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