Datasets:

RL-MIND
/

4K-Aircraft-Detection

#!/usr/bin/env python3
"""Export Hub Parquet rows to YOLO without resizing or re-encoding images.
    pip install datasets pyarrow huggingface_hub
    python export_yolo.py --repo-id OWNER/DATASET --output ./aircraft-yolo
    python export_yolo.py --repo-id OWNER/DATASET --output ./plane-yolo --single-class
    python export_yolo.py --local-dir ./downloaded-hub-repo --output ./aircraft-yolo
The default retains size classes 0/1/2. --single-class maps all three to plane=0.
The streaming Image column is explicitly decoded as bytes, so even large native
images are written without pixel decoding. Some individual native images exceed
1 GiB; sufficient memory and disk space for the encoded row are still required.
"""
import argparse
import collections
import hashlib
import json
import math
from pathlib import Path

NAMES = {0: 'small_plane', 1: 'middle_plane', 2: 'big_plane'}


def annotation_lists(row):
    objects = row.get('objects')
    if objects is None:
        raise ValueError('Missing objects field; expected objects.yolo_bbox and category/category_id.')
    if isinstance(objects, dict):
        boxes = objects.get('yolo_bbox')
        categories = objects.get('category', objects.get('category_id', objects.get('categories')))
    elif isinstance(objects, list):
        boxes = [obj['yolo_bbox'] for obj in objects]
        categories = [obj.get('category', obj.get('category_id')) for obj in objects]
    else:
        raise ValueError('objects must be a dict of lists or a list of object dictionaries.')
    if boxes is None or categories is None or len(boxes) != len(categories):
        raise ValueError('Missing or inconsistent object categories and normalized YOLO boxes.')
    return boxes, categories


def export_one_row(row, split, output, single_class=False):
    image = row.get('image')
    if isinstance(image, dict):
        encoded = image.get('bytes')
        encoded_path = image.get('path')
    elif isinstance(image, (bytes, bytearray, memoryview)):
        encoded = image
        encoded_path = None
    else:
        encoded = row.get('image_bytes')
        encoded_path = None
    if encoded is None:
        raise ValueError('No embedded image bytes. Ensure the Image column uses decode=False.')
    if not isinstance(encoded, (bytes, bytearray, memoryview)):
        raise ValueError('Image bytes have an unexpected type; pixel decoding is not supported.')
    filename = row.get('file_name') or row.get('filename') or row.get('original_filename') or encoded_path
    if not filename:
        raise ValueError('Missing filename.')
    # Keep only the basename from a parquet image.path while rejecting user-facing paths.
    filename = str(filename)
    if row.get('file_name') or row.get('filename') or row.get('original_filename'):
        if Path(filename).name != filename or '\\' in filename:
            raise ValueError('Unsafe filename: ' + filename)
    else:
        filename = Path(filename).name
    if filename in ['', '.', '..']:
        raise ValueError('Unsafe filename.')
    image_path = output / 'images' / split / filename
    label_path = output / 'labels' / split / (Path(filename).stem + '.txt')
    if image_path.exists() or label_path.exists():
        raise FileExistsError('Output filename already exists: ' + filename)
    digest = hashlib.sha256(encoded).hexdigest()
    expected = row.get('sha256') or row.get('image_sha256')
    if expected and digest != expected:
        raise ValueError('Image SHA256 mismatch: ' + filename)
    boxes, categories = annotation_lists(row)
    lines = []
    counts = collections.Counter()
    for box, category in zip(boxes, categories):
        category = int(category)
        if category not in NAMES or len(box) != 4:
            raise ValueError('Invalid category or YOLO bbox: ' + filename)
        cx, cy, width, height = map(float, box)
        values = [cx, cy, width, height]
        if not all(math.isfinite(value) for value in values) or width <= 0 or height <= 0:
            raise ValueError('Invalid normalized YOLO bbox: ' + filename)
        if min(cx-width/2, cy-height/2) < -1e-10 or max(cx+width/2, cy+height/2) > 1+1e-10:
            raise ValueError('YOLO bbox extends beyond image bounds: ' + filename)
        selected = 0 if single_class else category
        counts[selected] += 1
        lines.append(str(selected) + ' ' + ' '.join(format(value, '.17g') for value in values))
    image_path.parent.mkdir(parents=True, exist_ok=True)
    label_path.parent.mkdir(parents=True, exist_ok=True)
    # Write the exact encoded bytes, with no Image.open, decode, resize, or save.
    view = memoryview(encoded)
    with image_path.open('xb') as handle:
        for position in range(0, len(view), 8*1024*1024):
            handle.write(view[position:position+8*1024*1024])
    label_path.write_text('\n'.join(lines) + ('\n' if lines else ''))
    return {'split': split, 'filename': filename, 'image_relative_path': str(image_path.relative_to(output)), 'label_relative_path': str(label_path.relative_to(output)), 'sha256': digest, 'sample_id': row.get('sample_id'), 'source_group_id': row.get('source_group_id'), 'source_dataset': row.get('source_dataset'), 'annotation_count': len(lines), 'class_counts': dict(counts)}


def discover_parquet(root, splits):
    files = list(root.rglob('*.parquet'))
    result = {}
    for split in splits:
        aliases = ['validation', 'val'] if split == 'val' else [split]
        matched = sorted(str(path) for path in files if any(path.name.startswith(alias+'-') or alias in path.relative_to(root).parts[:-1] for alias in aliases))
        if not matched:
            raise FileNotFoundError('No parquet shards found for split ' + split + ' under ' + str(root))
        result['validation' if split == 'val' else split] = matched
    return result


def main():
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    source = parser.add_mutually_exclusive_group(required=True)
    source.add_argument('--repo-id', help='Hugging Face dataset ID, OWNER/DATASET.')
    source.add_argument('--local-dir', type=Path, help='Downloaded repository containing train/val/test Parquet shards.')
    parser.add_argument('--config', default='default', help='Hub dataset configuration; default is the full-resolution default config. The preview config is not a training source.')
    parser.add_argument('--revision', help='Optional Hub commit SHA or revision.')
    parser.add_argument('--output', type=Path, required=True)
    parser.add_argument('--single-class', action='store_true', help='Map original size class IDs 0/1/2 to one plane=0 class.')
    parser.add_argument('--splits', nargs='+', choices=['train', 'val', 'test'], default=['train', 'val', 'test'])
    parser.add_argument('--max-samples', type=int, help='Optional smoke-test limit per split; omitted exports all images.')
    args = parser.parse_args()
    if args.max_samples is not None and args.max_samples <= 0:
        parser.error('--max-samples must be positive.')
    if args.config == 'preview':
        parser.error('The preview configuration contains display images and cannot be exported as training data. Use --config default.')
    if args.output.exists() and any(args.output.iterdir()):
        parser.error('--output must be a new or empty directory; existing files are never overwritten.')
    from datasets import Image, load_dataset
    args.output.mkdir(parents=True, exist_ok=True)
    if args.local_dir:
        datasets = load_dataset('parquet', data_files=discover_parquet(args.local_dir, args.splits), streaming=True)
    else:
        kwargs = {'path': args.repo_id, 'streaming': True}
        if args.config:
            kwargs['name'] = args.config
        if args.revision:
            kwargs['revision'] = args.revision
        datasets = load_dataset(**kwargs)
    summary = {'mode': 'plane' if args.single_class else 'size3', 'splits': {}, 'image_bytes_reencoded': False}
    with (args.output/'export_manifest.jsonl').open('w') as manifest:
        for split in args.splits:
            source_split = 'validation' if split == 'val' else split
            if source_split not in datasets and split == 'val' and 'val' in datasets:
                source_split = 'val'
            data = datasets[source_split]
            if 'image' in (data.features or {}):
                data = data.cast_column('image', Image(decode=False))
            images = 0
            objects = 0
            classes = collections.Counter()
            for row in data:
                result = export_one_row(row, split, args.output, args.single_class)
                manifest.write(json.dumps(result, ensure_ascii=False)+'\n')
                images += 1
                objects += result['annotation_count']
                classes.update(result['class_counts'])
                if images % 100 == 0:
                    print(split+': '+str(images)+' images', flush=True)
                if args.max_samples is not None and images >= args.max_samples:
                    break
            summary['splits'][split] = {'images': images, 'objects': objects, 'class_counts': dict(classes)}
    names = {0: 'plane'} if args.single_class else NAMES
    (args.output/'data.yaml').write_text('train: images/train\nval: images/val\ntest: images/test\n\nnames:\n'+''.join('  '+str(key)+': '+name+'\n' for key, name in names.items()))
    (args.output/'export_summary.json').write_text(json.dumps(summary, ensure_ascii=False, indent=2)+'\n')
    print(json.dumps(summary, ensure_ascii=False, indent=2))


if __name__ == '__main__':
    main()

	#!/usr/bin/env python3
	"""Export Hub Parquet rows to YOLO without resizing or re-encoding images.

	pip install datasets pyarrow huggingface_hub
	python export_yolo.py --repo-id OWNER/DATASET --output ./aircraft-yolo
	python export_yolo.py --repo-id OWNER/DATASET --output ./plane-yolo --single-class
	python export_yolo.py --local-dir ./downloaded-hub-repo --output ./aircraft-yolo

	The default retains size classes 0/1/2. --single-class maps all three to plane=0.
	The streaming Image column is explicitly decoded as bytes, so even large native
	images are written without pixel decoding. Some individual native images exceed
	1 GiB; sufficient memory and disk space for the encoded row are still required.
	"""
	import argparse
	import collections
	import hashlib
	import json
	import math
	from pathlib import Path

	NAMES = {0: 'small_plane', 1: 'middle_plane', 2: 'big_plane'}


	def annotation_lists(row):
	objects = row.get('objects')
	if objects is None:
	raise ValueError('Missing objects field; expected objects.yolo_bbox and category/category_id.')
	if isinstance(objects, dict):
	boxes = objects.get('yolo_bbox')
	categories = objects.get('category', objects.get('category_id', objects.get('categories')))
	elif isinstance(objects, list):
	boxes = [obj['yolo_bbox'] for obj in objects]
	categories = [obj.get('category', obj.get('category_id')) for obj in objects]
	else:
	raise ValueError('objects must be a dict of lists or a list of object dictionaries.')
	if boxes is None or categories is None or len(boxes) != len(categories):
	raise ValueError('Missing or inconsistent object categories and normalized YOLO boxes.')
	return boxes, categories


	def export_one_row(row, split, output, single_class=False):
	image = row.get('image')
	if isinstance(image, dict):
	encoded = image.get('bytes')
	encoded_path = image.get('path')
	elif isinstance(image, (bytes, bytearray, memoryview)):
	encoded = image
	encoded_path = None
	else:
	encoded = row.get('image_bytes')
	encoded_path = None
	if encoded is None:
	raise ValueError('No embedded image bytes. Ensure the Image column uses decode=False.')
	if not isinstance(encoded, (bytes, bytearray, memoryview)):
	raise ValueError('Image bytes have an unexpected type; pixel decoding is not supported.')
	filename = row.get('file_name') or row.get('filename') or row.get('original_filename') or encoded_path
	if not filename:
	raise ValueError('Missing filename.')
	# Keep only the basename from a parquet image.path while rejecting user-facing paths.
	filename = str(filename)
	if row.get('file_name') or row.get('filename') or row.get('original_filename'):
	if Path(filename).name != filename or '\\' in filename:
	raise ValueError('Unsafe filename: ' + filename)
	else:
	filename = Path(filename).name
	if filename in ['', '.', '..']:
	raise ValueError('Unsafe filename.')
	image_path = output / 'images' / split / filename
	label_path = output / 'labels' / split / (Path(filename).stem + '.txt')
	if image_path.exists() or label_path.exists():
	raise FileExistsError('Output filename already exists: ' + filename)
	digest = hashlib.sha256(encoded).hexdigest()
	expected = row.get('sha256') or row.get('image_sha256')
	if expected and digest != expected:
	raise ValueError('Image SHA256 mismatch: ' + filename)
	boxes, categories = annotation_lists(row)
	lines = []
	counts = collections.Counter()
	for box, category in zip(boxes, categories):
	category = int(category)
	if category not in NAMES or len(box) != 4:
	raise ValueError('Invalid category or YOLO bbox: ' + filename)
	cx, cy, width, height = map(float, box)
	values = [cx, cy, width, height]
	if not all(math.isfinite(value) for value in values) or width <= 0 or height <= 0:
	raise ValueError('Invalid normalized YOLO bbox: ' + filename)
	if min(cx-width/2, cy-height/2) < -1e-10 or max(cx+width/2, cy+height/2) > 1+1e-10:
	raise ValueError('YOLO bbox extends beyond image bounds: ' + filename)
	selected = 0 if single_class else category
	counts[selected] += 1
	lines.append(str(selected) + ' ' + ' '.join(format(value, '.17g') for value in values))
	image_path.parent.mkdir(parents=True, exist_ok=True)
	label_path.parent.mkdir(parents=True, exist_ok=True)
	# Write the exact encoded bytes, with no Image.open, decode, resize, or save.
	view = memoryview(encoded)
	with image_path.open('xb') as handle:
	for position in range(0, len(view), 810241024):
	handle.write(view[position:position+810241024])
	label_path.write_text('\n'.join(lines) + ('\n' if lines else ''))
	return {'split': split, 'filename': filename, 'image_relative_path': str(image_path.relative_to(output)), 'label_relative_path': str(label_path.relative_to(output)), 'sha256': digest, 'sample_id': row.get('sample_id'), 'source_group_id': row.get('source_group_id'), 'source_dataset': row.get('source_dataset'), 'annotation_count': len(lines), 'class_counts': dict(counts)}


	def discover_parquet(root, splits):
	files = list(root.rglob('*.parquet'))
	result = {}
	for split in splits:
	aliases = ['validation', 'val'] if split == 'val' else [split]
	matched = sorted(str(path) for path in files if any(path.name.startswith(alias+'-') or alias in path.relative_to(root).parts[:-1] for alias in aliases))
	if not matched:
	raise FileNotFoundError('No parquet shards found for split ' + split + ' under ' + str(root))
	result['validation' if split == 'val' else split] = matched
	return result


	def main():
	parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
	source = parser.add_mutually_exclusive_group(required=True)
	source.add_argument('--repo-id', help='Hugging Face dataset ID, OWNER/DATASET.')
	source.add_argument('--local-dir', type=Path, help='Downloaded repository containing train/val/test Parquet shards.')
	parser.add_argument('--config', default='default', help='Hub dataset configuration; default is the full-resolution default config. The preview config is not a training source.')
	parser.add_argument('--revision', help='Optional Hub commit SHA or revision.')
	parser.add_argument('--output', type=Path, required=True)
	parser.add_argument('--single-class', action='store_true', help='Map original size class IDs 0/1/2 to one plane=0 class.')
	parser.add_argument('--splits', nargs='+', choices=['train', 'val', 'test'], default=['train', 'val', 'test'])
	parser.add_argument('--max-samples', type=int, help='Optional smoke-test limit per split; omitted exports all images.')
	args = parser.parse_args()
	if args.max_samples is not None and args.max_samples <= 0:
	parser.error('--max-samples must be positive.')
	if args.config == 'preview':
	parser.error('The preview configuration contains display images and cannot be exported as training data. Use --config default.')
	if args.output.exists() and any(args.output.iterdir()):
	parser.error('--output must be a new or empty directory; existing files are never overwritten.')
	from datasets import Image, load_dataset
	args.output.mkdir(parents=True, exist_ok=True)
	if args.local_dir:
	datasets = load_dataset('parquet', data_files=discover_parquet(args.local_dir, args.splits), streaming=True)
	else:
	kwargs = {'path': args.repo_id, 'streaming': True}
	if args.config:
	kwargs['name'] = args.config
	if args.revision:
	kwargs['revision'] = args.revision
	datasets = load_dataset(**kwargs)
	summary = {'mode': 'plane' if args.single_class else 'size3', 'splits': {}, 'image_bytes_reencoded': False}
	with (args.output/'export_manifest.jsonl').open('w') as manifest:
	for split in args.splits:
	source_split = 'validation' if split == 'val' else split
	if source_split not in datasets and split == 'val' and 'val' in datasets:
	source_split = 'val'
	data = datasets[source_split]
	if 'image' in (data.features or {}):
	data = data.cast_column('image', Image(decode=False))
	images = 0
	objects = 0
	classes = collections.Counter()
	for row in data:
	result = export_one_row(row, split, args.output, args.single_class)
	manifest.write(json.dumps(result, ensure_ascii=False)+'\n')
	images += 1
	objects += result['annotation_count']
	classes.update(result['class_counts'])
	if images % 100 == 0:
	print(split+': '+str(images)+' images', flush=True)
	if args.max_samples is not None and images >= args.max_samples:
	break
	summary['splits'][split] = {'images': images, 'objects': objects, 'class_counts': dict(classes)}
	names = {0: 'plane'} if args.single_class else NAMES
	(args.output/'data.yaml').write_text('train: images/train\nval: images/val\ntest: images/test\n\nnames:\n'+''.join(' '+str(key)+': '+name+'\n' for key, name in names.items()))
	(args.output/'export_summary.json').write_text(json.dumps(summary, ensure_ascii=False, indent=2)+'\n')
	print(json.dumps(summary, ensure_ascii=False, indent=2))


	if __name__ == '__main__':
	main()