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from __future__ import annotations |
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import argparse |
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import math |
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import random |
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import shutil |
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from pathlib import Path |
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from typing import List |
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IMG_EXTS = {".jpg", ".jpeg", ".JPG", ".JPEG"} |
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def list_images(root: Path) -> List[Path]: |
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files: List[Path] = [] |
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for p in root.rglob("*"): |
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if p.is_file() and p.suffix in IMG_EXTS: |
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files.append(p) |
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return files |
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def clean_dir(p: Path) -> None: |
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if p.exists(): |
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for child in p.iterdir(): |
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if child.is_file(): |
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child.unlink() |
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else: |
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shutil.rmtree(child) |
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def copy_files(files: List[Path], dst_dir: Path) -> None: |
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dst_dir.mkdir(parents=True, exist_ok=True) |
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for f in files: |
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shutil.copy2(f, dst_dir / f.name) |
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def main(): |
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ap = argparse.ArgumentParser(description="Prepare train/val/test splits from a raw images folder (Route B).") |
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ap.add_argument("--src", type=Path, required=True, help="Root folder containing raw images (recursively)") |
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ap.add_argument("--dst", type=Path, default=Path("data"), help="Destination data folder containing splits") |
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ap.add_argument("--train", type=float, default=1.0, help="Train ratio (0..1)") |
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ap.add_argument("--val", type=float, default=0.0, help="Validation ratio (0..1)") |
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ap.add_argument("--test", type=float, default=0.0, help="Test ratio (0..1)") |
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ap.add_argument("--seed", type=int, default=42, help="Random seed") |
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ap.add_argument("--clean", action="store_true", help="Clean split folders before copying") |
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args = ap.parse_args() |
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total_ratio = args.train + args.val + args.test |
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if not (0.999 <= total_ratio <= 1.001): |
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raise SystemExit(f"Ratios must sum to 1.0; got {total_ratio}") |
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imgs = list_images(args.src) |
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if not imgs: |
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raise SystemExit(f"No JPEG images found under {args.src}") |
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random.seed(args.seed) |
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random.shuffle(imgs) |
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n = len(imgs) |
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n_train = int(math.floor(n * args.train)) |
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n_val = int(math.floor(n * args.val)) |
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n_test = n - n_train - n_val |
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train_files = imgs[:n_train] |
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val_files = imgs[n_train:n_train + n_val] |
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test_files = imgs[n_train + n_val:] |
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print(f"Total images: {n} -> train {len(train_files)}, val {len(val_files)}, test {len(test_files)}") |
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for split, files in (("train", train_files), ("validation", val_files), ("test", test_files)): |
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split_dir = args.dst / split |
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if args.clean: |
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clean_dir(split_dir) |
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if files: |
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copy_files(files, split_dir) |
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print(f"Copied {len(files)} files to {split_dir}") |
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try: |
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from scripts.extract_split_metadata import extract_split |
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except Exception: |
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import sys |
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here = Path(__file__).resolve().parent |
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if str(here) not in sys.path: |
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sys.path.insert(0, str(here)) |
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try: |
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from extract_split_metadata import extract_split |
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except Exception as e: |
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raise SystemExit(f"Failed to import extractor: {e}") |
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written = [] |
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for split in ("train", "validation", "test"): |
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split_dir = args.dst / split |
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if split_dir.exists(): |
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csv_path = extract_split(split_dir) |
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written.append(csv_path) |
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print("Done. Metadata files:") |
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for p in written: |
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print(p) |
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if __name__ == "__main__": |
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main() |
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