utils/download_swim.py

"""
download_swim.py
Streams and downloads the full paired dataset (images + label txt files) from a Hugging Face Hub repository.
It recursively processes all available chunk subfolders (e.g., '000', '001', ...) under given parent paths.
Features:
- Recursively discovers subdirs (chunks) using HfFileSystem
- Optionally flattens the directory structure by removing the deepest chunk level
- Saves each .png image with its corresponding .txt label
Use this script if you want to download the complete dataset for model training or offline access.
Usage:
    # Download all chunks (flattened)
    python utils/download_swim.py --output-dir ./SWiM --flatten
    # Download specific chunks
    python utils/download_swim.py --chunks 000 001 002 --flatten False
Arguments:
    --repo-id          Hugging Face dataset repository ID
    --images-parent    Parent directory for image chunks (e.g., Baseline/images/train)
    --labels-parent    Parent directory for label chunks (e.g., Baseline/labels/train)
    --output-dir       Where to save the downloaded dataset
    --flatten          Remove final 'chunk' subdir in output paths (default: True)
    --chunks           Specific chunk names (e.g., 000 001); omit to download all
"""



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

def enumerate_chunks(repo_id, images_parent):
    """
    Lists all immediate chunk subdirs under the images parent using HfFileSystem.
    Returns sorted list of subdir names (e.g. ['000', '001', ...]).
    """
    fs = HfFileSystem()
    repo_path = f"datasets/{repo_id}/{images_parent}"
    entries = fs.ls(repo_path, detail=True)
    subdirs = [entry['name'].split('/')[-1] for entry in entries if entry['type'] == 'directory']
    subdirs.sort()
    return subdirs

def sample_dataset(
    repo_id: str,
    images_parent: str,
    labels_parent: str,
    output_dir: str,
#    max_files: int = 500,
    flatten: bool,
    chunks: list = None
):
    total_downloaded = 0
    all_chunks = chunks
    if all_chunks is None:
        all_chunks = enumerate_chunks(repo_id, images_parent)
        print(f"Found chunks: {all_chunks}")
    for chunk in all_chunks:
        image_subdir = f"{images_parent}/{chunk}"
        label_subdir = f"{labels_parent}/{chunk}"

        # List only in the specified chunk
        image_files = list_repo_tree(
            repo_id=repo_id,
            path_in_repo=image_subdir,
            repo_type="dataset",
            recursive=True,
        )

        for img_file in tqdm(image_files, desc=f"Downloading {chunk}", leave=False):
            if not isinstance(img_file, RepoFile) or not img_file.path.lower().endswith(".png"):
                continue

            rel_path = Path(img_file.path).relative_to(image_subdir)
            label_path = f"{label_subdir}/{rel_path.with_suffix('.txt')}"

            if flatten:
                parts = img_file.path.split('/')
#                print(parts)
                # Remove the chunk dir (second last)
                flat_path = '/'.join(parts[:-2] + [parts[-1]])
                # For labels, also strip the chunk and substitute extension
                flat_label_path = flat_path.replace('.png', '.txt').replace('images', 'labels')
                local_image_path = Path(output_dir) / flat_path
                local_label_path = Path(output_dir) / flat_label_path
            else:
                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)

            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")
            try:
                with fsspec.open(image_url) as f:
                    image = Image.open(BytesIO(f.read()))
                    image.save(local_image_path)
                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)
                total_downloaded += 1
            except Exception as e:
                print(f"Failed {rel_path}: {e}")




    print(f"Downloaded {total_downloaded} 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, optionally across multiple chunks.")
    parser.add_argument("--repo-id", default="RiceD2KLab/SWiM-SpacecraftWithMasks", help="Hugging Face dataset repo ID.")
    parser.add_argument("--images-parent", default="Baseline/images", help="Parent directory for image chunks.")
    parser.add_argument("--labels-parent", default="Baseline/labels", help="Parent directory for label chunks.")
    parser.add_argument("--output-dir", default="./SWiM", help="Where to save sampled data.")
    #parser.add_argument("--count", type=int, default=500, help="How many samples to download in total.")
    parser.add_argument("--flatten",  action='store_true', help="Save all samples in a single folder without subdirectories.")
    parser.add_argument("--chunks", nargs="*", default=None, help="Specific chunk names to sample (e.g. 000 001). Leave empty to process all.")
    return parser.parse_args()

if __name__ == "__main__":
    args = parse_args()
    sample_dataset(
        repo_id=args.repo_id,
        images_parent=args.images_parent,
        labels_parent=args.labels_parent,
        output_dir=args.output_dir,
#        max_files=args.count,
        flatten=args.flatten,
        chunks=args.chunks
    )