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""" |
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sample_swim.py |
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Streams and saves a sample of paired images and labels from a Hugging Face dataset repository. |
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Default configuration: |
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- Repo: "JeffreyJsam/SWiM-SpacecraftWithMasks" |
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- Image subdir: "Baseline/images/val/000" |
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- Label subdir: "Baseline/labels/val/000" |
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- Saves the first 500 matched image/txt files by default. |
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This script is useful for quick local inspection, prototyping, or lightweight evaluation |
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without downloading the full dataset. |
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Usage: |
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python utils/sample_swim.py --output-dir ./samples --count 100 |
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Arguments: |
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--repo-id Hugging Face dataset repository ID |
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--image-subdir Path to image subdirectory inside the dataset repo |
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--label-subdir Path to corresponding label subdirectory |
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--output-dir Directory to save downloaded files |
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--count Number of samples to download |
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""" |
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import argparse |
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from io import BytesIO |
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from pathlib import Path |
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from huggingface_hub import list_repo_tree, hf_hub_url |
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from huggingface_hub.hf_api import RepoFile |
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import fsspec |
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from PIL import Image |
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from tqdm import tqdm |
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def sample_dataset( |
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repo_id: str, |
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image_subdir: str, |
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label_subdir: str, |
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output_dir: str, |
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max_files: int = 500, |
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): |
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image_files = list_repo_tree( |
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repo_id=repo_id, |
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path_in_repo=image_subdir, |
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repo_type="dataset", |
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recursive=True |
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) |
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count = 0 |
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for img_file in tqdm(image_files, desc="Downloading samples"): |
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if not isinstance(img_file, RepoFile) or not img_file.path.lower().endswith((".png")): |
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continue |
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rel_path = Path(img_file.path).relative_to(image_subdir) |
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label_path = f"{label_subdir}/{rel_path.with_suffix('.txt')}" |
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image_url = hf_hub_url(repo_id=repo_id, filename=img_file.path, repo_type="dataset") |
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label_url = hf_hub_url(repo_id=repo_id, filename=label_path, repo_type="dataset") |
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local_image_path = Path(output_dir) / img_file.path |
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local_label_path = Path(output_dir) / label_path |
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local_image_path.parent.mkdir(parents=True, exist_ok=True) |
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local_label_path.parent.mkdir(parents=True, exist_ok=True) |
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try: |
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with fsspec.open(image_url) as f: |
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image = Image.open(BytesIO(f.read())) |
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image.save(local_image_path) |
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with fsspec.open(label_url) as f: |
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txt_content = f.read() |
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with open(local_label_path, "wb") as out_f: |
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out_f.write(txt_content) |
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count += 1 |
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except Exception as e: |
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print(f" Failed {rel_path}: {e}") |
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if count >= max_files: |
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break |
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print(f" Downloaded {count} image/txt pairs.") |
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print(f" Saved under: {Path(output_dir).resolve()}") |
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def parse_args(): |
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parser = argparse.ArgumentParser(description="Stream and sample paired images + txt labels from a Hugging Face folder-structured dataset.") |
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parser.add_argument("--repo-id", required=False, default = "RiceD2KLab/SWiM-SpacecraftWithMasks",help="Hugging Face dataset repo ID.") |
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parser.add_argument("--image-subdir", required=False, default = "Baseline/images/val/000", help="Subdirectory path for images.") |
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parser.add_argument("--label-subdir", required=False, default="Baseline/labels/val/000", help="Subdirectory path for txt masks.") |
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parser.add_argument("--output-dir", default="./Sampled-SWiM", help="Where to save sampled data.") |
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parser.add_argument("--count", type=int, default=500, help="How many samples to download.") |
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return parser.parse_args() |
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if __name__ == "__main__": |
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args = parse_args() |
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sample_dataset( |
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repo_id=args.repo_id, |
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image_subdir=args.image_subdir, |
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label_subdir=args.label_subdir, |
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output_dir=args.output_dir, |
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max_files=args.count, |
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) |
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