| | """ |
| | Apply CT windowing parameter from DL_info.csv to Images_png |
| | """ |
| |
|
| | import os |
| | import cv2 |
| |
|
| | import numpy as np |
| | import pandas as pd |
| |
|
| | from glob import glob |
| | from tqdm import tqdm |
| |
|
| |
|
| | dir_in = '../Images_png' |
| | dir_out = '../Images_png_wn' |
| | info_fn = '../DL_info.csv' |
| |
|
| | if not os.path.exists(dir_out): |
| | os.mkdir(dir_out) |
| |
|
| | dl_info = pd.read_csv(info_fn) |
| |
|
| | def clip_and_normalize(np_image: np.ndarray, |
| | clip_min: int = -150, |
| | clip_max: int = 250 |
| | ) -> np.ndarray: |
| | np_image = np.clip(np_image, clip_min, clip_max) |
| | np_image = (np_image - clip_min) / (clip_max - clip_min) |
| | return np_image |
| |
|
| |
|
| | for idx, row in tqdm(dl_info.iterrows(), total=len(dl_info)): |
| |
|
| | folder = row['File_name'].rsplit('_', 1)[0] |
| | images = sorted(glob(f'{dir_in}/{folder}/*.png')) |
| |
|
| | if not os.path.exists(f'{dir_out}/{folder}'): |
| | os.mkdir(f'{dir_out}/{folder}') |
| | DICOM_windows = [float(value.strip()) for value in row['DICOM_windows'].split(',')] |
| |
|
| | for im in images: |
| | try: |
| | image = cv2.imread(im, cv2.IMREAD_UNCHANGED) |
| | image = image.astype('int32') - 32768 |
| | image = clip_and_normalize(image, *DICOM_windows) |
| | image = (image*255).astype('uint8') |
| | cv2.imwrite(f'{dir_out}/{folder}/{os.path.basename(im)}', image) |
| | except AttributeError: |
| | |
| | |
| | |
| | print(f'Conversion failed: {im}') |
| | continue |
| |
|