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""" |
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Apply CT windowing parameter from DL_info.csv to Images_png |
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""" |
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import os |
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import cv2 |
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import numpy as np |
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import pandas as pd |
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from glob import glob |
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from tqdm import tqdm |
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dir_in = '/path/to/DeepLesion/Images_png' |
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dir_out = './Keyslices_1bbox' |
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info_fn = './DL_info.csv' |
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if not os.path.exists(dir_out): |
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os.mkdir(dir_out) |
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dl_info = pd.read_csv(info_fn) |
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def clip_and_normalize(np_image: np.ndarray, |
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clip_min: int = -150, |
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clip_max: int = 250 |
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) -> np.ndarray: |
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np_image = np.clip(np_image, clip_min, clip_max) |
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np_image = (np_image - clip_min) / (clip_max - clip_min) |
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return np_image |
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def draw_bounding_box(image, bbox): |
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if len(image.shape) == 2: |
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image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR) |
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x1, y1, x2, y2 = bbox |
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cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) |
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return image |
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for idx, row in tqdm(dl_info.iterrows(), total=len(dl_info)): |
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folder, filename = row['File_name'].rsplit('_', 1) |
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image_file = f'{dir_in}/{folder}/{filename}' |
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DICOM_windows = [float(value.strip()) for value in row['DICOM_windows'].split(',')] |
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bbox = [int(float(value.strip())) for value in row['Bounding_boxes'].split(',')] |
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try: |
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image = cv2.imread(image_file, cv2.IMREAD_UNCHANGED) |
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image = image.astype('int32') - 32768 |
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image = clip_and_normalize(image, *DICOM_windows) |
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image = (image * 255).astype('uint8') |
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image = draw_bounding_box(image, bbox) |
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cv2.imwrite(f'{dir_out}/lesion_{idx}.png', image) |
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except AttributeError: |
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print(f'Conversion failed: {image_file}') |
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continue |
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