LAP/data_preparation/prepare_rsna_data.py

from typing import List, Tuple
import argparse
from os import makedirs, path, listdir
import numpy as np
import cv2
from multiprocessing import Pool


def resize_image(img_dir: str, img_save_dir: str, res: int) -> None:
    img = cv2.imread(img_dir)
    img = cv2.resize(img, dsize=(res, res))
    cv2.imwrite(img_save_dir, img)


if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument('kaggle_dataset_dir', type=str, help='download the dataset from https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data, extract it, and pass its path is this argument')
    parser.add_argument('resolution', type=int, help='The resolution required for your model, 224 for resnet, 299 for vanilla inception, 256 for modified inception')
    parser.add_argument('cores', type=int, help='The number of cores for multiprocessing.')
    args = parser.parse_args()

    save_dir = f'data/RSNA-Kaggle_R{args.resolution}'
    makedirs(save_dir, exist_ok=True)

    kaggle_dataset_dir = args.kaggle_dataset_dir
    assert path.exists(kaggle_dataset_dir), f'{kaggle_dataset_dir} does not exist!'

    # reading rsna images names
    rsna_imgs_path = path.join(kaggle_dataset_dir, 'stage_2_train_images')
    assert path.exists(rsna_imgs_path), 'Make sure there is a folder named stage_2_train_images in the passed kaggle_directory!'

    imgs_names = np.asarray(listdir(rsna_imgs_path))

    imgs_src = np.vectorize(lambda x: path.join(rsna_imgs_path, x))(imgs_names)
    imgs_dst = np.vectorize(lambda x: path.join(save_dir, x))(imgs_names)

    pool = Pool(args.cores)
    pool.starmap(resize_image, zip(imgs_src, imgs_dst, np.full((len(imgs_src),), args.resolution)))
    pool.close()