How to change pre_processing function execution order for ImageDataGenerator at Keras?

Question

I am using Keras's "ImageDataGenerator" class for data augmentation. Since the image has the bounding box of the relevant object, I want to crop the image to the relevant part before augmenting it. The class has an argument named "preprocessing_function" among its arguments and allows us to implement the desired function after augmentation and resizing. I am asking for this to happen the opposite. First, let the function run, then the augmentation takes place. How can I implement that to the code?

tf.keras.preprocessing.image.ImageDataGenerator(
    featurewise_center=False,
    samplewise_center=False,
    featurewise_std_normalization=False,
    samplewise_std_normalization=False,
    zca_whitening=False,
    zca_epsilon=1e-06,
    rotation_range=0,
    width_shift_range=0.0,
    height_shift_range=0.0,
    brightness_range=None,
    shear_range=0.0,
    zoom_range=0.0,
    channel_shift_range=0.0,
    fill_mode="nearest",
    cval=0.0,
    horizontal_flip=False,
    vertical_flip=False,
    rescale=None,
    preprocessing_function=None,
    data_format=None,
    validation_split=0.0,
    dtype=None,
)

preprocessing_function: a function that will be applied to each input. The function will run after the image is resized and augmented. The function should take one argument: one image (Numpy tensor with rank 3) and should output a Numpy tensor with the same shape.

Answer 1

Keras team members said that the ImageDataGenerator class is legacy. They suggest me to use transformation layers. They can be used anytime while training.

Example usage of transformation layers: Keras Transformation layers example page

Github Issue (Closed): GitHub Issues