Buckets:
| """Screen a DIOR-R review queue against selected DOTA development scenes.""" | |
| import argparse | |
| import hashlib | |
| import json | |
| from pathlib import Path | |
| from evaluation.audit_fair1m_dior_queue_duplicates import fingerprints | |
| def audit(queue, reference_images): | |
| raw = queue.read_bytes() | |
| data = json.loads(raw) | |
| records = data['records'] | |
| if not records or not reference_images: | |
| raise ValueError('queue and development image list must be nonempty') | |
| refs = [] | |
| for record in records: | |
| if record.get('training_eligible') is not False: | |
| raise ValueError('queue must remain training-ineligible') | |
| payload = Path(record['image']).read_bytes() | |
| if hashlib.sha256(payload).hexdigest() != record['image_sha256']: | |
| raise ValueError(f"queue image hash mismatch: {record['id']}") | |
| fp = fingerprints(payload) | |
| refs.append((record['id'], fp['pixel_sha256'], [int(x, 16) for x in fp['phashes']])) | |
| matches, errors, reference_hashes = [], [], [] | |
| for path in reference_images: | |
| try: | |
| payload = path.read_bytes() | |
| fp = fingerprints(payload) | |
| reference_hashes.append({'path': str(path), 'sha256': hashlib.sha256(payload).hexdigest()}) | |
| values = [int(x, 16) for x in fp['phashes']] | |
| for identifier, pixels, hashes in refs: | |
| distance = min((a ^ b).bit_count() for a in values for b in hashes) | |
| if pixels == fp['pixel_sha256'] or distance <= 6: | |
| matches.append({'queue_id': identifier, 'reference_path': str(path), | |
| 'exact_rgb': pixels == fp['pixel_sha256'], 'minimum_phash_distance': distance}) | |
| except Exception as exc: | |
| errors.append({'reference_path': str(path), 'error': str(exc)}) | |
| return {'status': 'PENDING_REVIEW', 'queue_sha256': hashlib.sha256(raw).hexdigest(), | |
| 'queue_images': len(refs), 'reference_images': len(reference_images), | |
| 'reference_hashes': reference_hashes, 'matches': matches, 'errors': errors, | |
| 'phash_distance_limit': 6, 'training_eligible': False, 'independence_established': False, | |
| 'limitations': ['Whole-image exact-RGB and coarse DCT screen only; matches require review.', | |
| 'Cannot rule out crops, rescaling, nearby acquisitions, geography, other sources or pretraining exposure.', | |
| 'No-match does not grant training eligibility or replace independent label review.']} | |
| def main(): | |
| parser = argparse.ArgumentParser(description=__doc__) | |
| parser.add_argument('--queue', type=Path, required=True) | |
| parser.add_argument('--development-images', type=Path, required=True) | |
| parser.add_argument('--output', type=Path, required=True) | |
| args = parser.parse_args() | |
| if args.output.exists(): | |
| raise FileExistsError(args.output) | |
| images = sorted(args.development_images.rglob('*.png')) | |
| result = audit(args.queue, images) | |
| with args.output.open('x', encoding='utf-8') as handle: | |
| json.dump(result, handle, indent=2) | |
| handle.write('\n') | |
| print(json.dumps({key: result[key] for key in ('queue_images', 'reference_images', 'training_eligible', 'independence_established')}), | |
| 'matches', len(result['matches']), 'errors', len(result['errors'])) | |
| if __name__ == '__main__': | |
| main() | |
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