Resizing images in Keras ImageDataGenerator flow methods

For large training dataset, performing transformations such as resizing on the entire training data is very memory consuming. As Keras did in ImageDataGenerator, it's better to do it batch by batch. As far as I know, there're 2 ways to achieve this other than operating the whole dataset:

1. You can use Lambda Layer to create a layer and then feed original training data to it. The output is the resized you need.

Here is the sample code if you use TensorFlow as the backend of Keras:

original_dim = (32, 32, 3)
target_size = (64, 64)
input = keras.layers.Input(original_dim)
x = tf.keras.layers.Lambda(lambda image: tf.image.resize(image, target_size))(input)

2. As @Retardust mentioned, maybe you can customize your own ImageDataGenerator as well as the preprocessing_function.

flow_from_directory(directory) generates augmented images from directory with arbitrary collection of images. So there is need of parameter target_size to make all images of same shape.

While flow(X, y) augments images which are already stored in a sequence in X which is nothing but numpy matrix and can be easily preprocessed/resized before passing to flow. So no need for target_size parameter. As for resizing I prefer using scipy.misc.imresize over PIL.Image resize, or cv2.resize as it can operate on numpy image data.

import scipy
new_shape = (28,28,3)
X_train_new = np.empty(shape=(X_train.shape[0],)+new_shape)
for idx in xrange(X_train.shape[0]):
    X_train_new[idx] = scipy.misc.imresize(X_train[idx], new_shape)