data_load.py

import numpy as np
import tensorflow as tf
import nibabel as nib
from tensorflow.keras.utils import to_categorical

def load_img(img_files):
    ''' Load one image and its target form file
    '''
    N = len(img_files)
    # target
    y = nib.load(img_files[N-1]).get_fdata(dtype='float32')
    y = y[4:180,17:209,2:162]   
    X_norm = np.empty((182, 218, 182, 1))
    for channel in range(N-1):
        X = nib.load(img_files[channel]).get_fdata(dtype='float32')
        X_norm[:,:,:,channel] = X     
        
    X_norm = X_norm[4:180,17:209,2:162,:]
    
    return X_norm, y

class DataGenerator(tf.keras.utils.Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, batch_size=4, dim=(176,192,160), n_channels=1, n_classes=4, shuffle=True):
        'Initialization'
        self.list_IDs = list_IDs
        self.batch_size = batch_size
        self.dim = dim
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.shuffle = shuffle
        self.on_epoch_end()

    def __len__(self):
        'Denotes the number of batches per epoch'
        return int(np.ceil(len(self.list_IDs) / self.batch_size))

    def __getitem__(self, index):
        'Generate one batch of data'
        # Generate indexes of the batch
        indexes = self.indexes[index*self.batch_size:(index+1)*self.batch_size]

        # Find list of IDs
        list_IDs_temp = [self.list_IDs[k] for k in indexes]

        # Generate data     
        X, y = self.__data_generation(list_IDs_temp)

        if index == self.__len__()-1:
            self.on_epoch_end()
        
        return X, y

    def on_epoch_end(self):
        'Updates indexes after each epoch'
        self.indexes = np.arange(len(self.list_IDs))
        if self.shuffle == True:
            np.random.shuffle(self.indexes)
  
    def __data_generation(self, list_IDs_temp):
        'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels)
        # Initialization
        X = np.empty((self.batch_size, *self.dim, self.n_channels))
        y = np.empty((self.batch_size, *self.dim))

        # Generate data
        for i, IDs in enumerate(list_IDs_temp):
            # Store sample
            X[i], y[i] = load_img(IDs)
            
        return X.astype('float32'), to_categorical(y, self.n_classes)