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from light_training.preprocessing.preprocessors.preprocessor_mri import MultiModalityPreprocessor
import argparse
data_filename = ["t2w.nii.gz",
"t2f.nii.gz",
"t1n.nii.gz",
"t1c.nii.gz"]
seg_filename = "seg.nii.gz"
def _parse_spacing(s: str):
parts = [p.strip() for p in s.split(",") if p.strip()]
if len(parts) != 3:
raise ValueError(f"output_spacing should be like '1,1,1', got: {s}")
return [float(parts[0]), float(parts[1]), float(parts[2])]
def main():
parser = argparse.ArgumentParser(description="BraTS2023 preprocessing (resample/normalization/cropping).")
parser.add_argument(
"--base_dir",
type=str,
default="./data/raw_data/BraTS2023/",
help="Base directory that contains the BraTS2023 image_dir folder.",
)
parser.add_argument(
"--image_dir",
type=str,
default="ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData",
help="Folder name under base_dir.",
)
parser.add_argument(
"--output_dir",
type=str,
default="./data/fullres/train/",
help="Output directory for preprocessed npz/npy/pkl files.",
)
parser.add_argument(
"--output_spacing",
type=str,
default="1,1,1",
help="Target spacing, e.g. '1,1,1'.",
)
parser.add_argument(
"--num_processes",
type=int,
default=8,
help="Number of worker processes for preprocessing.",
)
parser.add_argument(
"--only_plan",
action="store_true",
help="Only run planning (statistics) and exit.",
)
parser.add_argument(
"--skip_plan",
action="store_true",
help="Skip planning step.",
)
args = parser.parse_args()
preprocessor = MultiModalityPreprocessor(
base_dir=args.base_dir,
image_dir=args.image_dir,
data_filenames=data_filename,
seg_filename=seg_filename,
)
if not args.skip_plan:
preprocessor.run_plan()
if args.only_plan:
return
out_spacing = _parse_spacing(args.output_spacing)
preprocessor.run(
output_spacing=out_spacing,
output_dir=args.output_dir,
all_labels=[1, 2, 3],
num_processes=args.num_processes,
)
if __name__ == "__main__":
main()
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