feat: add extraction script (#4)

- feat: add extraction script (59e608f626de5a2c8f7c9265caf84b6867bdac7c)

extract.py

@@ -0,0 +1,124 @@
+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,
+    )