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GeoIntel release gates / Python and npm vulnerability policy (push) Successful in 21s
GeoIntel release gates / Production AI image, SBOM and container scan (push) Successful in 5m39s
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#!/usr/bin/env python3
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"""Create a checksummed luminance-normalized YOLO dataset without changing labels/splits."""
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from __future__ import annotations
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import argparse
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import hashlib
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import json
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import shutil
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import sys
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from pathlib import Path
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from PIL import Image
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SCRIPT_DIR = Path(__file__).resolve().parent
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if str(SCRIPT_DIR) not in sys.path:
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sys.path.insert(0, str(SCRIPT_DIR))
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from training_release_manifest import ( # noqa: E402
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TrainingReleaseError,
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assert_yolo_summary_bound_to_training_release,
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file_sha256,
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training_release_paths,
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)
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def sha256(path: Path) -> str:
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digest = hashlib.sha256()
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with path.open("rb") as stream:
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for chunk in iter(lambda: stream.read(1024 * 1024), b""):
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digest.update(chunk)
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return digest.hexdigest()
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def main() -> int:
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parser = argparse.ArgumentParser()
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parser.add_argument("--summary", type=Path, required=True)
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parser.add_argument("--train-yaml", type=Path, required=True)
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parser.add_argument("--corpus-manifest", type=Path, required=True)
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parser.add_argument("--output-dir", type=Path, required=True)
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parser.add_argument(
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"--fixture-mode",
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action="store_true",
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help="Accept only an explicitly fixture-only release; never creates a production-ready derived dataset.",
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)
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parser.add_argument("--force", action="store_true")
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args = parser.parse_args()
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try:
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release = assert_yolo_summary_bound_to_training_release(
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summary_path=args.summary,
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train_yaml=args.train_yaml,
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corpus_manifest=args.corpus_manifest,
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fixture_mode=args.fixture_mode,
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)
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except TrainingReleaseError as exc:
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raise SystemExit(str(exc)) from exc
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if args.output_dir.exists():
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if not args.force:
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raise SystemExit(f"Output exists: {args.output_dir}")
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shutil.rmtree(args.output_dir)
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args.output_dir.mkdir(parents=True)
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summary = json.loads(args.summary.read_text(encoding="utf-8"))
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converted = 0
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for tile in summary["tiles"]:
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split = tile["split"]
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source_image = Path(tile["image_path"])
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source_label = Path(tile["label_path"])
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image_target = args.output_dir / "images" / split / source_image.name
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label_target = args.output_dir / "labels" / split / source_label.name
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image_target.parent.mkdir(parents=True, exist_ok=True)
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label_target.parent.mkdir(parents=True, exist_ok=True)
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with Image.open(source_image) as image:
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image.convert("L").convert("RGB").save(image_target)
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shutil.copyfile(source_label, label_target)
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tile["image_path"] = str(image_target)
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tile["label_path"] = str(label_target)
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converted += 1
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dataset_yaml = args.output_dir / "dataset.yaml"
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dataset_yaml.write_text(
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f"path: {args.output_dir}\ntrain: images/train\nval: images/val\nnames:\n 0: building\n",
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encoding="utf-8",
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)
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# The source release binds the original image bytes. These transformed
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# bytes cannot inherit it, so remove its operational release pointers and
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# retain them only as explicitly non-trainable parent evidence below.
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for field_name in (
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"training_release_manifest",
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"training_release_manifest_sha256",
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"training_release_freeze",
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"training_asset_manifest",
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):
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summary.pop(field_name, None)
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summary.update(
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{
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"output_dir": str(args.output_dir),
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"dataset_yaml": str(dataset_yaml),
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"preprocessing": "luminance_rgb_replicated",
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"source_summary": str(args.summary),
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"source_summary_sha256": sha256(args.summary),
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}
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)
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output_summary = args.output_dir / "yolo_tile_dataset_summary.json"
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output_summary.write_text(json.dumps(summary, indent=2), encoding="utf-8")
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evidence = {
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"schema_version": 1,
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"status": "ok",
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"preprocessing": "luminance_rgb_replicated",
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"source_summary": str(args.summary),
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"source_summary_sha256": sha256(args.summary),
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"source_training_release": str(training_release_paths(args.train_yaml)["release_manifest"]),
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"source_training_release_sha256": file_sha256(
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training_release_paths(args.train_yaml)["release_manifest"]
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),
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"source_corpus_manifest_sha256": release["corpus"]["manifest_sha256"],
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"converted_tile_count": converted,
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"output_summary": str(output_summary),
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"output_summary_sha256": sha256(output_summary),
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"dataset_yaml": str(dataset_yaml),
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"training_eligible": False,
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"training_eligibility_reason": (
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"Derived image bytes require a new governed corpus, validation report and immutable training release."
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),
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}
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(args.output_dir / "grayscale-dataset-evidence.json").write_text(
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json.dumps(evidence, indent=2), encoding="utf-8"
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)
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print(json.dumps(evidence, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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