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import argparse
import glob
import json
import os
import monai
from sklearn.model_selection import train_test_split
def produce_sample_dict(line: str):
names = os.listdir(line)
seg, t1ce, t1, t2, flair = [], [], [], [], []
for name in names:
name = os.path.join(line, name)
if "_seg.nii" in name:
seg.append(name)
elif "_t1ce.nii" in name:
t1ce.append(name)
elif "_t1.nii" in name:
t1.append(name)
elif "_t2.nii" in name:
t2.append(name)
elif "_flair.nii" in name:
flair.append(name)
return {"label": seg[0], "image": t1ce + t1 + t2 + flair}
def produce_datalist(dataset_dir: str, train_size: int = 200):
"""
This function is used to split the dataset.
It will produce "train_size" number of samples for training, and the other samples
are divided equally into val and test sets.
"""
samples = sorted(glob.glob(os.path.join(dataset_dir, "*", "*"), recursive=True))
datalist = []
for line in samples:
datalist.append(produce_sample_dict(line))
train_list, other_list = train_test_split(datalist, train_size=train_size)
val_list, test_list = train_test_split(other_list, train_size=0.5)
return {"training": train_list, "validation": val_list, "testing": test_list}
def main(args):
"""
split the dataset and output the data list into a json file.
"""
data_file_base_dir = os.path.join(os.path.abspath(args.path), "training")
# produce deterministic data splits
monai.utils.set_determinism(seed=123)
datalist = produce_datalist(dataset_dir=data_file_base_dir, train_size=args.train_size)
with open(args.output, "w") as f:
json.dump(datalist, f)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--path",
type=str,
default="/workspace/data/medical/brats2018challenge",
help="root path of brats 2018 dataset.",
)
parser.add_argument(
"--output", type=str, default="configs/datalist.json", help="relative path of output datalist json file."
)
parser.add_argument("--train_size", type=int, default=200, help="number of training samples.")
args = parser.parse_args()
main(args)
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