move_detect/edge/detectors/fall.py
유창욱 9386dfc580 feat(delivery): install-path fixes + cafe24 package + delivery-audit hardening
Three intertwined bodies of work developed and tested together this session.
Full test suite green: python (edge) + phpunit (cloud).

## Edge/Pi install path
- setup_online.sh: apt-lock retry (apt-daily contention), Type=exec unit +
  StartLimitIntervalSec in [Unit], journalctl-on-failure, crash-loop detection.
- onnxruntime is now best-effort (no sdist / no 32-bit-ARM wheel); [6/8] probes the
  real backend and hard-fails if none works.
- Hailo HEF auto-fetch: edge/tools/fetch_hailo_hef.py (pinned sha256, refuses to guess
  8 vs 8L, HAILO_ARCH override for serial-less boards); hailo_pose.py run() timeout fix.
- install_offline.sh: mirrors the backend gate; onnxruntime optional.

## Cafe24 cloud package (cloud/deploy/)
- build_cafe24_package.sh emits move_detect_app/ (private, one folder) + www/move_detect/
  (docroot subdir); no-dev classmap autoloader; preflight.php; hardened .htaccess.
- Subdirectory mount support: Request::baseFrom/pathFor strip the mount prefix; index.php
  walks up to find the app root; views/JS prefix every URL with APP_BASE; session cookie
  scoped to the mount.

## Delivery audit — silent-failure & correctness (docs/delivery-audit.md)
All 4 blockers + all Phase-1 silent-failure + Phase-2 HIGH correctness:
- Edge cloud clients send a custom User-Agent (cafe24 WAF blocks python-requests) and
  allow_redirects=False.
- Daemon fails loud on blank CAFE24_URL/DEVICE_SECRET; run_loop logs swallowed exceptions;
  heartbeat carries real fps + per-camera health; UvcSource reopens a dead camera;
  offline queue is size-capped and drops poison rows; TARGET_FPS/QUEUE_DB validated.
- Zone coordinates normalized 0..1 on both sides (were canvas-px vs camera-px — every zone
  misplaced); pinch zones emit PINCH/HIGH (were downgraded to WARNING).
- Fall detection uses bbox cues when keypoints are low-confidence (occluded fallen person);
  ghost incident not cancelled by a bystander; displacement normalized per-second (FPS-invariant).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_019Xmn8KZWqiQUguEMRtn6rW
2026-07-17 09:07:33 +09:00

113 lines
4.7 KiB
Python

from dataclasses import dataclass
from edge.tracking import Track
from edge.geometry import (
torso_angle_deg, head_below_hips, core_visible, visible_count,
track_displacement,
)
@dataclass
class FallConfig:
falling_angle: float = 45.0
fallen_angle: float = 70.0
aspect_ratio: float = 1.25
height_collapse: float = 0.35 # baseline 대비 남은 높이 비율(이 값 미만이면 급붕괴)
prone_confirm_s: float = 3.0
no_motion_s: float = 10.0
motion_thr: float = 0.02 # 정규화 관절변위 임계
min_conf: float = 0.5
min_visible: int = 10
recover_grace_s: float = 1.0 # 사고 상태에서 회복 판정까지 요구되는 지속 비-prone 시간
@dataclass
class _State:
name: str = "STANDING"
baseline_height: float | None = None
prone_since: float | None = None
still_since: float | None = None
fall_emitted: bool = False
no_motion_emitted: bool = False
not_prone_since: float | None = None
class FallDetector:
def __init__(self, cfg: FallConfig):
self.cfg = cfg
self._states: dict[int, _State] = {}
def forget(self, track_id: int) -> None:
self._states.pop(track_id, None)
def _prone(self, kps, bbox, st: _State, kp_ok: bool) -> tuple[bool, dict]:
ar = bbox.aspect
remaining = 1.0
if st.baseline_height:
remaining = bbox.height / st.baseline_height
# bbox cues need NO keypoints — a fallen person is wide-and-short, or their bbox height
# collapsed. These stay live even when the pose model has lost confidence (occluded /
# on the floor), which is exactly when a fall must not be missed.
prone = ar > self.cfg.aspect_ratio or remaining < self.cfg.height_collapse
angle = torso_angle_deg(kps)
# keypoint cues only when the keypoints are actually trustworthy, else they are noise.
if kp_ok:
prone = prone or angle > self.cfg.fallen_angle or head_below_hips(kps)
return prone, {"torso_angle": round(angle, 1),
"aspect_ratio": round(ar, 2),
"height_remaining": round(remaining, 2)}
def update(self, track: Track, now: float) -> tuple[str | None, dict]:
st = self._states.setdefault(track.track_id, _State())
f = track.latest
kps, bbox = f.keypoints, f.bbox
# Confidence gate — no longer a blanket early-return (that disabled fall/no_motion/
# ghost for the occluded person on the ground). It now only decides whether the
# KEYPOINT-based cues are trustworthy; the bbox cues always run.
kp_ok = core_visible(kps, self.cfg.min_conf) and visible_count(kps) >= self.cfg.min_visible
prone, meta = self._prone(kps, bbox, st, kp_ok)
# incident 비활성일 때만 baseline(정상 키높이) 갱신 = incident 중 freeze. Only trust a
# confident standing frame for the baseline so a bad low-conf bbox can't corrupt it.
if st.name == "STANDING" and not prone and kp_ok:
st.baseline_height = bbox.height
if not prone:
if st.name in ("FALLING", "FALLEN", "NO_MOTION"):
# 단일 튐(noisy) 프레임만으로 CRITICAL 사고를 즉시 취소하지 않도록
# recover_grace_s만큼 지속된 비-prone을 요구한다.
if st.not_prone_since is None:
st.not_prone_since = now
return None, {}
if now - st.not_prone_since >= self.cfg.recover_grace_s:
self._states[track.track_id] = _State(baseline_height=bbox.height)
return "recover", {}
return None, {}
st.prone_since = None
return None, {}
# prone 상태 (재-prone 프레임은 대기 중인 recover 유예를 취소)
st.not_prone_since = None
if st.prone_since is None:
st.prone_since = now
st.name = "FALLING"
if now - st.prone_since >= self.cfg.prone_confirm_s and not st.fall_emitted:
st.name = "FALLEN"
st.fall_emitted = True
st.still_since = now
return "fall", meta
# FALLEN 이후 미동 감시
if st.fall_emitted:
if track_displacement(track) < self.cfg.motion_thr:
if st.still_since is None:
st.still_since = now
elif now - st.still_since >= self.cfg.no_motion_s and not st.no_motion_emitted:
st.name = "NO_MOTION"
st.no_motion_emitted = True
return "no_motion", {**meta, "immobility_s": round(now - st.still_since, 1)}
else:
st.still_since = None
return None, {}