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 person_tracker 已确认"有人"的时间段内跑 MediaPipe Pose 关键点估计，
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用确定性规则引擎判断是否摔倒，写入 events 表（event_type='fall'）。
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不用VLM做摔倒检测：这是安全信号，需要可解释、可调阈值、宁可多报不漏报的确定性
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规则，而不是模型的"感觉"。规则：短时间内从"直立"骤变为"横躺"，且"横躺"状态
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持续一段时间——单独判断"是否躺平"分不清摔倒和弯腰/坐下，必须叠加"骤变"和
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"持续"两个时间条件。
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"""
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import json
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import math
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from datetime import datetime, timedelta
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import cv2
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import mediapipe as mp
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import numpy as np
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from mediapipe.tasks import python as mp_python
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from mediapipe.tasks.python import vision
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from analysis.config import (
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FALL_MODEL_PATH,
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FALL_SAMPLE_STRIDE,
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FALL_MIN_VISIBILITY,
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FALL_ASPECT_RATIO_THRESH,
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FALL_TORSO_ANGLE_UPRIGHT,
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FALL_TORSO_ANGLE_PRONE,
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FALL_TRANSITION_MAX_SEC,
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FALL_SUSTAIN_MIN_SEC,
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FALL_GAP_TOLERANCE_SEC,
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PROJECT_ROOT,
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)
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from analysis.db import get_all_videos, get_events, insert_event, update_video_status
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LEFT_SHOULDER, RIGHT_SHOULDER = 11, 12
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LEFT_HIP, RIGHT_HIP = 23, 24
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def _make_landmarker():
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base_options = mp_python.BaseOptions(model_asset_path=str(FALL_MODEL_PATH))
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options = vision.PoseLandmarkerOptions(
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base_options=base_options,
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running_mode=vision.RunningMode.VIDEO,
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num_poses=1,
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)
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return vision.PoseLandmarker.create_from_options(options)
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def _classify_pose(landmarks, width, height):
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"""返回 'upright' / 'prone' / 'unknown'（关键点不足或角度介于两者之间）。"""
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visible = [lm for lm in landmarks if lm.visibility >= FALL_MIN_VISIBILITY]
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if len(visible) < 4:
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return "unknown"
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xs = [lm.x * width for lm in visible]
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ys = [lm.y * height for lm in visible]
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bbox_w = max(xs) - min(xs)
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bbox_h = max(ys) - min(ys)
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if bbox_h <= 0:
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return "unknown"
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aspect_ratio = bbox_w / bbox_h
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shoulders = [landmarks[i] for i in (LEFT_SHOULDER, RIGHT_SHOULDER) if landmarks[i].visibility >= FALL_MIN_VISIBILITY]
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hips = [landmarks[i] for i in (LEFT_HIP, RIGHT_HIP) if landmarks[i].visibility >= FALL_MIN_VISIBILITY]
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if not shoulders or not hips:
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return "unknown"
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shoulder_mid = (sum(lm.x for lm in shoulders) / len(shoulders) * width,
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sum(lm.y for lm in shoulders) / len(shoulders) * height)
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hip_mid = (sum(lm.x for lm in hips) / len(hips) * width,
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sum(lm.y for lm in hips) / len(hips) * height)
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dx = abs(hip_mid[0] - shoulder_mid[0])
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dy = abs(hip_mid[1] - shoulder_mid[1])
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torso_angle = math.degrees(math.atan2(dx, dy + 1e-6)) # 0°=垂直直立，90°=水平躺平
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if aspect_ratio >= FALL_ASPECT_RATIO_THRESH and torso_angle >= FALL_TORSO_ANGLE_PRONE:
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return "prone"
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if aspect_ratio < 1.0 and torso_angle <= FALL_TORSO_ANGLE_UPRIGHT:
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return "upright"
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return "unknown"
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def _extract_pose_states(landmarker, cap, fps, width, height, start_sec, end_sec):
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"""在给定时间段内采样跑姿态估计，返回 [(t, state), ...]。"""
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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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states = []
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frame_idx = start_frame
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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) % FALL_SAMPLE_STRIDE == 0:
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t = frame_idx / fps
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rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=np.ascontiguousarray(rgb))
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result = landmarker.detect_for_video(mp_image, int(t * 1000))
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if result.pose_landmarks:
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state = _classify_pose(result.pose_landmarks[0], width, height)
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else:
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state = "unknown"
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states.append((t, state))
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frame_idx += 1
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return states
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def _find_fall_windows(states):
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"""从时序姿态状态里找出"直立→骤变→横躺并持续"的片段，返回 [(start_sec, end_sec), ...]。
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用"疑似摔倒事件"（episode）建模：只有真正观察到"直立"样本才会结束当前事件的计时；
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事件进行期间即使长时间检测不到姿态（真实摔倒后人体角度/遮挡经常导致关键点大面积
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漏检，不代表已经爬起来），也继续算同一次事件，直到确认恢复直立，或者长时间
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（超过 FALL_GAP_TOLERANCE_SEC）连"横躺"样本都等不到才判定事件已经结束/跟丢。
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"""
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windows = []
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last_upright_t = None
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episode_start = None
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episode_last_prone = None
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def close_episode():
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nonlocal episode_start, episode_last_prone
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if episode_start is not None and (episode_last_prone - episode_start) >= FALL_SUSTAIN_MIN_SEC:
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windows.append((episode_start, episode_last_prone))
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episode_start = None
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episode_last_prone = None
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for t, state in states:
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if state == "upright":
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close_episode()
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last_upright_t = t
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elif state == "prone":
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if episode_start is None:
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# 只有紧接在"直立"之后、且间隔够短，才算一次骤变
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if last_upright_t is not None and (t - last_upright_t) <= FALL_TRANSITION_MAX_SEC:
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episode_start = t
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episode_last_prone = t
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# 否则忽略：不是从直立状态骤变而来（比如视频开头就是横躺，或者是缓慢躺下）
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elif (t - episode_last_prone) <= FALL_GAP_TOLERANCE_SEC:
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episode_last_prone = t
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else:
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# 距离上一次确认"横躺"太久，且中途也没等到"直立"信号，视为跟丢，结算当前事件
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close_episode()
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# state == "unknown" 不改变状态机，只是跳过这一帧（容忍短暂遮挡/漏检）
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close_episode()
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return windows
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def _merge_windows(events):
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"""合并人物track的时间区间（有重叠或紧邻就合并成一段）。
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同一个人有时会被 ByteTrack 拆成多个track_id（跟踪中途丢失又重新编号），
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这些track的时间区间往往高度重叠——如果按track各自跑一遍姿态估计，
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同一次摔倒会被重复检测、重复插入，所以要先合并再处理，保证同一段画面只处理一次。
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"""
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spans = sorted((e["start_offset_sec"], e["end_offset_sec"]) for e in events)
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merged = []
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for start, end in spans:
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if merged and start <= merged[-1][1]:
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merged[-1] = (merged[-1][0], max(merged[-1][1], end))
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else:
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merged.append((start, end))
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return merged
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def process_video(video, person_events):
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if not person_events:
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update_video_status(video["id"], "fall_done")
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return 0
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cap = cv2.VideoCapture(str(PROJECT_ROOT / video["file_path"]))
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if not cap.isOpened():
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update_video_status(video["id"], "fall_done")
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return 0
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fps = video["fps"] or 15.0
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width, height = video["width"], video["height"]
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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
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fall_count = 0
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# landmarker 实例按需复用：VIDEO 模式要求时间戳在同一实例内严格递增，只有当下一个
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# 事件的起始时间会比当前实例已处理到的时间更早（多人同时/交替出现导致事件时间重叠）
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# 时才换新实例。之前"每个事件都新建一个实例"的写法在批量跑千级视频时会持续新建
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# GPU/Metal 上下文，资源迟迟未完全释放，最终在某个看似无关的帧读取操作上引发底层崩溃
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landmarker = _make_landmarker()
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last_t = -1.0
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try:
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# 先合并所有track的时间区间再处理：同一个人有时会被拆成多个重叠的track_id，
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# 按原始track各自跑一遍会导致同一次摔倒被重复检测、重复插入
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for merged_start, merged_end in _merge_windows(person_events):
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start_frame_t = int(merged_start * fps) / fps
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if start_frame_t <= last_t:
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landmarker.close()
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landmarker = _make_landmarker()
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states = _extract_pose_states(
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landmarker, cap, fps, width, height,
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merged_start, merged_end,
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)
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if states:
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last_t = states[-1][0]
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for start_sec, end_sec in _find_fall_windows(states):
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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": "fall",
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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": None,
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"track_id": None,
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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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fall_count += 1
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except Exception as e:
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# 兜底：单条视频处理异常不应中断整个批次，标记失败并继续下一条。
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# 异常本身可能就是底层解码/GPU上下文已经出问题，close()也可能再报错，不能让它盖过原始异常
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try:
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landmarker.close()
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except Exception:
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pass
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cap.release()
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update_video_status(video["id"], "fall_failed", error_message=str(e))
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return fall_count
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landmarker.close()
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cap.release()
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update_video_status(video["id"], "fall_done")
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return fall_count
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def process_all(limit=None):
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videos = get_all_videos(status="person_done")
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if limit:
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videos = videos[:limit]
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events_by_video = {}
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for e in get_events("person_presence"):
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events_by_video.setdefault(e["video_id"], []).append(e)
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processed = 0
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total_falls = 0
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for video in videos:
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total_falls += process_video(video, events_by_video.get(video["id"], []))
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processed += 1
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return {"processed": processed, "total_falls": total_falls}
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if __name__ == "__main__":
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print("===== 开始检测摔倒事件 =====")
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result = process_all()
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print(f"→ 处理 {result['processed']} 个视频，检测到 {result['total_falls']} 次疑似摔倒")
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print("===== 检测完成 =====")
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