            continue
+
+        h, w = frame.shape[:2]
+        scale = MOTION_RESIZE_WIDTH / w
+        small = cv2.resize(frame, (MOTION_RESIZE_WIDTH, max(1, int(h * scale))))
+        small = cv2.GaussianBlur(small, (5, 5), 0)
+
+        fg_mask = subtractor.apply(small)
+        # MOG2 用 127 标记阴影、255 标记前景，阴影不算真实活动，只统计255
+        foreground_ratio = (fg_mask == 255).sum() / fg_mask.size
+
+        if sample_idx >= MOTION_WARMUP_FRAMES:
+            motion_flags.append((frame_idx / fps, foreground_ratio > MOTION_PIXEL_RATIO))
+
+        frame_idx += 1
+        sample_idx += 1
+
+    cap.release()
+    return merge_motion_windows(motion_flags)
+
+
+def merge_motion_windows(motion_flags):
+    windows = []
+    window_start = None
+    window_last_motion = None
+
+    for t, is_motion in motion_flags:
+        if is_motion:
+            if window_start is None:
+                window_start = t
+            window_last_motion = t
+        elif window_start is not None and (t - window_last_motion) > MOTION_MERGE_GAP_SEC:
+            windows.append([window_start, window_last_motion])
+            window_start = None
+            window_last_motion = None
+
+    if window_start is not None:
+        windows.append([window_start, window_last_motion])
+
+    return [w for w in windows if (w[1] - w[0]) >= MOTION_MIN_DURATION_SEC]
+
+
+def process_all(limit=None):
+    videos = get_all_videos(status="pending")
+    if limit:
+        videos = videos[:limit]
+
+    processed = 0
+    no_motion = 0
+    failed = 0
+    for video in videos:
+        try:
+            file_path = PROJECT_ROOT / video["file_path"]
+            if not file_path.exists():
+                update_video_status(video["id"], "motion_failed", error_message="文件不存在")
+                failed += 1
+                processed += 1
+                continue
+            windows = detect_motion_windows(file_path, duration_sec=video["duration_sec"])
+            update_motion_windows(
+                video["id"],
+                json.dumps(windows),
+                status="motion_done",
+                analyzed_at=datetime.now().isoformat(),
+            )
+            processed += 1
+            if not windows:
+                no_motion += 1
+        except Exception as e:
+            update_video_status(video["id"], "motion_failed", error_message=str(e))
+            failed += 1
+            processed += 1
+
+    return {"processed": processed, "no_motion": no_motion, "failed": failed}
+
+
+if __name__ == "__main__":
+    print("===== 开始检测活动窗口 =====")
+    result = process_all()
+    print(f"→ 处理 {result['processed']} 个视频，其中 {result['no_motion']} 个未检测到活动")
+    print("===== 检测完成 =====")