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import os
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import numpy as np
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from PIL import Image
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import concurrent.futures
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from tqdm import tqdm
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from collections import Counter
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import unicodedata
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import monai.transforms as mtf
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from multiprocessing import Pool
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from unidecode import unidecode
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input_dir = 'PATH/M3D_Cap/ct_case/'
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output_dir = 'PATH/M3D_Cap_npy/ct_case/'
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subfolders = [folder for folder in os.listdir(input_dir) if os.path.isdir(os.path.join(input_dir, folder))]
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transform = mtf.Compose([
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mtf.CropForeground(),
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mtf.Resize(spatial_size=[32, 256, 256], mode="bilinear")
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])
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def process_subfolder(subfolder):
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output_id_folder = os.path.join(output_dir, subfolder)
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input_id_folder = os.path.join(input_dir, subfolder)
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os.makedirs(output_id_folder, exist_ok=True)
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for subsubfolder in os.listdir(input_id_folder):
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if subsubfolder.endswith('.txt'):
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text_path = os.path.join(input_dir, subfolder, subsubfolder)
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with open(text_path, 'r') as file:
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text_content = file.read()
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search_text = "study_findings:"
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index = text_content.find(search_text)
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if index != -1:
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filtered_text = text_content[index + len(search_text):].replace("\n", " ").strip()
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else:
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print("Specified string not found")
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filtered_text = text_content.replace("\n", " ").strip()
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if len(filtered_text.replace("\n", "").replace(" ", "")) < 5:
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search_text = "discussion:"
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index = text_content.find(search_text)
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if index != -1:
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filtered_text = text_content[index + len(search_text):].replace("\n", " ").strip()
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else:
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print("Specified string not found")
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filtered_text = text_content.replace("\n", " ").strip()
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if len(filtered_text.replace("\n", "").replace(" ", "")) < 5:
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filtered_text = text_content.replace("\n", " ").strip()
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new_text_path = os.path.join(output_dir, subfolder, subsubfolder)
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with open(new_text_path, 'w') as new_file:
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new_file.write(filtered_text)
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subsubfolder_path = os.path.join(input_dir, subfolder, subsubfolder)
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if os.path.isdir(subsubfolder_path):
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subsubfolder = unidecode(subsubfolder)
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output_path = os.path.join(output_dir, subfolder, f'{subsubfolder}.npy')
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image_files = [file for file in os.listdir(subsubfolder_path) if
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file.endswith('.jpeg') or file.endswith('.png')]
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if len(image_files) == 0:
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continue
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image_files.sort(key=lambda x: int(os.path.splitext(x)[0]))
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images_3d = []
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for image_file in image_files:
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image_path = os.path.join(subsubfolder_path, image_file)
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try:
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img = Image.open(image_path)
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img = img.convert("L")
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img_array = np.array(img)
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img_array = img_array.astype(np.float32) / 255.0
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images_3d.append(img_array[None])
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except:
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print("This image is error: ", image_path)
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images_3d_pure = []
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try:
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img_shapes = [img.shape for img in images_3d]
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item_counts = Counter(img_shapes)
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most_common_shape = item_counts.most_common(1)[0][0]
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for img in images_3d:
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if img.shape == most_common_shape:
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images_3d_pure.append(img)
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final_3d_image = np.vstack(images_3d_pure)
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image = final_3d_image[np.newaxis, ...]
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image = image - image.min()
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image = image / np.clip(image.max(), a_min=1e-8, a_max=None)
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img_trans = transform(image)
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np.save(output_path, img_trans)
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except:
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print([img.shape for img in images_3d])
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print("This folder is vstack error: ", output_path)
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with Pool(processes=32) as pool:
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with tqdm(total=len(subfolders), desc="Processing") as pbar:
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for _ in pool.imap_unordered(process_subfolder, subfolders):
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pbar.update(1)
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