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#!/usr/bin/env python3
import os
import pandas as pd
from datasets import load_dataset
def row_to_dict(row, split_name):
return {
"image_uid": row["id"],
"age": int(row["metadata"]["age"]),
"sex": 1 if row["metadata"]["sex"].lower() == "male" else 2,
"image_path": os.path.abspath(row["nii_filepath"]),
"split": split_name
}
def main():
# Load the datasets
ds_train = load_dataset("radiata-ai/brain-structure", split="train", trust_remote_code=True)
ds_val = load_dataset("radiata-ai/brain-structure", split="validation", trust_remote_code=True)
ds_test = load_dataset("radiata-ai/brain-structure", split="test", trust_remote_code=True)
rows = []
# Process each split
for data_row in ds_train:
rows.append(row_to_dict(data_row, "train"))
for data_row in ds_val:
rows.append(row_to_dict(data_row, "validation"))
for data_row in ds_test:
rows.append(row_to_dict(data_row, "test"))
# Create a DataFrame and write it to CSV
df = pd.DataFrame(rows)
output_csv = "inputs.csv"
df.to_csv(output_csv, index=False)
print(f"CSV file created: {output_csv}")
if __name__ == "__main__":
main()
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