Datasets:
Tasks:
Image Segmentation
Formats:
imagefolder
Sub-tasks:
semantic-segmentation
Size:
1K - 10K
ArXiv:
License:
| from pathlib import Path | |
| import csv | |
| import os | |
| # ====== SET YOUR ROOT DATA DIRECTORY HERE ====== | |
| # Example: | |
| # root_dir = Path("/home/yourname/datadir") | |
| root_dir = Path("/home/buddhiw/Downloads/SyntheticV5/SyntheticDatasetFINAL/Train/SyntheticGenV5/") | |
| train_dir = root_dir / "Train" | |
| output_csv = train_dir / "metadata.csv" | |
| domains = ["Urban", "Rural"] | |
| rows = [] | |
| missing_mask = 0 | |
| missing_mask_rgb = 0 | |
| total_images = 0 | |
| for domain in domains: | |
| image_dir = train_dir / domain / "images_png" | |
| mask_dir = train_dir / domain / "masks_png" | |
| mask_rgb_dir = train_dir / domain / "mask_rgb_png" | |
| if not image_dir.exists(): | |
| print(f"Skipping {domain}: image folder not found -> {image_dir}") | |
| continue | |
| image_files = sorted(image_dir.glob("*.png")) | |
| for image_path in image_files: | |
| total_images += 1 | |
| file_name = image_path.name | |
| mask_path = mask_dir / file_name | |
| mask_rgb_path = mask_rgb_dir / file_name | |
| if not mask_path.exists(): | |
| print(f"Missing mask file for: {image_path}") | |
| missing_mask += 1 | |
| continue | |
| if not mask_rgb_path.exists(): | |
| print(f"Missing RGB mask file for: {image_path}") | |
| missing_mask_rgb += 1 | |
| continue | |
| rows.append({ | |
| "image_file_name": f"{domain}/images_png/{file_name}", | |
| "mask_file_name": f"{domain}/masks_png/{file_name}", | |
| "mask_rgb_file_name": f"{domain}/mask_rgb_png/{file_name}", | |
| "domain": domain, | |
| "source_dataset": "LoveDA" | |
| }) | |
| # Save CSV | |
| with open(output_csv, "w", newline="", encoding="utf-8") as f: | |
| writer = csv.DictWriter( | |
| f, | |
| fieldnames=[ | |
| "image_file_name", | |
| "mask_file_name", | |
| "mask_rgb_file_name", | |
| "domain", | |
| "source_dataset" | |
| ] | |
| ) | |
| writer.writeheader() | |
| writer.writerows(rows) | |
| print("\nDone.") | |
| print(f"Total images found : {total_images}") | |
| print(f"Valid matched samples : {len(rows)}") | |
| print(f"Missing masks : {missing_mask}") | |
| print(f"Missing RGB masks : {missing_mask_rgb}") | |
| print(f"CSV saved to : {output_csv}") |