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