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"""
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 = '/path/to/DeepLesion/Images_png'
dir_out = './Keyslices_1bbox'
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
def draw_bounding_box(image, bbox):
if len(image.shape) == 2:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
x1, y1, x2, y2 = bbox
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
return image
# import matplotlib.pyplot as plt
for idx, row in tqdm(dl_info.iterrows(), total=len(dl_info)):
folder, filename = row['File_name'].rsplit('_', 1)
image_file = f'{dir_in}/{folder}/{filename}'
DICOM_windows = [float(value.strip()) for value in row['DICOM_windows'].split(',')]
bbox = [int(float(value.strip())) for value in row['Bounding_boxes'].split(',')]
try:
image = cv2.imread(image_file, cv2.IMREAD_UNCHANGED)
image = image.astype('int32') - 32768
image = clip_and_normalize(image, *DICOM_windows)
image = (image * 255).astype('uint8')
image = draw_bounding_box(image, bbox)
# plt.imshow(image)
# plt.show()
cv2.imwrite(f'{dir_out}/lesion_{idx}.png', image)
except AttributeError:
# Broken Images
# 001821_07_01/372.png
# 002161_04_02/116.png
print(f'Conversion failed: {image_file}')
continue
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