utils/sample_swim.py

"""
sample_swim.py

Streams and saves a sample of paired images and labels from a Hugging Face dataset repository.

Default configuration:
- Repo: "JeffreyJsam/SWiM-SpacecraftWithMasks"
- Image subdir: "Baseline/images/val/000"
- Label subdir: "Baseline/labels/val/000"
- Saves the first 500 matched image/txt files by default.

This script is useful for quick local inspection, prototyping, or lightweight evaluation
without downloading the full dataset.

Usage:
    python utils/sample_swim.py --output-dir ./samples --count 100

Arguments:
    --repo-id          Hugging Face dataset repository ID
    --image-subdir     Path to image subdirectory inside the dataset repo
    --label-subdir     Path to corresponding label subdirectory
    --output-dir       Directory to save downloaded files
    --count            Number of samples to download
"""

import argparse
from io import BytesIO
from pathlib import Path
from huggingface_hub import list_repo_tree, hf_hub_url
from huggingface_hub.hf_api import RepoFile
import fsspec
from PIL import Image
from tqdm import tqdm

def sample_dataset(
    repo_id: str,
    image_subdir: str,
    label_subdir: str,
    output_dir: str,
    max_files: int = 500,
):


    
    image_files = list_repo_tree(
        repo_id=repo_id,
        path_in_repo=image_subdir,
        repo_type="dataset",
        recursive=True
    )

    count = 0
    for img_file in tqdm(image_files, desc="Downloading samples"):
        if not isinstance(img_file, RepoFile) or not img_file.path.lower().endswith((".png")):
            continue

        # Relative path after the image_subdir (e.g., img_0001.png)
        rel_path = Path(img_file.path).relative_to(image_subdir)
        label_path = f"{label_subdir}/{rel_path.with_suffix('.txt')}"  # Change extension to .txt

        image_url = hf_hub_url(repo_id=repo_id, filename=img_file.path, repo_type="dataset")
        label_url = hf_hub_url(repo_id=repo_id, filename=label_path, repo_type="dataset")

        local_image_path = Path(output_dir) / img_file.path
        local_label_path = Path(output_dir) / label_path

        local_image_path.parent.mkdir(parents=True, exist_ok=True)
        local_label_path.parent.mkdir(parents=True, exist_ok=True)

        try:
            # Download and save the image
            with fsspec.open(image_url) as f:
                image = Image.open(BytesIO(f.read()))
                image.save(local_image_path)

            # Download and save the corresponding .txt label
            with fsspec.open(label_url) as f:
                txt_content = f.read()
                with open(local_label_path, "wb") as out_f:
                    out_f.write(txt_content)

#            print(f"[{count+1}]  {rel_path} and {rel_path.with_suffix('.txt')}")
            count += 1
        except Exception as e:
            print(f" Failed {rel_path}: {e}")

        if count >= max_files:
            break

    print(f" Downloaded {count} image/txt pairs.")
    print(f" Saved under: {Path(output_dir).resolve()}")

def parse_args():
    parser = argparse.ArgumentParser(description="Stream and sample paired images + txt labels from a Hugging Face folder-structured dataset.")
    parser.add_argument("--repo-id", required=False, default = "RiceD2KLab/SWiM-SpacecraftWithMasks",help="Hugging Face dataset repo ID.")
    parser.add_argument("--image-subdir", required=False, default = "Baseline/images/val/000", help="Subdirectory path for images.")
    parser.add_argument("--label-subdir", required=False, default="Baseline/labels/val/000", help="Subdirectory path for txt masks.")
    parser.add_argument("--output-dir", default="./Sampled-SWiM", help="Where to save sampled data.")
    parser.add_argument("--count", type=int, default=500, help="How many samples to download.")
    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()
    sample_dataset(
        repo_id=args.repo_id,
        image_subdir=args.image_subdir,
        label_subdir=args.label_subdir,
        output_dir=args.output_dir,
        max_files=args.count,
    )