import os import nibabel as nib import pandas as pd import numpy as np import torch import monai import torch.nn.functional as F from multiprocessing import Pool from tqdm import tqdm def read_nii_files(directory): """ Retrieve paths of all NIfTI files in the given directory. Args: directory (str): Path to the directory containing NIfTI files. Returns: list: List of paths to NIfTI files. """ nii_files = [] for root, dirs, files in os.walk(directory): for file in files: if file.endswith('1.nii.gz'): nii_files.append(os.path.join(root, file)) return nii_files def read_nii_data(file_path): """ Read NIfTI file data. Args: file_path (str): Path to the NIfTI file. Returns: np.ndarray: NIfTI file data. """ try: nii_img = nib.load(file_path) nii_data = nii_img.get_fdata() return nii_data except Exception as e: print(f"Error reading file {file_path}: {e}") return None def resize_array(array, current_spacing, target_spacing): """ Resize the array to match the target spacing. Args: array (torch.Tensor): Input array to be resized. current_spacing (tuple): Current voxel spacing (z_spacing, xy_spacing, xy_spacing). target_spacing (tuple): Target voxel spacing (target_z_spacing, target_x_spacing, target_y_spacing). Returns: np.ndarray: Resized array. """ # Calculate new dimensions original_shape = array.shape[2:] scaling_factors = [ current_spacing[i] / target_spacing[i] for i in range(len(original_shape)) ] new_shape = [ int(original_shape[i] * scaling_factors[i]) for i in range(len(original_shape)) ] # Resize the array resized_array = F.interpolate(array, size=new_shape, mode='trilinear', align_corners=False).cpu().numpy() return resized_array def process_file(file_path): """ Process a single NIfTI file. Args: file_path (str): Path to the NIfTI file. Returns: None """ monai_loader = monai.transforms.Compose( [ monai.transforms.LoadImaged(keys=['image']), monai.transforms.AddChanneld(keys=['image']), monai.transforms.Orientationd(axcodes="LPS", keys=['image']), # zyx # monai.transforms.Spacingd(keys=["image"], pixdim=(1, 1, 3), mode=("bilinear")), monai.transforms.CropForegroundd(keys=["image"], source_key="image"), monai.transforms.ToTensord(keys=["image"]), ] ) dictionary = monai_loader({'image':file_path}) img_data = dictionary['image'] file_name = os.path.basename(file_path) row = df[df['VolumeName'] == file_name] slope = float(row["RescaleSlope"].iloc[0]) intercept = float(row["RescaleIntercept"].iloc[0]) xy_spacing = float(row["XYSpacing"].iloc[0][1:][:-2].split(",")[0]) z_spacing = float(row["ZSpacing"].iloc[0]) # Define the target spacing values for SAT segmentation target_x_spacing = 1.0 target_y_spacing = 1.0 target_z_spacing = 3.0 current = (z_spacing, xy_spacing, xy_spacing) target = (target_z_spacing, target_x_spacing, target_y_spacing) img_data = slope * img_data + intercept img_data = img_data[0].numpy() img_data = img_data.transpose(2, 0, 1) tensor = torch.tensor(img_data) tensor = tensor.unsqueeze(0).unsqueeze(0) resized_array = resize_array(tensor, current, target) resized_array = resized_array[0][0] resized_array = resized_array.transpose(1,2,0) # print('resized:',resized_array.shape) # resized: (231, 387, 387) save_folder = "../upload_data/valid_preprocessed/" #save folder for preprocessed folder_path_new = os.path.join(save_folder, "valid_" + file_name.split("_")[1], "valid_" + file_name.split("_")[1] + file_name.split("_")[2]) #folder name for train or validation os.makedirs(folder_path_new, exist_ok=True) save_path = os.path.join(folder_path_new, file_name) # np.savez(save_path, resized_array) # Create an identity matrix image_nifti = nib.Nifti1Image(resized_array,affine = np.eye(4)) nib.save(image_nifti, save_path) # Example usage: if __name__ == "__main__": split_to_preprocess = '../src_data/valid' #select the validation or test split nii_files = read_nii_files(split_to_preprocess) df = pd.read_csv("../src_data/metadata/validation_metadata.csv") #select the metadata num_workers = 18 # Number of worker processes # # Process files using multiprocessing with tqdm progress bar with Pool(num_workers) as pool: list(tqdm(pool.imap(process_file, nii_files), total=len(nii_files)))