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processed_code/preprocess_ctrate_train.py

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+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'):
+                # /mnt/petrelfs/share_data/zhangxiaoman/DATA/CT-RATE/dataset/train_preprocessed
+                # preprocessed_file = file.replace('/mnt/petrelfs/share_data/zhangxiaoman/DATA/CT-RATE/dataset/train','/mnt/petrelfs/share_data/zhangxiaoman/DATA/CT-RATE/dataset/train_preprocessed')
+                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/train_preprocessed/" #save folder for preprocessed
+    folder_path_new = os.path.join(save_folder, "train_" + file_name.split("_")[1], "train_" + 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/train' #select the validation or test split
+    nii_files = read_nii_files(split_to_preprocess)
+    print(len(nii_files))  
+    
+    df = pd.read_csv("../src_data/metadata/train_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)))