extract.py

import os
import random
from pathlib import Path
import shutil
import zipfile
import argparse
import tempfile


def reorganize_dataset(
    zip_file: str,
    *,
    destination_dir: str = "./data",
    split_ratio: float = 0.8,
    random_seed: int = 42,
    remove_zip: bool = False,
) -> None:
    """Reorganize dataset into train and test directories.

    Args:
        zip_file (str): Path to the zip file.
        dest_dir (str, optional): Path to the destination directory. Defaults to './data'.
        train_ratio (float, optional): Ratio of data to be used for training. Defaults to 0.7.
        random_seed (int, optional): Random seed for reproducibility. Defaults to 42.
        remove_zip (bool, optional): Whether to remove the zip file after extraction. Defaults to False.

    Raises:
        ValueError: If the source directory or destination directory does not exist.
        ValueError: If the destination directory is not empty.
    """
    # Convert the destination directory to a Path object
    dest_dir = Path(destination_dir)

    # Check if the source directory exists
    if not Path(zip_file).is_file():
        raise ValueError(f"Source directory '{zip_file}' does not exist.")

    # Check if the destination directory is empty
    if dest_dir.exists() and any(dest_dir.iterdir()):
        raise ValueError(f"Destination directory '{dest_dir}' is not empty.")

    # Set the random seed for reproducibility
    random.seed(random_seed)

    # Create train and test directories in the destination directory
    train_dir = dest_dir / "train"
    test_dir = dest_dir / "test"
    train_dir.mkdir(parents=True, exist_ok=True)
    test_dir.mkdir(parents=True, exist_ok=True)

    # Extract the zip file to a temporary directory
    with tempfile.TemporaryDirectory() as temp_dir:
        with zipfile.ZipFile(zip_file, "r") as zip_ref:
            zip_ref.extractall(temp_dir)

        # Navigate to the animals directory inside the extracted files
        root_dir = Path(temp_dir) / "animals" / "animals"

        # Iterate through each animal directory
        for animal_path in root_dir.iterdir():
            if animal_path.is_dir() and animal_path.name not in ["train", "test"]:
                # Create corresponding directories in train and test
                (train_dir / animal_path.name).mkdir(parents=True, exist_ok=True)
                (test_dir / animal_path.name).mkdir(parents=True, exist_ok=True)

                # Get all files in the animal directory
                files = [file for file in animal_path.iterdir() if file.is_file()]
                random.shuffle(files)

                # Split files into train and test sets
                split_index = int(len(files) * split_ratio)
                train_files = files[:split_index]
                test_files = files[split_index:]

                # Move files to train directory
                for file in train_files:
                    dst = train_dir / animal_path.name / file.name
                    shutil.move(str(file), str(dst))

                # Move files to test directory
                for file in test_files:
                    dst = test_dir / animal_path.name / file.name
                    shutil.move(str(file), str(dst))

    # Remove the zip file if the flag is set to True
    if remove_zip:
        os.remove(zip_file)

    print(f"Dataset reorganization complete! (Seed: {random_seed})")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Reorganize dataset.")
    parser.add_argument("zip_file", type=str, help="Path to the zip file.")
    parser.add_argument(
        "--destination-dir",
        type=str,
        default="./data",
        help="Path to the destination directory.",
    )
    parser.add_argument(
        "--split-ratio",
        type=float,
        default=0.8,
        help="Ratio of data to be used for training.",
    )
    parser.add_argument(
        "--random-seed", type=int, default=42, help="Random seed for reproducibility."
    )
    parser.add_argument(
        "--remove-zip",
        action="store_true",
        help="Whether to remove the source zip archive file after extraction.",
    )

    args = parser.parse_args()

    reorganize_dataset(
        args.zip_file,
        destination_dir=args.destination_dir,
        split_ratio=args.split_ratio,
        random_seed=args.random_seed,
        remove_zip=args.remove_zip,
    )