tsha-mri-tumor-labeling/kimo/3-inference.py

# done with GWJU7LPC-20201007

import logging
import os
import shutil

from monai.apps.nnunet import nnUNetV2Runner

import yaml

DATASET="Dataset2602_BraTS-CK"
YAML   ="2602.yaml"

DST_DIR="/mnt/t24/Public/kimo/raw/ensemble_predictions_postprocessed"

def copytree(src, dst):
    os.makedirs(dst, exist_ok=True)
    for root, dirs, files in os.walk(src):
        for file in files:
            src_file = os.path.join(root, file)
            dst_file = os.path.join(dst, file)
            shutil.copyfile(src_file, dst_file)


def main():
    FORMAT = '%(asctime)s [%(filename)s:%(lineno)d] %(message)s'
    logging.basicConfig(
        level=logging.INFO,
        format=FORMAT,
        handlers=[
            logging.StreamHandler(),
            # logging.FileHandler(__file__.replace('.py','.%s.log'%str(datetime.datetime.now()).replace(':','')), encoding='utf-8')
            logging.FileHandler(__file__.replace('.py','.log'), encoding='utf-8')
        ]
    )

    with open(YAML, "r") as f:
        config = yaml.safe_load(f)

    PREPROCESSED_DIR = config["nnunet_preprocessed"]
    RAW_DIR          = config["nnunet_raw"]
    RESULTS_DIR      = config["nnunet_results"]


    IMAGES_TS = os.path.join(RAW_DIR, DATASET, "imagesTs")
    IMAGES_TS_DONE = os.path.join(RAW_DIR, DATASET, "imagesTs_done")

    POSTPROCESSED_DIR = os.path.join(RESULTS_DIR, DATASET, "ensemble_predictions_postprocessed")

    os.makedirs(IMAGES_TS_DONE, exist_ok=True)
    if os.path.exists(POSTPROCESSED_DIR):
        processed_files = [f for f in os.listdir(POSTPROCESSED_DIR) if f.endswith('.nii.gz')]
        processed_ids = [f.replace('.nii.gz', '') for f in processed_files]
        for pid in processed_ids:
            for mod in range(10): # support up to 10 modalities
                mod_file = f"{pid}_{mod:04d}.nii.gz"
                src_path = os.path.join(IMAGES_TS, mod_file)
                dst_path = os.path.join(IMAGES_TS_DONE, mod_file)
                if os.path.exists(src_path):
                    shutil.move(src_path, dst_path) 

    # clean up
    shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_2d', ignore_errors=True)
    shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_3d_fullres', ignore_errors=True)
    shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_3d_lowres', ignore_errors=True)
    shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/ensemble_predictions', ignore_errors=True)
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/ensemble_predictions_postprocessed', ignore_errors=True)

    runner = nnUNetV2Runner(YAML)
    try:
        runner.predict_ensemble_postprocessing(
            # num_processes_preprocessing=1,
            # num_processes_segmentation_export=1
        )
    except Exception as e:
        logging.exception(e)
        exit()

    copytree(f'{RESULTS_DIR}/{DATASET}/ensemble_predictions_postprocessed', DST_DIR)

    # exit()

    # clean up
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_2d', ignore_errors=True)
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_3d_fullres', ignore_errors=True)
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/pred_3d_lowres', ignore_errors=True)
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/ensemble_predictions', ignore_errors=True)
    # shutil.rmtree(f'{RESULTS_DIR}/{DATASET}/ensemble_predictions_postprocessed', ignore_errors=True)

if __name__ == "__main__":
    main()