280 lines
9.9 KiB
Python
280 lines
9.9 KiB
Python
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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import pytest
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from k1link.compute.e46e_ready_stack import (
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E46E_RUNTIME_SCHEMA,
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E46EReadyStackError,
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analyze_e46e_frames,
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build_e46e_ready_stack,
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read_e46e_ready_stack,
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)
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def test_e46e_projects_stock_deepstream_output_without_custom_tracking(
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tmp_path: Path,
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) -> None:
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source, profile = _source_and_profile(tmp_path, frame_count=6)
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raw = _raw_output(tmp_path, profile, frame_count=6)
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_detector(raw, 0, "car", (10, 20, 40, 60), 0.91)
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_tracker(raw, 0, 7, "car", (10, 20, 40, 60), 0.88)
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_tracker(raw, 1, 7, "car", (12, 20, 42, 60), 0.73)
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_detector(raw, 5, "person", (100, 80, 130, 160), 0.93)
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_tracker(raw, 5, 7, "person", (100, 80, 130, 160), 0.79)
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result = build_e46e_ready_stack(
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source_job_root=source,
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raw_root=raw,
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profile_path=profile,
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output_root=tmp_path / "results",
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)
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metrics = result["report"]["metrics"]
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assert metrics["frame_count"] == 6
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assert metrics["detection_observation_count"] == 2
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assert metrics["track_observation_count"] == 3
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assert metrics["detection_box_clipped_count"] == 0
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assert metrics["track_box_clipped_count"] == 0
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assert metrics["tracker_recovered_frame_count"] == 1
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assert metrics["full_layer_blackout_event_count"] == 1
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assert metrics["route_id_gap_event_count"] == 1
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assert metrics["track_class_switch_count"] == 1
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assert result["frames"][1]["objects"][0]["object_id"] == "nvdcf-7"
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assert result["frames"][1]["objects"][0]["bbox"] == [12.0, 20.0, 30.0, 40.0]
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assert [
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(component["kind"], component["role"])
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for component in result["report"]["method"]["components"]
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] == [
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("source", "exact recorded camera evidence"),
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("model", "framewise traffic-object detection"),
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("tool", "official RT-DETR output decoding"),
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("algorithm", "route-local temporal association"),
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("runtime", "GPU inference and media pipeline"),
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]
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assert result["report"]["decision"]["custom_temporal_logic_used"] is False
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assert result["manifest"]["authority"]["navigation_or_safety_accepted"] is False
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repeated = build_e46e_ready_stack(
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source_job_root=source,
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raw_root=raw,
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profile_path=profile,
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output_root=tmp_path / "results",
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)
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assert repeated["result_id"] == result["result_id"]
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def test_e46e_clips_only_display_geometry_at_the_source_plane(tmp_path: Path) -> None:
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source, profile = _source_and_profile(tmp_path, frame_count=1)
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raw = _raw_output(tmp_path, profile, frame_count=1)
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_tracker(raw, 0, 31, "person", (447, 547, 527, 608), 0.69)
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result = build_e46e_ready_stack(
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source_job_root=source,
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raw_root=raw,
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profile_path=profile,
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output_root=tmp_path / "results",
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)
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item = result["frames"][0]["objects"][0]
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assert item["bbox"] == [447.0, 547.0, 80.0, 53.0]
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assert item["source_bbox_ltrb"] == [447.0, 547.0, 527.0, 608.0]
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assert item["source_plane_clipped"] is True
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assert result["report"]["metrics"]["track_box_clipped_count"] == 1
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assert result["report"]["decision"]["custom_temporal_logic_used"] is False
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def test_e46e_rejects_tampered_immutable_artifact(tmp_path: Path) -> None:
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source, profile = _source_and_profile(tmp_path, frame_count=1)
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raw = _raw_output(tmp_path, profile, frame_count=1)
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result = build_e46e_ready_stack(
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source_job_root=source,
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raw_root=raw,
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profile_path=profile,
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output_root=tmp_path / "results",
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)
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(result["result_root"] / "overlay.mp4").write_bytes(b"changed")
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with pytest.raises(E46EReadyStackError, match="artifact changed"):
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read_e46e_ready_stack(result["result_root"])
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def test_e46e_frame_analyzer_requires_contiguous_source_order() -> None:
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with pytest.raises(E46EReadyStackError, match="not contiguous"):
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analyze_e46e_frames(
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[
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{
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"frame_index": 1,
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"session_seconds": 1.0,
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"detections": [],
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"objects": [],
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}
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]
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)
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def _source_and_profile(tmp_path: Path, *, frame_count: int) -> tuple[Path, Path]:
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source = tmp_path / "source-job"
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camera = source / "input" / "camera" / "sensor.camera.right" / "epoch-1"
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camera.mkdir(parents=True)
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index_rows = [
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{
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"schema_version": "missioncore.camera-recording-index/v1",
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"kind": "media",
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"sequence": sequence,
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"session_monotonic_ns": 1_000_000_000 + (sequence - 1) * 100_000_000,
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"sha256": hashlib.sha256(f"frame-{sequence}".encode()).hexdigest(),
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}
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for sequence in range(1, frame_count + 1)
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]
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index_path = camera / "index.jsonl"
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index_path.write_text(
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"".join(json.dumps(row, sort_keys=True) + "\n" for row in index_rows),
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encoding="utf-8",
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)
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stream_sha = "1" * 64
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summary_path = camera / "summary.json"
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summary_path.write_text(
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json.dumps(
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{
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"schema_version": "missioncore.camera-recording/v1",
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"stream_sha256": stream_sha,
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"segment_count": frame_count,
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},
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indent=2,
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)
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+ "\n",
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encoding="utf-8",
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)
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index_sha = _sha(index_path)
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summary_sha = _sha(summary_path)
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job = {
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"schema_version": "missioncore.compute-job/v1",
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"job_id": "recorded-test",
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"input": {
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"session_id": "test-session",
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"source_id": "sensor.camera.right",
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"segment_count": frame_count,
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"archive_index_sha256": index_sha,
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"archive_summary_sha256": summary_sha,
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"timeline": {"start_seconds": 10.0, "end_seconds": 11.0},
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},
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}
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(source / "job.json").write_text(json.dumps(job, indent=2) + "\n", encoding="utf-8")
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profile_value = {
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"schema_version": "missioncore.e46e-ready-stack-profile/v1",
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"profile_id": "e46e-test/v1",
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"source": {
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"camera_source_id": "sensor.camera.right",
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"job_id": "recorded-test",
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"session_id": "test-session",
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"segment_count": frame_count,
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"stream_sha256": stream_sha,
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"archive_index_sha256": index_sha,
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"archive_summary_sha256": summary_sha,
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},
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"runtime": {
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"container_image": "nvcr.io/nvidia/deepstream:9.1-samples-multiarch@sha256:"
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+ "2" * 64,
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"deepstream_version": "9.1",
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},
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"detector": {
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"name": "NVIDIA TrafficCamNet Transformer Lite",
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"version": "test",
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"model_sha256": "3" * 64,
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"custom_postprocessing": False,
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},
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"parser": {
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"name": "NVIDIA DeepStream TAO custom bounding-box parser",
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"repository": "https://github.com/NVIDIA/DeepStream.git",
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"commit": "8" * 40,
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"symbol": "NvDsInferParseCustomDDETRTAO",
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"library_sha256": "9" * 64,
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"custom_mission_core_logic": False,
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},
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"tracker": {
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"name": "NVIDIA NvDCF",
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"configuration": "stock-accuracy",
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"custom_association": False,
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"custom_hold_or_stitch": False,
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},
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"output": {"frame_width": 800, "frame_height": 600},
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"authority": {
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"ground_truth": False,
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"candidate_accepted": False,
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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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profile = tmp_path / "profile.json"
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profile.write_text(json.dumps(profile_value, indent=2) + "\n", encoding="utf-8")
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return source, profile
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def _raw_output(tmp_path: Path, profile_path: Path, *, frame_count: int) -> Path:
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raw = tmp_path / "raw"
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detections = raw / "detections"
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tracks = raw / "tracks"
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detections.mkdir(parents=True)
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tracks.mkdir()
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for frame in range(frame_count):
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(detections / f"00_000_{frame:06d}.txt").write_text("", encoding="utf-8")
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(tracks / f"00_000_{frame:06d}.txt").write_text("", encoding="utf-8")
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overlay = raw / "overlay.mp4"
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overlay.write_bytes(b"synthetic-overlay")
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(raw / "deepstream.log").write_text("synthetic success\n", encoding="utf-8")
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profile = json.loads(profile_path.read_text(encoding="utf-8"))
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image = profile["runtime"]["container_image"]
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runtime = {
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"schema_version": E46E_RUNTIME_SCHEMA,
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"status": "completed",
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"worker_host": "TEST-WORKER-006",
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"gpu_name": "Synthetic RTX",
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"container_image": image,
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"container_image_digest": image.rsplit("@sha256:", 1)[1],
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"model_sha256": profile["detector"]["model_sha256"],
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"model_engine_sha256": "4" * 64,
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"deepstream_config_sha256": "5" * 64,
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"detector_config_sha256": "6" * 64,
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"parser_library_sha256": profile["parser"]["library_sha256"],
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"tracker_config_sha256": "7" * 64,
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"input_stream_sha256": profile["source"]["stream_sha256"],
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"overlay_sha256": _sha(overlay),
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}
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(raw / "runtime.json").write_text(json.dumps(runtime, indent=2) + "\n", encoding="utf-8")
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return raw
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def _detector(
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raw: Path,
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frame: int,
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label: str,
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box: tuple[int, int, int, int],
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confidence: float,
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) -> None:
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left, top, right, bottom = box
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(raw / "detections" / f"00_000_{frame:06d}.txt").write_text(
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f"{label} 0.0 0 0.0 {left} {top} {right} {bottom} 0 0 0 0 0 0 0 {confidence}\n",
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encoding="utf-8",
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)
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def _tracker(
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raw: Path,
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frame: int,
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track_id: int,
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label: str,
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box: tuple[int, int, int, int],
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confidence: float,
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) -> None:
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left, top, right, bottom = box
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(raw / "tracks" / f"00_000_{frame:06d}.txt").write_text(
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f"{label} {track_id} 0.0 0 0.0 {left} {top} {right} {bottom} 0 0 0 0 0 0 0 {confidence}\n",
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encoding="utf-8",
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)
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def _sha(path: Path) -> str:
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return hashlib.sha256(path.read_bytes()).hexdigest()
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