Delete processed_code/preprocess_ctrate_train.py

processed_code/preprocess_ctrate_train.py

@@ -1,148 +0,0 @@
-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)))