feat(perception): add diagnostic semantic SLAM replay
This commit is contained in:
DCCONSTRUCTIONS
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from __future__ import annotations
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import hashlib
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import json
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import zipfile
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from pathlib import Path
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import numpy as np
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import pytest
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from fastapi import HTTPException
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from fastapi.routing import APIRoute
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from k1link.perception.semantic_slam_replay import (
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PUBLICATION_STATUS,
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SemanticSlamReplayResult,
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)
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from k1link.web import e47_semantic_slam_api as api
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RESULT_ID = f"e47-semantic-slam-{'a' * 64}"
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M4_RESULT_ID = f"m4-threat-replay-{'b' * 64}"
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PNG_0 = b"\x89PNG\r\n\x1a\nsealed-mask-zero"
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PNG_1 = b"\x89PNG\r\n\x1a\nsealed-mask-one"
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def _endpoint(path: str, root: Path):
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router = api.build_e47_semantic_slam_router(root_provider=lambda: root)
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return next(
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route.endpoint
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for route in router.routes
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if isinstance(route, APIRoute) and route.path == path
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)
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@pytest.fixture(autouse=True)
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def _clear_api_caches() -> None:
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api._read_semantic_result_cached.cache_clear()
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api._read_point_ledger_cached.cache_clear()
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yield
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api._read_semantic_result_cached.cache_clear()
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api._read_point_ledger_cached.cache_clear()
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@pytest.fixture
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def publication(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> tuple[Path, Path]:
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root = tmp_path / "semantic-slam-results"
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result_root = root / RESULT_ID
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result_root.mkdir(parents=True)
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taxonomy = {
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"schema_version": "missioncore.e47-semantic-taxonomy/v1",
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"classes": [
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{
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"class_id": 0,
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"label": "ambiguous",
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"disposition": "ambiguous",
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"color_rgb": [0, 0, 0],
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},
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{
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"class_id": 1,
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"label": "road",
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"disposition": "labeled",
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"color_rgb": [128, 64, 128],
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},
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],
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}
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taxonomy_payload = json.dumps(taxonomy, separators=(",", ":")).encode()
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(result_root / "taxonomy.json").write_bytes(taxonomy_payload)
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np.savez(
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result_root / "semantic-points.npz",
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frame_offsets=np.asarray([0, 4, 7], dtype=np.int64),
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point_labels=np.asarray([1, 0, 0, 0, 0, 1, 1], dtype=np.uint8),
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point_status_codes=np.asarray([3, 2, 1, 0, 2, 3, 3], dtype=np.uint8),
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frame_source_point_counts=np.asarray([4, 3], dtype=np.int32),
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frame_labeled_point_counts=np.asarray([1, 2], dtype=np.int32),
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frame_ambiguous_point_counts=np.asarray([1, 1], dtype=np.int32),
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frame_unprojected_point_counts=np.asarray([1, 0], dtype=np.int32),
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frame_absent_point_counts=np.asarray([1, 0], dtype=np.int32),
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)
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with zipfile.ZipFile(result_root / "semantic-masks.zip", mode="w") as archive:
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archive.writestr("semantic-masks/frame-000001.png", PNG_0)
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archive.writestr("semantic-masks/frame-000002.png", PNG_1)
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for name, payload in (
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("manifest.json", b"fixture-manifest"),
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("report.json", b"fixture-report"),
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("semantic-observations.jsonl", b"fixture-observations\n"),
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):
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(result_root / name).write_bytes(payload)
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metrics = {
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"frames": {"total": 2, "mask_available": 2, "source_available": 2},
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"points": {
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"total": 7,
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"projected": 5,
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"labeled": 3,
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"ambiguous": 2,
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"unprojected": 1,
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"absent": 1,
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},
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"observations": {
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"total": 3,
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"labeled": 1,
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"ambiguous": 1,
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"unprojected": 1,
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"absent": 0,
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},
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"runtime": {"elapsed_ms": 10.0, "frames_per_second": 200.0},
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}
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identity = {
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"profile_id": "ravnoves00-eomt-kb4-slam-shadow/v1",
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"base_m4_result_id": M4_RESULT_ID,
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"semantic_result_id": f"result-{'c' * 64}",
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"geometry_result_id": f"m4-geometry-replay-{'d' * 64}",
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"source_pack_id": "ravnoves00-source-pack/v1",
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"calibration_content_sha256": "e" * 64,
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"taxonomy_sha256": hashlib.sha256(taxonomy_payload).hexdigest(),
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"semantic_provider": {
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"provider_id": "eomt-cityscapes-semantic-control/v1",
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"model_id": "tue-mps/eomt",
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"model_revision": "f" * 40,
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"model_weights_sha256": "1" * 64,
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"preprocess_id": "raw-kb4-valid-fov-semantic/v1",
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"role": "fixed-control-not-selected-production-provider",
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},
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"temporal_binding": {
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"semantic_to_camera": "exact-sequence-and-session-time",
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"camera_to_lidar": "accepted-e6-nearest-host-arrival-best-effort",
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"clock_basis": "recorded-host-monotonic-arrival",
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"maximum_lidar_camera_delta_ms": 100.0,
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"maximum_pose_point_delta_ms": 100.0,
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"physical_synchronization_proven": False,
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},
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"authority": {
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"ground_truth": False,
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"semantic_authority": "diagnostic-only",
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"navigation_or_safety_accepted": False,
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"actuation_allowed": False,
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},
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}
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frozen = SemanticSlamReplayResult(
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result_id=RESULT_ID,
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result_root=result_root,
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status=PUBLICATION_STATUS,
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metrics=metrics,
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report={
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"status": PUBLICATION_STATUS,
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"metrics": metrics,
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"limitations": ["No independent semantic truth."],
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},
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manifest={
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"result_id": RESULT_ID,
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"created_at_utc": "2026-08-06T06:30:00.000Z",
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"identity": identity,
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},
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)
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monkeypatch.setattr(api, "_read_semantic_result_cached", lambda *_: frozen)
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return root, result_root
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def test_catalog_projects_exact_diagnostic_only_view(
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publication: tuple[Path, Path],
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) -> None:
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root, _ = publication
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list_results = _endpoint("/api/v1/laboratory/e47-semantic-slam/results", root)
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catalog = list_results(limit=1)
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assert catalog["schema_version"] == "missioncore.e47-semantic-slam-catalog/v1"
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assert catalog["candidate_total"] == 1
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assert catalog["invalid_total"] == 0
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item = catalog["items"][0]
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assert item["schema_version"] == "missioncore.e47-semantic-slam-view/v1"
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assert item["result_id"] == RESULT_ID
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assert item["status"] == "diagnostic-semantic-slam-shadow"
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assert item["base_m4_result_id"] == M4_RESULT_ID
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assert item["provider"]["provider_id"] == "eomt-cityscapes-semantic-control/v1"
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assert item["temporal_binding"]["semantic_to_camera"] == (
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"exact-sequence-and-session-time"
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)
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assert item["temporal_binding"]["physical_synchronization_proven"] is False
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assert [entry["label"] for entry in item["taxonomy"]] == ["ambiguous", "road"]
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assert item["acceptance"] == {
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"artifact_contract_passed": True,
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"frame_accounting_passed": True,
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"point_accounting_passed": True,
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"observation_binding_passed": True,
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"temporal_binding_passed": True,
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"independent_semantic_truth_passed": False,
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"provider_promoted": False,
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}
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assert item["semantic_authority"] == "diagnostic-only"
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assert item["navigation_or_safety_accepted"] is False
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assert item["actuation_allowed"] is False
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def test_timeline_chunk_preserves_point_index_space_and_unavailable_sentinel(
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publication: tuple[Path, Path],
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) -> None:
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root, _ = publication
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get_chunk = _endpoint(
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"/api/v1/laboratory/e47-semantic-slam/results/{result_id}/timeline/chunk",
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root,
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)
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chunk = get_chunk(RESULT_ID, start=0, count=2)
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assert chunk["schema_version"] == "missioncore.e47-semantic-slam-chunk/v1"
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assert chunk["frame_count"] == 2
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assert chunk["next_sequence"] is None
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first = chunk["frames"][0]
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assert first == {
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"schema_version": "missioncore.e47-semantic-slam-frame/v1",
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"sequence": 0,
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"source_point_count": 4,
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"class_ids": [1, 0, -1, -1],
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"status_codes": [3, 2, 1, 0],
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"counts": {"labeled": 1, "ambiguous": 1, "unprojected": 1, "absent": 1},
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}
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assert chunk["frames"][1]["class_ids"] == [0, 1, 1]
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def test_timeline_chunk_rejects_out_of_range_and_oversized_requests(
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publication: tuple[Path, Path],
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) -> None:
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root, _ = publication
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get_chunk = _endpoint(
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"/api/v1/laboratory/e47-semantic-slam/results/{result_id}/timeline/chunk",
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root,
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)
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with pytest.raises(HTTPException) as out_of_range:
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get_chunk(RESULT_ID, start=2, count=1)
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assert out_of_range.value.status_code == 404
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with pytest.raises(HTTPException) as oversized:
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get_chunk(RESULT_ID, start=0, count=25)
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assert oversized.value.status_code == 422
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def test_mask_endpoint_streams_exact_png_with_immutable_identity(
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publication: tuple[Path, Path],
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) -> None:
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root, _ = publication
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get_mask = _endpoint(
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"/api/v1/laboratory/e47-semantic-slam/results/{result_id}/masks/{sequence}",
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root,
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)
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response = get_mask(RESULT_ID, 1)
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assert response.body == PNG_1
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assert response.media_type == "image/png"
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assert response.headers["cache-control"] == "private, max-age=31536000, immutable"
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assert response.headers["etag"] == f'"{hashlib.sha256(PNG_1).hexdigest()}"'
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assert response.headers["x-content-type-options"] == "nosniff"
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def test_result_resolution_rejects_invalid_id_and_symlink(tmp_path: Path) -> None:
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root = tmp_path / "results"
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root.mkdir()
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outside = tmp_path / RESULT_ID
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outside.mkdir()
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(root / RESULT_ID).symlink_to(outside, target_is_directory=True)
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get_chunk = _endpoint(
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"/api/v1/laboratory/e47-semantic-slam/results/{result_id}/timeline/chunk",
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root,
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)
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with pytest.raises(HTTPException) as invalid:
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get_chunk("../escape", start=0, count=1)
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assert invalid.value.status_code == 404
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with pytest.raises(HTTPException) as linked:
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get_chunk(RESULT_ID, start=0, count=1)
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assert linked.value.status_code == 404
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@@ -127,10 +127,11 @@ def test_product_registry_declares_every_advanced_evidence_source() -> None:
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repository_root / "config" / "laboratories"
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)
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assert len(registry.definitions) == 32
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assert len(registry.definitions) == 33
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assert {item.work_id for item in registry.definitions} >= {
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"e31-source-binding",
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"e46j-raw-fisheye-realtime",
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"e47-semantic-slam-shadow",
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"l3-pointpillars-visual-audit",
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"l31-pointpillars-ravnoves",
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"l32-pointpillars-camera-review",
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@@ -94,8 +94,16 @@ def test_repository_registry_classifies_every_evidence_definition() -> None:
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"e33-worker-shadow",
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"e35-degradation-recovery",
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"e46j-raw-fisheye-realtime",
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"e47-semantic-slam-shadow",
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}
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assert all(row.lifecycle == "canonical" for row in execution.definitions)
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by_work_id = {row.work_id: row for row in execution.definitions}
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assert by_work_id["e47-semantic-slam-shadow"].lifecycle == "experimental"
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assert by_work_id["e47-semantic-slam-shadow"].isolation == "bounded-adapter"
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assert all(
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row.lifecycle == "canonical"
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for row in execution.definitions
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if row.work_id != "e47-semantic-slam-shadow"
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)
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assert len(execution.definitions) + len(execution.legacy_work_ids) == len(
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evidence.definitions
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)
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@@ -0,0 +1,306 @@
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from __future__ import annotations
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from dataclasses import replace
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import numpy as np
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import pytest
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from k1link.perception.contracts import (
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EvidenceBasis,
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EvidenceCurrentness,
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MetricGeometry,
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ObstacleObservation,
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)
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from k1link.perception.geometry_math import ProjectedPointCloud
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from k1link.perception.semantic_fusion import (
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NO_SEMANTIC_CLASS_ID,
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SemanticClassDefinition,
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SemanticClassDisposition,
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SemanticEvidenceAuthority,
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SemanticEvidenceStatus,
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SemanticFusionError,
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SemanticMask,
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fuse_semantic_diagnostics,
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)
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def _classes() -> tuple[SemanticClassDefinition, ...]:
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return (
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SemanticClassDefinition(1, "road"),
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SemanticClassDefinition(2, "car"),
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SemanticClassDefinition(
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255,
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"void / uncertain",
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SemanticClassDisposition.AMBIGUOUS,
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),
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)
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def _mask(*, source_id: str = "RAVNOVES00", frame_id: str = "frame-000014") -> SemanticMask:
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return SemanticMask(
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source_id=source_id,
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frame_id=frame_id,
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provider_id="semantic-provider/v1",
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model_id="semantic-model/v1",
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preprocess_id="raw-kb4-semantic/v1",
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labels=np.asarray(
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[
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[1, 2, 255, 1],
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[1, 1, 1, 1],
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[1, 1, 1, 1],
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],
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dtype=np.uint8,
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),
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classes=_classes(),
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)
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def _projection() -> ProjectedPointCloud:
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return ProjectedPointCloud(
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pixels_xy=np.asarray(
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[
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[0.1, 0.1],
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[1.2, 0.2],
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[1.8, 0.8],
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[2.1, 0.2],
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[9.0, 9.0],
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],
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dtype=np.float64,
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),
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depths_m=np.asarray([2.0, 2.1, 2.2, 2.3, 2.4], dtype=np.float64),
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source_indices=np.asarray([0, 1, 2, 3, 4], dtype=np.int64),
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source_point_count=5,
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camera_front_point_count=5,
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)
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def _geometry_observation(
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*point_ids: int,
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observation_id: str = "geometry-observation-1",
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) -> ObstacleObservation:
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return ObstacleObservation(
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observation_id=observation_id,
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occupancy_key=f"occupancy-{observation_id}",
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source_id="RAVNOVES00",
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frame_id="frame-000014",
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evidence_time_ns=14_000_000_000,
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basis=EvidenceBasis.LIDAR,
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currentness=EvidenceCurrentness.CURRENT,
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occupied_support=True,
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source_point_ids=point_ids,
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metric_geometry=MetricGeometry(
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coordinate_frame="map",
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centroid_xyz_m=(2.0, 0.0, 0.5),
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range_m=2.0,
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covariance_diagonal_m2=(0.1, 0.1, 0.1),
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),
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proposal_ids=(),
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semantic_hint=None,
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reason_codes=("qualified-lidar-points",),
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)
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def _camera_only_observation() -> ObstacleObservation:
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return ObstacleObservation(
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observation_id="camera-observation-1",
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occupancy_key="occupancy-camera-observation-1",
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source_id="RAVNOVES00",
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frame_id="frame-000014",
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evidence_time_ns=14_000_000_000,
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basis=EvidenceBasis.CAMERA,
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currentness=EvidenceCurrentness.CURRENT,
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occupied_support=False,
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source_point_ids=(),
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metric_geometry=None,
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proposal_ids=("proposal-1",),
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semantic_hint=None,
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reason_codes=("camera-only",),
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)
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def test_semantic_mask_is_strict_source_bound_uint8_and_immutable() -> None:
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labels = np.asarray([[1, 2]], dtype=np.uint8)
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semantic = SemanticMask(
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source_id="RAVNOVES00",
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frame_id="frame-000014",
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provider_id="semantic-provider/v1",
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model_id="semantic-model/v1",
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preprocess_id="raw-kb4-semantic/v1",
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labels=labels,
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classes=_classes(),
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)
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labels[0, 0] = 2
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assert semantic.labels.tolist() == [[1, 2]]
|
||||
assert semantic.labels.flags.writeable is False
|
||||
with pytest.raises(ValueError):
|
||||
semantic.labels[0, 0] = 2
|
||||
|
||||
with pytest.raises(SemanticFusionError, match="uint8 HxW"):
|
||||
replace(semantic, labels=np.asarray([[1, 2]], dtype=np.int64))
|
||||
with pytest.raises(SemanticFusionError, match="undeclared"):
|
||||
replace(semantic, labels=np.asarray([[1, 7]], dtype=np.uint8))
|
||||
with pytest.raises(SemanticFusionError, match="unique"):
|
||||
replace(
|
||||
semantic,
|
||||
classes=(SemanticClassDefinition(1, "road"), SemanticClassDefinition(1, "other")),
|
||||
)
|
||||
|
||||
|
||||
def test_mask_projection_keeps_absence_ambiguity_and_unprojected_separate() -> None:
|
||||
result = fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(),
|
||||
)
|
||||
labels = result.point_labels
|
||||
assert [labels.status_for(index) for index in range(5)] == [
|
||||
SemanticEvidenceStatus.LABELED,
|
||||
SemanticEvidenceStatus.LABELED,
|
||||
SemanticEvidenceStatus.LABELED,
|
||||
SemanticEvidenceStatus.AMBIGUOUS,
|
||||
SemanticEvidenceStatus.UNPROJECTED,
|
||||
]
|
||||
assert [labels.class_id_for(index) for index in range(5)] == [1, 2, 2, 255, None]
|
||||
assert [labels.label_for(index) for index in range(5)] == [
|
||||
"road",
|
||||
"car",
|
||||
"car",
|
||||
"void / uncertain",
|
||||
None,
|
||||
]
|
||||
assert labels.class_ids.tolist() == [1, 2, 2, 255, NO_SEMANTIC_CLASS_ID]
|
||||
assert labels.class_ids.flags.writeable is False
|
||||
assert labels.status_codes.flags.writeable is False
|
||||
assert result.authority is SemanticEvidenceAuthority.DIAGNOSTIC_ONLY
|
||||
|
||||
|
||||
def test_observation_aggregation_is_detached_from_geometry_and_safety_authority() -> None:
|
||||
observation = _geometry_observation(0, 1, 2, 4)
|
||||
before = observation.to_dict()
|
||||
result = fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(observation,),
|
||||
)
|
||||
evidence = result.observation_evidence[0]
|
||||
assert observation.to_dict() == before
|
||||
assert evidence.observation_id == observation.observation_id
|
||||
assert evidence.occupancy_key == observation.occupancy_identity
|
||||
assert evidence.status is SemanticEvidenceStatus.LABELED
|
||||
assert evidence.dominant_class_id == 2
|
||||
assert evidence.dominant_label == "car"
|
||||
assert evidence.dominant_fraction_of_labeled == pytest.approx(2 / 3)
|
||||
assert evidence.labeled_point_count == 3
|
||||
assert evidence.unprojected_point_count == 1
|
||||
assert evidence.semantic_coverage_fraction == pytest.approx(0.75)
|
||||
assert evidence.authority is SemanticEvidenceAuthority.DIAGNOSTIC_ONLY
|
||||
assert not hasattr(evidence, "occupied_support")
|
||||
assert not hasattr(evidence, "motion")
|
||||
assert not hasattr(evidence, "threat")
|
||||
assert not hasattr(evidence, "actuation_allowed")
|
||||
|
||||
|
||||
def test_tied_or_provider_ambiguous_labels_remain_ambiguous() -> None:
|
||||
tied = _geometry_observation(0, 1, observation_id="geometry-tied")
|
||||
provider_ambiguous = _geometry_observation(3, observation_id="geometry-void")
|
||||
result = fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(tied, provider_ambiguous),
|
||||
)
|
||||
tie_evidence, void_evidence = result.observation_evidence
|
||||
assert tie_evidence.status is SemanticEvidenceStatus.AMBIGUOUS
|
||||
assert tie_evidence.reason_code == "semantic-label-majority-ambiguous"
|
||||
assert tie_evidence.dominant_class_id is None
|
||||
assert {item.label: item.point_count for item in tie_evidence.class_evidence} == {
|
||||
"road": 1,
|
||||
"car": 1,
|
||||
}
|
||||
assert void_evidence.status is SemanticEvidenceStatus.AMBIGUOUS
|
||||
assert void_evidence.reason_code == "semantic-classes-ambiguous"
|
||||
assert void_evidence.ambiguous_point_count == 1
|
||||
assert void_evidence.class_evidence[0].disposition is SemanticClassDisposition.AMBIGUOUS
|
||||
|
||||
mixed = fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(_geometry_observation(1, 3, observation_id="geometry-mixed"),),
|
||||
).observation_evidence[0]
|
||||
assert mixed.status is SemanticEvidenceStatus.AMBIGUOUS
|
||||
assert mixed.reason_code == "semantic-label-majority-ambiguous"
|
||||
assert mixed.dominant_class_id is None
|
||||
|
||||
|
||||
def test_missing_mask_and_pointless_geometry_have_distinct_outcomes() -> None:
|
||||
observation = _geometry_observation(0, 1)
|
||||
absent = fuse_semantic_diagnostics(
|
||||
semantic_mask=None,
|
||||
projected=_projection(),
|
||||
observations=(observation,),
|
||||
)
|
||||
assert absent.mask_available is False
|
||||
assert [absent.point_labels.status_for(index) for index in range(5)] == [
|
||||
SemanticEvidenceStatus.ABSENT
|
||||
] * 5
|
||||
assert absent.observation_evidence[0].status is SemanticEvidenceStatus.ABSENT
|
||||
assert absent.observation_evidence[0].absent_point_count == 2
|
||||
|
||||
unprojected = fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(_camera_only_observation(),),
|
||||
)
|
||||
evidence = unprojected.observation_evidence[0]
|
||||
assert evidence.status is SemanticEvidenceStatus.UNPROJECTED
|
||||
assert evidence.reason_code == "observation-has-no-source-points"
|
||||
assert evidence.source_point_count == 0
|
||||
|
||||
|
||||
def test_fusion_rejects_frame_escape_invalid_point_ids_and_duplicate_ownership() -> None:
|
||||
observation = _geometry_observation(0)
|
||||
with pytest.raises(SemanticFusionError, match="source frame"):
|
||||
fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(frame_id="frame-000015"),
|
||||
projected=_projection(),
|
||||
observations=(observation,),
|
||||
)
|
||||
with pytest.raises(SemanticFusionError, match="outside the source frame"):
|
||||
fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(_geometry_observation(5),),
|
||||
)
|
||||
with pytest.raises(SemanticFusionError, match="duplicate observation ownership"):
|
||||
fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=_projection(),
|
||||
observations=(
|
||||
observation,
|
||||
_geometry_observation(0, observation_id="geometry-observation-2"),
|
||||
),
|
||||
)
|
||||
with pytest.raises(SemanticFusionError, match="escaped their source frame"):
|
||||
fuse_semantic_diagnostics(
|
||||
semantic_mask=None,
|
||||
projected=_projection(),
|
||||
observations=(
|
||||
observation,
|
||||
replace(
|
||||
_geometry_observation(1, observation_id="geometry-observation-2"),
|
||||
frame_id="frame-000015",
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
def test_projection_validator_rejects_malformed_existing_contract_values() -> None:
|
||||
malformed = replace(
|
||||
_projection(),
|
||||
source_indices=np.asarray([0, 1, 2, 3, 5], dtype=np.int64),
|
||||
)
|
||||
with pytest.raises(SemanticFusionError, match="outside the source frame"):
|
||||
fuse_semantic_diagnostics(
|
||||
semantic_mask=_mask(),
|
||||
projected=malformed,
|
||||
observations=(),
|
||||
)
|
||||
@@ -0,0 +1,506 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import hashlib
|
||||
import io
|
||||
import json
|
||||
import tarfile
|
||||
from dataclasses import dataclass, replace
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
from PIL import Image
|
||||
|
||||
import k1link.perception.semantic_slam_replay as replay
|
||||
from k1link.perception.contracts import (
|
||||
EvidenceBasis,
|
||||
EvidenceCurrentness,
|
||||
MetricGeometry,
|
||||
ObstacleObservation,
|
||||
)
|
||||
from k1link.perception.geometry import GeometryFrame, RecordedFrameTemporalBinding
|
||||
from k1link.perception.geometry_math import Kb4ProjectionProfile
|
||||
from k1link.perception.geometry_replay import GeometryReplayResult
|
||||
from k1link.perception.semantic_fusion import (
|
||||
SemanticClassDisposition,
|
||||
SemanticEvidenceStatus,
|
||||
)
|
||||
from k1link.perception.threat_replay import ThreatReplayResult
|
||||
|
||||
|
||||
def _canonical_json(value: object) -> bytes:
|
||||
return json.dumps(
|
||||
value,
|
||||
ensure_ascii=False,
|
||||
sort_keys=True,
|
||||
separators=(",", ":"),
|
||||
allow_nan=False,
|
||||
).encode("utf-8")
|
||||
|
||||
|
||||
def _sha256(path: Path) -> str:
|
||||
return hashlib.sha256(path.read_bytes()).hexdigest()
|
||||
|
||||
|
||||
def _write_jsonl(path: Path, rows: list[dict[str, object]]) -> None:
|
||||
path.write_bytes(b"".join(_canonical_json(row) + b"\n" for row in rows))
|
||||
|
||||
|
||||
def _png(labels: np.ndarray) -> bytes:
|
||||
buffer = io.BytesIO()
|
||||
Image.fromarray(labels, mode="L").save(buffer, format="PNG")
|
||||
return buffer.getvalue()
|
||||
|
||||
|
||||
@dataclass(frozen=True)
|
||||
class _Store:
|
||||
frame: GeometryFrame
|
||||
lidar_delta_ms: float = 4.0
|
||||
pose_delta_ms: float = 2.0
|
||||
|
||||
def frame_for_index(self, frame_index: int) -> GeometryFrame | None:
|
||||
return self.frame if frame_index == 0 else None
|
||||
|
||||
def temporal_binding_for_index(self, frame_index: int) -> RecordedFrameTemporalBinding:
|
||||
return RecordedFrameTemporalBinding(
|
||||
frame_index=frame_index,
|
||||
source_time_ns=(frame_index + 1) * 1_000_000_000,
|
||||
source_available=frame_index == 0,
|
||||
lidar_camera_delta_ms=self.lidar_delta_ms if frame_index == 0 else None,
|
||||
pose_point_delta_ms=self.pose_delta_ms if frame_index == 0 else None,
|
||||
)
|
||||
|
||||
|
||||
def _observation() -> ObstacleObservation:
|
||||
return ObstacleObservation(
|
||||
observation_id="frame-000000:obstacle-0",
|
||||
occupancy_key="frame-000000:obstacle-0",
|
||||
source_id="RAVNOVES00",
|
||||
frame_id="frame-000000",
|
||||
evidence_time_ns=1,
|
||||
basis=EvidenceBasis.FUSED,
|
||||
currentness=EvidenceCurrentness.CURRENT,
|
||||
occupied_support=True,
|
||||
source_point_ids=(0, 1),
|
||||
metric_geometry=MetricGeometry(
|
||||
coordinate_frame="map",
|
||||
centroid_xyz_m=(0.0, 0.0, 1.0),
|
||||
range_m=1.0,
|
||||
covariance_diagonal_m2=(0.0, 0.0, 0.0),
|
||||
),
|
||||
proposal_ids=("proposal-0",),
|
||||
semantic_hint="car",
|
||||
reason_codes=("current-test-support",),
|
||||
)
|
||||
|
||||
|
||||
def _fixture(tmp_path: Path) -> replay._AdmittedInputs:
|
||||
source = tmp_path / "source"
|
||||
source.mkdir()
|
||||
profile_path = source / "profile.json"
|
||||
profile_path.write_text('{"fixture":true}\n', encoding="utf-8")
|
||||
authority = {
|
||||
"ground_truth": False,
|
||||
"physical_live": False,
|
||||
"commands_enabled": False,
|
||||
"actuation_allowed": False,
|
||||
"navigation_or_safety_accepted": False,
|
||||
"semantic_authority": "diagnostic-only",
|
||||
}
|
||||
fusion = {
|
||||
"projection": "factory-kb4-current-increment/v1",
|
||||
"point_index_space": "frame-local-source-point-id/v1",
|
||||
"observation_aggregation": "dominant-labeled-majority-diagnostic/v1",
|
||||
"unprojected_status": "unprojected",
|
||||
"semantic_absence_means_free": False,
|
||||
"semantic_can_create_obstacle": False,
|
||||
"semantic_can_change_identity": False,
|
||||
"semantic_can_change_metric_geometry": False,
|
||||
"semantic_can_change_occupancy": False,
|
||||
"semantic_can_change_motion": False,
|
||||
"semantic_can_change_threat": False,
|
||||
}
|
||||
profile = replay._SemanticSlamProfile(
|
||||
path=profile_path,
|
||||
sha256=_sha256(profile_path),
|
||||
profile_id="fixture-semantic-slam/v1",
|
||||
source_id="RAVNOVES00",
|
||||
session_id="fixture-session",
|
||||
frame_count=2,
|
||||
image_width=4,
|
||||
image_height=4,
|
||||
source_pack_id="fixture-source-pack",
|
||||
source_pack_sha256="1" * 64,
|
||||
calibration_sha256="2" * 64,
|
||||
provider_id="fixture-semantic-provider/v1",
|
||||
model_id="fixture-model",
|
||||
model_revision="fixture-revision",
|
||||
model_weights_sha256="3" * 64,
|
||||
preprocess_id="fixture-preprocess/v1",
|
||||
mask_metadata_schema_version="missioncore.panoptic-frame/v1",
|
||||
mask_payload={
|
||||
"media_type": "image/png",
|
||||
"encoding": "uint8-class-id",
|
||||
"width": 4,
|
||||
"height": 4,
|
||||
"sequence_binding": "sequence-0-to-frame-000001",
|
||||
},
|
||||
provider_role="fixed-control-not-selected-production-provider",
|
||||
classes=(
|
||||
replay._TaxonomyClass(
|
||||
class_id=0,
|
||||
label="outside_valid_fov",
|
||||
disposition=SemanticClassDisposition.AMBIGUOUS,
|
||||
color_rgb=(0, 0, 0),
|
||||
),
|
||||
replay._TaxonomyClass(
|
||||
class_id=4,
|
||||
label="car",
|
||||
disposition=SemanticClassDisposition.LABELED,
|
||||
color_rgb=(0, 0, 142),
|
||||
),
|
||||
),
|
||||
fusion=fusion,
|
||||
temporal_binding={
|
||||
"semantic_to_camera": "exact-sequence-and-session-time",
|
||||
"camera_to_lidar": "accepted-e6-nearest-host-arrival-best-effort",
|
||||
"clock_basis": "recorded-host-monotonic-arrival",
|
||||
"maximum_lidar_camera_delta_ms": 100.0,
|
||||
"maximum_pose_point_delta_ms": 100.0,
|
||||
"physical_synchronization_proven": False,
|
||||
},
|
||||
acceptance={
|
||||
"full_frame_accounting_required": True,
|
||||
"point_accounting_required": True,
|
||||
"observation_binding_required": True,
|
||||
"exact_mask_archive_required": True,
|
||||
"independent_semantic_truth_required_for_provider_promotion": True,
|
||||
},
|
||||
authority=authority,
|
||||
)
|
||||
|
||||
semantic_root = source / "semantic"
|
||||
semantic_root.mkdir()
|
||||
result_json = semantic_root / "result.json"
|
||||
result_json.write_text('{"sealed":"fixture"}\n', encoding="utf-8")
|
||||
masks = []
|
||||
first = np.zeros((4, 4), dtype=np.uint8)
|
||||
first[2, 2] = 4
|
||||
masks.append(_png(first))
|
||||
masks.append(_png(np.zeros((4, 4), dtype=np.uint8)))
|
||||
mask_archive = semantic_root / "masks.tar.gz"
|
||||
with tarfile.open(mask_archive, mode="w:gz") as archive:
|
||||
directory = tarfile.TarInfo("semantic-masks")
|
||||
directory.type = tarfile.DIRTYPE
|
||||
archive.addfile(directory)
|
||||
for sequence, payload in enumerate(masks, start=1):
|
||||
member = tarfile.TarInfo(f"semantic-masks/frame-{sequence:06d}.png")
|
||||
member.size = len(payload)
|
||||
archive.addfile(member, io.BytesIO(payload))
|
||||
semantic_frames = semantic_root / "frames.jsonl"
|
||||
_write_jsonl(
|
||||
semantic_frames,
|
||||
[
|
||||
{
|
||||
"schema_version": "missioncore.panoptic-frame/v1",
|
||||
"frame_index": 0,
|
||||
"sequence": 1,
|
||||
"session_seconds": 1.0,
|
||||
"instances": [],
|
||||
"semantic_classes": [
|
||||
{
|
||||
"id": 4,
|
||||
"label": "car",
|
||||
"pixels": 1,
|
||||
"fraction_of_valid_fov": 0.0625,
|
||||
}
|
||||
],
|
||||
},
|
||||
{
|
||||
"schema_version": "missioncore.panoptic-frame/v1",
|
||||
"frame_index": 1,
|
||||
"sequence": 2,
|
||||
"session_seconds": 2.0,
|
||||
"instances": [],
|
||||
"semantic_classes": [],
|
||||
},
|
||||
],
|
||||
)
|
||||
semantic = replay._SemanticUpstream(
|
||||
result_id="result-" + "4" * 64,
|
||||
result_root=semantic_root,
|
||||
result_manifest_sha256=_sha256(result_json),
|
||||
frames_path=semantic_frames,
|
||||
frames_sha256=_sha256(semantic_frames),
|
||||
masks_path=mask_archive,
|
||||
masks_sha256=_sha256(mask_archive),
|
||||
created_at_utc="2026-08-06T00:00:00.000Z",
|
||||
job_id="fixture-job",
|
||||
input_sha256="5" * 64,
|
||||
source_id="sensor.camera.right",
|
||||
session_id="fixture-session",
|
||||
calibration_sha256="2" * 64,
|
||||
configuration_profile_sha256="6" * 64,
|
||||
model_id="fixture-model",
|
||||
model_revision="fixture-revision",
|
||||
model_weights_sha256="3" * 64,
|
||||
)
|
||||
|
||||
observation = _observation()
|
||||
geometry_root = source / "geometry"
|
||||
geometry_root.mkdir()
|
||||
geometry_frames = geometry_root / "frames.jsonl"
|
||||
_write_jsonl(
|
||||
geometry_frames,
|
||||
[
|
||||
{
|
||||
"schema_version": "missioncore.perception-geometry-replay-frame/v1",
|
||||
"sequence": 0,
|
||||
"frame_id": "frame-000000",
|
||||
"source_available": True,
|
||||
"observations": [observation.to_dict()],
|
||||
},
|
||||
{
|
||||
"schema_version": "missioncore.perception-geometry-replay-frame/v1",
|
||||
"sequence": 1,
|
||||
"frame_id": "frame-000001",
|
||||
"source_available": False,
|
||||
"observations": [],
|
||||
},
|
||||
],
|
||||
)
|
||||
geometry_sha256 = _sha256(geometry_frames)
|
||||
geometry = GeometryReplayResult(
|
||||
result_id="m4-geometry-replay-" + "7" * 64,
|
||||
result_root=geometry_root,
|
||||
accepted=True,
|
||||
metrics={"frames": {"total": 2}},
|
||||
report={},
|
||||
manifest={"identity": {"frames_sha256": geometry_sha256}},
|
||||
)
|
||||
|
||||
threat_root = source / "threat"
|
||||
threat_root.mkdir()
|
||||
threat_frames = threat_root / "frames.jsonl"
|
||||
_write_jsonl(threat_frames, [{"decision": "unchanged"}])
|
||||
threat_sha256 = _sha256(threat_frames)
|
||||
threat = ThreatReplayResult(
|
||||
result_id="m4-threat-replay-" + "8" * 64,
|
||||
result_root=threat_root,
|
||||
accepted=True,
|
||||
metrics={"frames": {"total": 2}},
|
||||
report={},
|
||||
manifest={"identity": {"frames_sha256": threat_sha256}},
|
||||
)
|
||||
|
||||
transform = np.eye(4, dtype=np.float64)
|
||||
frame = GeometryFrame(
|
||||
frame_index=0,
|
||||
points_map=np.asarray(((0.0, 0.0, 1.0), (100.0, 0.0, 1.0)), dtype=np.float64),
|
||||
point_class=np.zeros(2, dtype=np.uint8),
|
||||
sensor_position_map=np.zeros(3, dtype=np.float64),
|
||||
sensor_orientation_xyzw=np.asarray((0.0, 0.0, 0.0, 1.0), dtype=np.float64),
|
||||
projection=Kb4ProjectionProfile(
|
||||
width=4,
|
||||
height=4,
|
||||
intrinsic_fx_fy_cx_cy=(2.0, 2.0, 2.0, 2.0),
|
||||
distortion_kb4=(0.0, 0.0, 0.0, 0.0),
|
||||
t_camera_from_lidar=transform,
|
||||
),
|
||||
surface_valid=True,
|
||||
)
|
||||
return replay._AdmittedInputs(
|
||||
profile=profile,
|
||||
semantic=semantic,
|
||||
geometry=geometry,
|
||||
threat=threat,
|
||||
store=_Store(frame), # type: ignore[arg-type]
|
||||
geometry_frames_path=geometry_frames,
|
||||
geometry_frames_sha256=geometry_sha256,
|
||||
threat_frames_path=threat_frames,
|
||||
threat_frames_sha256=threat_sha256,
|
||||
)
|
||||
|
||||
|
||||
def _build(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> tuple[replay.SemanticSlamReplayResult, replay._AdmittedInputs]:
|
||||
admitted = _fixture(tmp_path)
|
||||
monkeypatch.setattr(replay, "_admit_inputs", lambda **_kwargs: admitted)
|
||||
result = replay.build_semantic_slam_replay(
|
||||
repository_root=tmp_path,
|
||||
semantic_result_root=tmp_path,
|
||||
threat_result_root=tmp_path,
|
||||
geometry_result_root=tmp_path,
|
||||
output_root=tmp_path / "output",
|
||||
)
|
||||
return result, admitted
|
||||
|
||||
|
||||
def test_builder_is_idempotent_and_preserves_geometry_and_threat_ledgers(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
admitted = _fixture(tmp_path)
|
||||
geometry_before = admitted.geometry_frames_path.read_bytes()
|
||||
threat_before = admitted.threat_frames_path.read_bytes()
|
||||
monkeypatch.setattr(replay, "_admit_inputs", lambda **_kwargs: admitted)
|
||||
arguments = {
|
||||
"repository_root": tmp_path,
|
||||
"semantic_result_root": tmp_path,
|
||||
"threat_result_root": tmp_path,
|
||||
"geometry_result_root": tmp_path,
|
||||
"output_root": tmp_path / "output",
|
||||
}
|
||||
first = replay.build_semantic_slam_replay(**arguments)
|
||||
manifest_before = (first.result_root / replay.SEMANTIC_SLAM_MANIFEST_NAME).read_bytes()
|
||||
second = replay.build_semantic_slam_replay(**arguments)
|
||||
|
||||
assert second.result_id == first.result_id
|
||||
assert (second.result_root / replay.SEMANTIC_SLAM_MANIFEST_NAME).read_bytes() == manifest_before
|
||||
assert admitted.geometry_frames_path.read_bytes() == geometry_before
|
||||
assert admitted.threat_frames_path.read_bytes() == threat_before
|
||||
identity = first.manifest["identity"]
|
||||
assert identity["geometry_frames_sha256"] == hashlib.sha256(geometry_before).hexdigest()
|
||||
assert identity["base_m4_frames_sha256"] == hashlib.sha256(threat_before).hexdigest()
|
||||
|
||||
|
||||
def test_unprojected_source_point_is_uint8_zero_with_explicit_status(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
result, _ = _build(tmp_path, monkeypatch)
|
||||
with np.load(
|
||||
result.result_root / replay.SEMANTIC_SLAM_POINTS_NAME,
|
||||
allow_pickle=False,
|
||||
) as archive:
|
||||
assert archive["point_labels"].dtype == np.uint8
|
||||
assert archive["point_labels"].tolist() == [4, 0]
|
||||
assert archive["point_status_codes"].tolist() == [
|
||||
int(SemanticEvidenceStatus.LABELED),
|
||||
int(SemanticEvidenceStatus.UNPROJECTED),
|
||||
]
|
||||
assert archive["point_projected"].tolist() == [1, 0]
|
||||
|
||||
rows = [
|
||||
json.loads(line)
|
||||
for line in (result.result_root / replay.SEMANTIC_SLAM_OBSERVATIONS_NAME)
|
||||
.read_text("utf-8")
|
||||
.splitlines()
|
||||
]
|
||||
assert rows[0]["observations"][0]["observation_id"] == _observation().observation_id
|
||||
assert rows[0]["observations"][0]["status"] == "labeled"
|
||||
assert rows[0]["observations"][0]["unprojected_point_count"] == 1
|
||||
assert "threat" not in rows[0]["observations"][0]
|
||||
assert rows[0]["source_time_ns"] == 1_000_000_000
|
||||
assert rows[0]["temporal_binding"] == {
|
||||
"semantic_to_camera": "exact-sequence-and-session-time",
|
||||
"camera_to_lidar": "accepted-e6-nearest-host-arrival-best-effort",
|
||||
"lidar_camera_delta_ms": 4.0,
|
||||
"pose_point_delta_ms": 2.0,
|
||||
"physical_synchronization_proven": False,
|
||||
}
|
||||
assert rows[1]["temporal_binding"]["lidar_camera_delta_ms"] is None
|
||||
|
||||
|
||||
def test_reader_rejects_tampered_point_artifact(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
result, _ = _build(tmp_path, monkeypatch)
|
||||
points = result.result_root / replay.SEMANTIC_SLAM_POINTS_NAME
|
||||
points.write_bytes(points.read_bytes() + b"tamper")
|
||||
|
||||
with pytest.raises(replay.SemanticSlamReplayError, match="digest changed"):
|
||||
replay.read_semantic_slam_replay_result(result.result_root)
|
||||
|
||||
|
||||
def test_builder_rejects_semantic_and_source_pack_session_time_mismatch(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
admitted = _fixture(tmp_path)
|
||||
rows = [
|
||||
json.loads(line) for line in admitted.semantic.frames_path.read_text("utf-8").splitlines()
|
||||
]
|
||||
rows[1]["session_seconds"] = 2.001
|
||||
_write_jsonl(admitted.semantic.frames_path, rows)
|
||||
semantic = replace(
|
||||
admitted.semantic,
|
||||
frames_sha256=_sha256(admitted.semantic.frames_path),
|
||||
)
|
||||
monkeypatch.setattr(
|
||||
replay,
|
||||
"_admit_inputs",
|
||||
lambda **_kwargs: replace(admitted, semantic=semantic),
|
||||
)
|
||||
|
||||
with pytest.raises(replay.SemanticSlamReplayError, match="session time disagree"):
|
||||
replay.build_semantic_slam_replay(
|
||||
repository_root=tmp_path,
|
||||
semantic_result_root=tmp_path,
|
||||
threat_result_root=tmp_path,
|
||||
geometry_result_root=tmp_path,
|
||||
output_root=tmp_path / "output",
|
||||
)
|
||||
|
||||
|
||||
def test_builder_rejects_best_effort_delta_outside_admitted_bound(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
admitted = _fixture(tmp_path)
|
||||
frame = admitted.store.frame_for_index(0)
|
||||
assert frame is not None
|
||||
monkeypatch.setattr(
|
||||
replay,
|
||||
"_admit_inputs",
|
||||
lambda **_kwargs: replace(
|
||||
admitted,
|
||||
store=_Store(frame, lidar_delta_ms=100.001), # type: ignore[arg-type]
|
||||
),
|
||||
)
|
||||
|
||||
with pytest.raises(replay.SemanticSlamReplayError, match="delta exceeds"):
|
||||
replay.build_semantic_slam_replay(
|
||||
repository_root=tmp_path,
|
||||
semantic_result_root=tmp_path,
|
||||
threat_result_root=tmp_path,
|
||||
geometry_result_root=tmp_path,
|
||||
output_root=tmp_path / "output",
|
||||
)
|
||||
|
||||
|
||||
def test_reader_rejects_leaf_result_symlink(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
result, _ = _build(tmp_path, monkeypatch)
|
||||
alias_parent = tmp_path / "alias"
|
||||
alias_parent.mkdir()
|
||||
alias = alias_parent / result.result_id
|
||||
alias.symlink_to(result.result_root, target_is_directory=True)
|
||||
|
||||
with pytest.raises(replay.SemanticSlamReplayError, match="result root is invalid"):
|
||||
replay.read_semantic_slam_replay_result(alias)
|
||||
|
||||
|
||||
def test_builder_rejects_existing_destination_symlink(
|
||||
tmp_path: Path,
|
||||
monkeypatch: pytest.MonkeyPatch,
|
||||
) -> None:
|
||||
result, admitted = _build(tmp_path, monkeypatch)
|
||||
monkeypatch.setattr(replay, "_admit_inputs", lambda **_kwargs: admitted)
|
||||
second_output = tmp_path / "second-output"
|
||||
second_output.mkdir()
|
||||
(second_output / result.result_id).symlink_to(result.result_root, target_is_directory=True)
|
||||
|
||||
with pytest.raises(replay.SemanticSlamReplayError, match="destination cannot be a symlink"):
|
||||
replay.build_semantic_slam_replay(
|
||||
repository_root=tmp_path,
|
||||
semantic_result_root=tmp_path,
|
||||
threat_result_root=tmp_path,
|
||||
geometry_result_root=tmp_path,
|
||||
output_root=second_output,
|
||||
)
|
||||
Reference in New Issue
Block a user