46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
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"""Translate per-track pose facts into state-machine evidence."""
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from typing import Dict
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from v1.evidence import PoseEvidence
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from v1.fall_state import Evidence, FallState
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class FallEvidencePolicy:
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"""Require a recent rapid drop before a horizontal pose becomes a candidate."""
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def __init__(self, suspect_window_seconds: float) -> None:
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self._suspect_window_seconds = float(suspect_window_seconds)
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self._rapid_drop_at: Dict[str, float] = {}
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def evaluate(
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self,
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track_id: str,
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pose_evidence: PoseEvidence,
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now: float,
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state: FallState,
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) -> Evidence:
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timestamp = float(now)
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if not pose_evidence.accepted:
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self._rapid_drop_at.pop(track_id, None)
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return Evidence(accepted=False, is_fall_candidate=False)
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if pose_evidence.rapid_vertical_change:
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self._rapid_drop_at[track_id] = timestamp
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candidate = False
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if state is FallState.SUSPECT:
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candidate = pose_evidence.horizontal_pose
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elif pose_evidence.horizontal_pose and track_id in self._rapid_drop_at:
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candidate = timestamp - self._rapid_drop_at[track_id] <= self._suspect_window_seconds
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recovery = (
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state in (FallState.CONFIRMED, FallState.RECOVERING)
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and not pose_evidence.horizontal_pose
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and not pose_evidence.rapid_vertical_change
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)
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return Evidence(
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accepted=pose_evidence.accepted,
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is_fall_candidate=candidate,
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is_recovery_candidate=recovery,
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)
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