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#!/usr/bin/env python3
"""
Generate HuggingFace-compatible metadata CSV files for the billiards dataset.
Creates train.csv, validation.csv, and test.csv with image paths and annotations.
"""
import csv
import os
from pathlib import Path
def read_yolo_labels(label_path):
"""Read YOLO format labels from a text file."""
if not os.path.exists(label_path):
return []
with open(label_path, 'r') as f:
lines = f.readlines()
annotations = []
for line in lines:
parts = line.strip().split()
if len(parts) == 5:
class_id, x_center, y_center, width, height = parts
annotations.append({
'class_id': int(class_id),
'x_center': float(x_center),
'y_center': float(y_center),
'width': float(width),
'height': float(height)
})
return annotations
def create_metadata_csv(split_name, output_filename):
"""Create a metadata CSV file for a given split."""
data_dir = Path('data')
images_dir = data_dir / split_name / 'images'
labels_dir = data_dir / split_name / 'labels'
if not images_dir.exists():
print(f"Warning: {images_dir} does not exist")
return
rows = []
image_files = sorted(images_dir.glob('*.png'))
for image_path in image_files:
# Get corresponding label file
label_filename = image_path.stem + '.txt'
label_path = labels_dir / label_filename
# Read annotations
annotations = read_yolo_labels(label_path)
# Create relative path from root
relative_image_path = str(image_path)
row = {
'image': relative_image_path,
'annotations': str(annotations) # Store as string for CSV compatibility
}
rows.append(row)
# Write CSV file
if rows:
with open(output_filename, 'w', newline='') as csvfile:
fieldnames = ['image', 'annotations']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(rows)
print(f"Created {output_filename} with {len(rows)} entries")
else:
print(f"No data found for {split_name}")
def main():
"""Generate metadata CSV files for all splits."""
# Map split directory names to HuggingFace-compatible output filenames
splits = {
'train': 'train.csv',
'val': 'validation.csv', # 'validation' is a recognized split name
'test': 'test.csv'
}
for split_dir, output_file in splits.items():
create_metadata_csv(split_dir, output_file)
print("\nMetadata CSV files created successfully!")
print("These files are compatible with HuggingFace's dataset viewer.")
if __name__ == '__main__':
main()
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