feat(perception): project recorded results into Rerun
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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from pathlib import Path
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from typing import Any
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import pytest
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import k1link.compute.results as result_module
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from k1link.compute import (
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RecordedPerceptionOverlayStore,
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prepare_camera_compute_job,
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validate_recorded_perception_result,
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)
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from k1link.sessions import SessionIntegrityError
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from k1link.web.camera_archive import CameraArchiveWriter
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def _canonical_json(value: object) -> bytes:
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return json.dumps(
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value,
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ensure_ascii=False,
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sort_keys=True,
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separators=(",", ":"),
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allow_nan=False,
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).encode("utf-8")
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def _job(tmp_path: Path) -> Any:
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origin_epoch_ns = 1_000_000_000
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origin_monotonic_ns = 2_000_000_000
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session = tmp_path / "session-1"
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session.mkdir()
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def box(box_type: bytes, payload: bytes = b"") -> bytes:
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return (8 + len(payload)).to_bytes(4, "big") + box_type + payload
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def full_box(box_type: bytes, payload: bytes = b"", *, flags: int = 0) -> bytes:
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return box(box_type, bytes([0]) + flags.to_bytes(3, "big") + payload)
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track_id = 1
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tkhd = full_box(b"tkhd", b"\x00" * 8 + track_id.to_bytes(4, "big") + b"\x00" * 4)
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mdhd = full_box(
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b"mdhd",
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b"\x00" * 8 + (1_000).to_bytes(4, "big") + b"\x00" * 4,
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)
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hdlr = full_box(b"hdlr", b"\x00" * 4 + b"vide")
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trak = box(b"trak", tkhd + box(b"mdia", mdhd + hdlr))
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trex = full_box(
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b"trex",
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track_id.to_bytes(4, "big")
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+ (1).to_bytes(4, "big")
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+ (500).to_bytes(4, "big")
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+ b"\x00" * 8,
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)
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init = box(b"ftyp", b"isom") + box(
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b"moov",
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trak + box(b"mvex", trex) + box(b"avcC", b"\x01\x64\x00\x28"),
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)
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tfhd = full_box(b"tfhd", track_id.to_bytes(4, "big"), flags=0x020000)
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tfdt = full_box(b"tfdt", (0).to_bytes(4, "big"))
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trun = full_box(b"trun", (1).to_bytes(4, "big"))
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fragment = box(b"moof", box(b"traf", tfhd + tfdt + trun)) + box(b"mdat", b"frame")
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writer = CameraArchiveWriter(session, "sensor.camera.left", 1)
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writer.append(
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"init",
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init,
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host_epoch_ns=origin_epoch_ns + 100_000_000,
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host_monotonic_ns=origin_monotonic_ns + 100_000_000,
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)
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writer.append(
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"media",
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fragment,
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host_epoch_ns=origin_epoch_ns + 200_000_000,
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host_monotonic_ns=origin_monotonic_ns + 200_000_000,
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)
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writer.close()
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return prepare_camera_compute_job(
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session_root=session,
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source_id="sensor.camera.left",
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codec_epoch=1,
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origin_epoch_ns=origin_epoch_ns,
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origin_monotonic_ns=origin_monotonic_ns,
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output_root=tmp_path / "jobs",
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)
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def _result(tmp_path: Path, job: Any) -> Path:
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parameters = {
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"score_threshold": 0.25,
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"nms_threshold": 0.45,
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"input_shape": [1, 3, 640, 640],
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}
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pipeline = {"id": "recorded-camera-coco-detection", "version": 1}
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model = {
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"id": "synthetic",
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"version": 1,
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"sha256": "a" * 64,
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"source_url": "https://example.invalid/model.onnx",
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"license": "test-only",
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"classes": "synthetic",
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}
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identity = {
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"schema_version": "missioncore.compute-result-identity/v1",
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"job_id": job.job_id,
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"input_sha256": job.input_sha256,
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"pipeline": pipeline,
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"model": {key: model[key] for key in ("id", "version", "sha256")},
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"parameters": parameters,
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}
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identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
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result_id = f"result-{identity_sha256}"
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root = tmp_path / "results" / job.job_id / result_id
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root.mkdir(parents=True)
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detections = {
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"schema_version": "missioncore.object-detections/v1",
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"result_id": result_id,
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"job_id": job.job_id,
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"input_sha256": job.input_sha256,
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"timestamp_basis": "session-time-seconds",
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"frames": [
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{
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"frame_index": 0,
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"epoch_seconds": 0.0,
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"session_seconds": job.timeline_start_seconds,
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"detections": [
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{
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"class_id": 0,
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"class_name": "person",
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"score": 0.75,
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"bbox_xyxy": [10.0, 20.0, 30.0, 60.0],
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}
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],
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}
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],
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}
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detection_payload = json.dumps(detections, indent=2).encode() + b"\n"
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(root / "detections.json").write_bytes(detection_payload)
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result = {
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"schema_version": "missioncore.compute-result/v1",
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"result_id": result_id,
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"identity_sha256": identity_sha256,
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"job_id": job.job_id,
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"input_sha256": job.input_sha256,
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"created_at_utc": "2026-07-19T00:00:00.000Z",
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"pipeline": pipeline,
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"runtime": {"kind": "synthetic"},
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"model": model,
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"parameters": parameters,
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"metrics": {"frames_processed": 1, "detections": 1},
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"artifacts": [
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{
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"kind": "object-detections",
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"path": "detections.json",
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"byte_length": len(detection_payload),
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"sha256": hashlib.sha256(detection_payload).hexdigest(),
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"schema_version": "missioncore.object-detections/v1",
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}
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],
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"warnings": [],
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}
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(root / "result.json").write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
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return root
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def test_result_is_bound_to_job_and_cache_reuses_complete_overlay(
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tmp_path: Path,
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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job = _job(tmp_path)
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result_root = _result(tmp_path, job)
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result = validate_recorded_perception_result(job.job_root, result_root)
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assert result.job.session_id == "session-1"
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assert result.frames[0].detections[0].class_name == "person"
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calls = 0
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def render(*_: object, **__: object) -> bytes:
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nonlocal calls
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calls += 1
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return b"RRF2synthetic-overlay"
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monkeypatch.setattr(result_module, "_render_overlay", render)
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store = RecordedPerceptionOverlayStore(
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jobs_root=tmp_path / "jobs",
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results_root=tmp_path / "results",
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cache_root=tmp_path / "cache",
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ffmpeg_path=Path(__file__),
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ffprobe_path=Path(__file__),
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)
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first = store.render(
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"session-1",
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application_id="nodedc_mission_core_recorded",
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recording_id="recording-1",
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)
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second = store.render(
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"session-1",
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application_id="nodedc_mission_core_recorded",
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recording_id="recording-1",
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)
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assert first == second == b"RRF2synthetic-overlay"
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assert calls == 1
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def test_result_rejects_changed_detection_payload(tmp_path: Path) -> None:
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job = _job(tmp_path)
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result_root = _result(tmp_path, job)
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detection_path = result_root / "detections.json"
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detection_path.write_bytes(detection_path.read_bytes().replace(b"0.75", b"0.76"))
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with pytest.raises(SessionIntegrityError, match="artifact identity changed"):
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validate_recorded_perception_result(job.job_root, result_root)
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