feat(perception): complete E32 full replay
This commit is contained in:
DCCONSTRUCTIONS
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from __future__ import annotations
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from collections import Counter
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import numpy as np
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import pytest
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from k1link.compute.e32_track_geometry_replay import (
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E32TrackGeometryReplayError,
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_comparison_document,
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_CorrectionPlan,
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_require_exact_e29_reproduction,
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_translate_frame,
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)
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from k1link.compute.e32_track_geometry_storage import frame_from_record
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from k1link.compute.semantic_geometry_fusion import (
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CAMERA_GEOMETRY_FRAME_SCHEMA,
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_GeometryClusterSupport,
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_SemanticSupport,
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)
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from k1link.compute.sensor_representation import K1_LIO_PCL_CAPABILITIES
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from k1link.compute.track_geometry import (
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TrackGeometryCurrentness,
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TrackGeometryEvidenceState,
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TrackGeometryMetricBasis,
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TrackGeometrySourceBinding,
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)
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def _binding() -> TrackGeometrySourceBinding:
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return TrackGeometrySourceBinding(
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source_pack_id="e10-lidar-pack-" + "a" * 64,
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source_session_id="source-session",
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representation_profile_id=K1_LIO_PCL_CAPABILITIES.profile_id,
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e31_qualification_id="e31-source-qualification-" + "b" * 64,
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calibration_sha256="c" * 64,
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coordinate_frame="map",
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time_basis="nearest-host-arrival-best-effort",
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selected_offset_ms=0,
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)
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def _semantic(
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*,
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track_id: int,
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label: str,
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status: str,
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indices: list[int],
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bbox: list[float],
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current: bool = True,
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) -> _SemanticSupport:
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return _SemanticSupport(
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document={
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"source_track_id": track_id,
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"track_id": track_id,
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"label": label,
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"association_group": label,
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"score": 0.9,
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"bbox_xyxy": bbox,
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"semantic_current": current,
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"camera_motion_state": "unknown",
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"camera_motion_confidence": None,
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"motion_state": "unknown",
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"motion_status": "unknown",
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"unknown_is_occupied": True,
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"navigation_or_safety_accepted": False,
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"geometry_status": status,
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"geometry_reason": (
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"camera-semantic-with-connected-occupied-lidar-support"
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if status == "agree"
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else (
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"semantic-observation-not-current"
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if not current
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else "camera-semantic-without-qualified-occupied-lidar-support"
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)
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),
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"range_m": 4.0 if status == "agree" else None,
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"occupied_centroid_map_xyz_m": None,
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"occupied_height_range_m": None,
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"support": {
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"projected_points_in_bbox": len(indices),
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"classified_points_in_bbox": len(indices),
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"surface_points_in_bbox": 0,
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"occupied_points_in_bbox": len(indices),
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"below_surface_points_in_bbox": 0,
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"connected_occupied_points": len(indices),
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"connected_occupied_voxels": int(bool(indices)),
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},
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},
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occupied_source_indices=np.asarray(indices, dtype=np.int64),
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)
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def _geometry(indices: list[int], range_m: float) -> _GeometryClusterSupport:
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return _GeometryClusterSupport(
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document={
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"geometry_status": "single-source-geometry",
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"semantic_class": None,
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"point_count": len(indices),
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"voxel_count": 1,
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"centroid_map_xyz_m": [0.0, 0.0, 0.0],
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"bounds_map_xyz_m": [[0.0, 0.0, 0.0], [1.0, 1.0, 1.0]],
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"height_range_m": [0.2, 1.0],
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"nearest_range_m": range_m,
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"unknown_is_occupied": True,
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"navigation_or_safety_accepted": False,
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},
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occupied_source_indices=np.asarray(indices, dtype=np.int64),
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)
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def test_e32_translation_applies_only_bound_corrections_and_closes_point_ownership() -> None:
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points = np.asarray(
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[[float(index), 0.0, 1.0] for index in range(8)],
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dtype=np.float64,
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)
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semantic_supports = (
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_semantic(
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track_id=7,
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label="car",
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status="agree",
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indices=[0, 1],
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bbox=[10.0, 10.0, 100.0, 100.0],
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),
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_semantic(
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track_id=8,
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label="person",
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status="single-source-camera",
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indices=[2],
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bbox=[300.0, 500.0, 340.0, 580.0],
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),
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_semantic(
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track_id=9,
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label="car",
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status="unknown",
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indices=[],
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bbox=[200.0, 100.0, 250.0, 150.0],
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current=False,
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),
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)
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geometry_supports = (
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_geometry([3, 4], 3.0),
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_geometry([5], 4.0),
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_geometry([6, 7], 5.0),
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)
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corrections = _CorrectionPlan(
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semantic_rectangle_normalized_xyxy=(0.30, 0.75, 0.50, 1.0),
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semantic_class_allowlist=frozenset({"person"}),
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image_width=800,
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image_height=600,
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exact_geometry_corrections={(12, 0): "e30-review-item-" + "d" * 64},
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human_geometry_dispositions={
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(12, 1): ("background-or-noise", "e30-review-item-" + "e" * 64),
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(12, 2): ("object-present", "e30-review-item-" + "f" * 64),
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},
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)
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translated = _translate_frame(
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frame_index=12,
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source_frame_index=120,
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session_seconds=42.0,
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source_available=True,
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frame_points=points,
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semantic_supports=semantic_supports,
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geometry_supports=geometry_supports,
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binding=_binding(),
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corrections=corrections,
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last_current_frame={},
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)
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frame = translated.frame
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assert [geometry.owner_key for geometry in frame.geometries] == [
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"track:7",
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"geometry:2",
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]
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assert frame.point_slab.owner_keys == ("track:7", "geometry:2")
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assert frame.point_slab.source_indices.tolist() == [0, 1, 6, 7]
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assert frame.point_slab.owner_indices.tolist() == [0, 0, 1, 1]
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assert frame.geometries[1].reason_codes == (
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"e29-unassociated-occupied-component",
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"a3-human-object-present",
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)
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assert translated.semantic_published == 1
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assert translated.semantic_masked == 1
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assert translated.semantic_unpublishable_held == 1
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assert translated.geometry_published == 1
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assert translated.geometry_exact_excluded == 1
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assert translated.geometry_human_excluded == 1
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assert translated.baseline_qualified_points == 7
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assert translated.published_qualified_points == 4
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assert translated.excluded_qualified_points == 3
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assert translated.ownership_overlap_claims == 0
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assert translated.unqualified_semantic_support_points == 1
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assert [change["reason"] for change in translated.changes] == [
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"e31-semantic-self-mask",
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"held-without-prior-current-provenance",
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"e31-exact-geometry-correction",
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"a3-human-background-or-noise",
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]
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restored = frame_from_record(
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record_value=translated.record,
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binding=_binding(),
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frame_offsets=np.asarray([0] * 13 + [4], dtype="<i8"),
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source_indices=translated.point_source_indices,
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points=translated.point_coordinates,
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owner_indices=translated.point_owner_indices,
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)
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assert restored.to_dict() == translated.frame.to_dict()
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def test_e32_held_track_keeps_prior_current_provenance_without_current_points() -> None:
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held = _semantic(
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track_id=11,
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label="truck",
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status="unknown",
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indices=[],
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bbox=[20.0, 20.0, 60.0, 60.0],
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current=False,
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)
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translated = _translate_frame(
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frame_index=15,
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source_frame_index=150,
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session_seconds=45.0,
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source_available=False,
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frame_points=np.empty((0, 3), dtype=np.float64),
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semantic_supports=(held,),
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geometry_supports=(),
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binding=_binding(),
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corrections=_CorrectionPlan(
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semantic_rectangle_normalized_xyxy=(0.30, 0.75, 0.50, 1.0),
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semantic_class_allowlist=frozenset({"person"}),
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image_width=800,
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image_height=600,
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exact_geometry_corrections={},
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human_geometry_dispositions={},
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),
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last_current_frame={11: 13},
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)
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geometry = translated.frame.geometries[0]
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assert geometry.currentness is TrackGeometryCurrentness.HELD
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assert geometry.evidence_state is TrackGeometryEvidenceState.UNKNOWN
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assert geometry.metric_basis is TrackGeometryMetricBasis.UNAVAILABLE
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assert geometry.held_from_frame_index == 13
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assert translated.frame.point_slab.row_count == 0
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def test_e32_arbitrates_overlapping_camera_claims_without_duplicate_points() -> None:
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larger = _semantic(
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track_id=20,
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label="car",
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status="agree",
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indices=[0, 1],
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bbox=[10.0, 10.0, 100.0, 100.0],
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)
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smaller = _semantic(
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track_id=21,
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label="person",
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status="agree",
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indices=[0, 1],
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bbox=[20.0, 20.0, 40.0, 70.0],
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)
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translated = _translate_frame(
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frame_index=20,
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source_frame_index=200,
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session_seconds=50.0,
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source_available=True,
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frame_points=np.asarray(
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[[1.0, 0.0, 1.0], [2.0, 0.0, 1.0]],
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dtype=np.float64,
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),
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semantic_supports=(larger, smaller),
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geometry_supports=(),
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binding=_binding(),
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corrections=_CorrectionPlan(
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semantic_rectangle_normalized_xyxy=(0.30, 0.75, 0.50, 1.0),
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semantic_class_allowlist=frozenset({"person"}),
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image_width=800,
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image_height=600,
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exact_geometry_corrections={},
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human_geometry_dispositions={},
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),
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last_current_frame={},
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)
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assert translated.frame.point_slab.source_indices.tolist() == [0, 1]
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assert translated.frame.point_slab.owner_keys == ("track:21",)
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assert translated.frame.geometries[0].evidence_state is TrackGeometryEvidenceState.UNKNOWN
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assert translated.frame.geometries[0].reason_codes[-1] == (
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"e32-point-ownership-collision"
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)
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assert translated.frame.geometries[1].evidence_state is TrackGeometryEvidenceState.AGREE
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assert translated.baseline_qualified_points == 4
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assert translated.published_qualified_points == 2
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assert translated.ownership_overlap_claims == 2
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assert translated.excluded_qualified_points == 0
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assert translated.changes[0]["reason"] == "point-ownership-arbitration"
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def test_e32_withholds_unqualified_e29_range_and_retains_camera_state() -> None:
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camera_only = _semantic(
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track_id=30,
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label="car",
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status="single-source-camera",
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indices=[0],
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bbox=[10.0, 10.0, 100.0, 100.0],
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)
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camera_only.document["range_m"] = 6.0
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translated = _translate_frame(
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frame_index=30,
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source_frame_index=300,
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session_seconds=60.0,
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source_available=True,
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frame_points=np.asarray([[1.0, 0.0, 1.0]], dtype=np.float64),
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semantic_supports=(camera_only,),
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geometry_supports=(),
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binding=_binding(),
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corrections=_CorrectionPlan(
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semantic_rectangle_normalized_xyxy=(0.30, 0.75, 0.50, 1.0),
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semantic_class_allowlist=frozenset({"person"}),
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image_width=800,
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image_height=600,
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exact_geometry_corrections={},
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human_geometry_dispositions={},
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),
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last_current_frame={},
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)
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geometry = translated.frame.geometries[0]
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assert geometry.evidence_state is TrackGeometryEvidenceState.CAMERA_ONLY
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assert geometry.metric_basis is TrackGeometryMetricBasis.UNAVAILABLE
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assert geometry.range_m is None
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assert geometry.reason_codes[-1] == "e32-unqualified-range-withheld"
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assert translated.unqualified_ranges_withheld == 1
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assert translated.changes[0]["reason"] == "unqualified-range-withheld"
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def test_e32_replay_rejects_any_e29_reproduction_drift() -> None:
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semantic = _semantic(
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track_id=1,
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label="car",
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status="single-source-camera",
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indices=[],
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bbox=[10.0, 10.0, 20.0, 20.0],
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)
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fusion_frame = {
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"source_frame_index": 10,
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"session_seconds": 3.0,
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}
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baseline = {
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"schema_version": CAMERA_GEOMETRY_FRAME_SCHEMA,
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"frame_index": 0,
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"source_frame_index": 10,
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"session_seconds": 3.0,
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"source_available": False,
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"local_surface_valid": False,
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"semantic_observations": [semantic.document],
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"geometry_only_occupied": [],
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"policy": {
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"camera_owns_semantics": True,
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"lidar_owns_metric_geometry": True,
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"absence_of_points_means_free": False,
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"unknown_is_occupied": True,
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},
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"authority": {
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"commands_enabled": False,
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"navigation_or_safety_accepted": False,
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},
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}
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_require_exact_e29_reproduction(
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e29_frame=baseline,
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frame_index=0,
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fusion_frame=fusion_frame,
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source_available=False,
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surface_valid=False,
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semantic_supports=(semantic,),
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geometry_supports=(),
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)
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baseline["semantic_observations"] = []
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with pytest.raises(E32TrackGeometryReplayError, match="exactly reproduce"):
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_require_exact_e29_reproduction(
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e29_frame=baseline,
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frame_index=0,
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fusion_frame=fusion_frame,
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source_available=False,
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surface_valid=False,
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semantic_supports=(semantic,),
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geometry_supports=(),
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)
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def test_e32_comparison_exposes_status_class_range_scene_and_cause_deltas() -> None:
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baseline = {
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(
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"agree",
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"car",
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"middle",
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"000-060s",
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"connected-support",
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): 2,
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(
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"single-source-geometry",
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"__geometry__",
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"near",
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"000-060s",
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"unassociated",
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): 1,
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}
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current = {
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(
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"agree",
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"car",
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"middle",
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"000-060s",
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"connected-support",
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): 1,
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}
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comparison = _comparison_document(
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Counter(baseline),
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Counter(current),
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)
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assert comparison["by_status"]["agree"] == {
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"e29": 2,
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"e32": 1,
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"delta": -1,
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}
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assert comparison["by_class"]["__geometry__"]["delta"] == -1
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assert comparison["by_range"]["near"]["delta"] == -1
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assert comparison["by_scene"]["000-060s"]["delta"] == -2
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assert comparison["by_cause"]["unassociated"]["delta"] == -1
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