feat(perception): integrate calibrated operator pipeline
Add calibrated K1 projection, recorded and near-live perception qualification, unified Rerun operator layers, bounded replay admission, audited viewer controls, worker experiments, and lab evidence.
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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from typing import Any
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import pytest
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from PIL import Image, ImageDraw
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from test_perception_qualification import _evaluation_fixture
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from k1link.compute import (
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AnnotationWorkspaceError,
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prepare_annotation_workspace,
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prepare_recorded_evaluation_pack,
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validate_annotation_workspace,
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)
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from k1link.device_plugins.xgrids_k1.analyze.valid_fov import validate_k1_valid_fov_mask
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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 _sha256(path: Path) -> str:
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return hashlib.sha256(path.read_bytes()).hexdigest()
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def _write_json(path: Path, payload: object) -> None:
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path.write_text(json.dumps(payload, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
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def _artifact(path: Path, root: Path) -> dict[str, Any]:
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return {
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"path": path.relative_to(root).as_posix(),
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"byte_length": path.stat().st_size,
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"sha256": _sha256(path),
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}
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def _prelabel_fixture(pack_root: Path, valid_fov_root: Path, output_root: Path) -> Path:
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pack = json.loads((pack_root / "manifest.json").read_text(encoding="utf-8"))
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valid_fov = validate_k1_valid_fov_mask(valid_fov_root)
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staging = output_root / "staging"
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(staging / "instance-prelabels").mkdir(parents=True)
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(staging / "semantic-prelabels").mkdir()
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with Image.open(valid_fov.mask_path) as opened:
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valid_mask = opened.copy()
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rows: list[dict[str, Any]] = []
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for order, source in enumerate(pack["identity"]["frames"], start=1):
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instance = Image.new("I;16", (valid_fov.width, valid_fov.height), 0)
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ImageDraw.Draw(instance).rectangle((390, 290, 409, 309), fill=1)
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instance.save(staging / "instance-prelabels" / f"image-{order:03d}.png")
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semantic = Image.composite(
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Image.new("L", (valid_fov.width, valid_fov.height), 7),
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Image.new("L", (valid_fov.width, valid_fov.height), 0),
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valid_mask,
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)
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ImageDraw.Draw(semantic).rectangle((390, 290, 409, 309), fill=4)
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semantic.save(staging / "semantic-prelabels" / f"image-{order:03d}.png")
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rows.append(
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{
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"schema_version": "missioncore.perception-evaluation-prelabel-frame/v1",
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"image_id": source["image_id"],
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"frame_index": source["frame_index"],
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"session_seconds": source["session_seconds"],
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"role": source["role"],
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"group_id": source["group_id"],
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"instances": [
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{
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"instance_id": 1,
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"draft_category_id": 4,
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"draft_category": "car",
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"source_model_category_id": 3,
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"source_model_category": "car",
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"score": 0.9,
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"box_xyxy": [390.0, 290.0, 410.0, 310.0],
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"mask_pixels": 400,
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"review_state": "unreviewed-model-draft",
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}
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],
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"review_state": "unreviewed-model-draft",
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}
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)
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(staging / "frames.jsonl").write_text(
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"".join(json.dumps(row, separators=(",", ":")) + "\n" for row in rows),
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encoding="utf-8",
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)
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identity = {
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"schema_version": "missioncore.perception-evaluation-prelabels-identity/v1",
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"evaluation_pack_id": pack["generation_id"],
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"evaluation_identity_sha256": pack["identity_sha256"],
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"valid_fov_generation_id": valid_fov.generation_id,
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"pipeline": "fixture/v1",
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}
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identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
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result_id = f"evaluation-prelabels-{identity_sha256}"
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artifacts = [
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_artifact(path, staging) for path in sorted(staging.rglob("*")) if path.is_file()
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]
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_write_json(
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staging / "result.json",
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{
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"schema_version": "missioncore.perception-evaluation-prelabels/v1",
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"result_id": result_id,
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"identity_sha256": identity_sha256,
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"identity": identity,
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"review_state": "unreviewed-model-draft",
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"artifacts": artifacts,
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},
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)
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final = output_root / result_id
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staging.rename(final)
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return final
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def _workspace_fixture(tmp_path: Path) -> tuple[Path, Path, Path, Path]:
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job_root, qualification_root, valid_fov_root, frames_root, timeline_path, requests = (
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_evaluation_fixture(tmp_path)
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)
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pack = prepare_recorded_evaluation_pack(
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job_root=job_root,
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qualification_root=qualification_root,
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valid_fov_root=valid_fov_root,
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decoded_frames_root=frames_root,
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timeline_path=timeline_path,
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output_root=tmp_path / "evaluation-packs",
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selection=requests,
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decoder_version="fixture-decoder/v1",
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selection_document_sha256="1" * 64,
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producer_files=(("fixture.py", "2" * 64),),
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)
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prelabels = _prelabel_fixture(pack.root, valid_fov_root, tmp_path / "prelabels")
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return pack.root, prelabels, valid_fov_root, tmp_path / "annotation-workspaces"
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def test_annotation_workspace_is_reproducible_and_remains_unreviewed(tmp_path: Path) -> None:
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pack_root, prelabels_root, valid_fov_root, output_root = _workspace_fixture(tmp_path)
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first = prepare_annotation_workspace(
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evaluation_pack_root=pack_root,
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prelabels_root=prelabels_root,
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valid_fov_root=valid_fov_root,
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output_root=output_root,
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producer_files=(("fixture.py", "3" * 64),),
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)
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repeated = prepare_annotation_workspace(
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evaluation_pack_root=pack_root,
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prelabels_root=prelabels_root,
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valid_fov_root=valid_fov_root,
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output_root=output_root,
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producer_files=(("fixture.py", "3" * 64),),
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)
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assert repeated == first
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assert first.frame_count == 22
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assert first.draft_instance_count == 22
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manifest = json.loads(first.manifest_path.read_text(encoding="utf-8"))
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assert manifest["ground_truth"] is False
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assert manifest["state"] == "prepared-unreviewed-model-draft"
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review = json.loads(first.review_template_path.read_text(encoding="utf-8"))
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assert {row["review_status"] for row in review["frames"]} == {"unreviewed"}
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with zipfile.ZipFile(first.instance_archive_path) as archive:
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coco = json.loads(archive.read("annotations/instances_default.json"))
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assert len(coco["images"]) == 22
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assert len(coco["annotations"]) == 22
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assert all(sum(row["segmentation"]["counts"]) == 800 * 600 for row in coco["annotations"])
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assert all(row["area"] == 400 for row in coco["annotations"])
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assert (
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validate_annotation_workspace(
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first.root,
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evaluation_pack_root=pack_root,
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prelabels_root=prelabels_root,
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valid_fov_root=valid_fov_root,
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)
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== first
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)
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def test_annotation_workspace_rejects_changed_archive(tmp_path: Path) -> None:
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pack_root, prelabels_root, valid_fov_root, output_root = _workspace_fixture(tmp_path)
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result = prepare_annotation_workspace(
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evaluation_pack_root=pack_root,
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prelabels_root=prelabels_root,
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valid_fov_root=valid_fov_root,
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output_root=output_root,
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producer_files=(("fixture.py", "3" * 64),),
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)
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result.semantic_archive_path.write_bytes(b"changed")
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with pytest.raises(AnnotationWorkspaceError, match="artifact changed"):
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validate_annotation_workspace(result.root)
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