text: import numpy as np
  import os, sys
  
  from tensorflow.keras.preprocessing.image import ImageDataGenerator
  from matplotlib import pyplot as plt
  from keras.applications.mobilenet_v2 import preprocess_input
  
  np.set_printoptions(threshold=sys.maxsize)
  
  
  def flipAndPreprocess(x):
      x = np.fliplr(x)
      # x = preprocess_input(x)
      return x
  
  
  preprocessingFunctions = [flipAndPreprocess]
  
  for preprocessingFunction in preprocessingFunctions:
      imageDataGen = ImageDataGenerator(preprocessing_function=preprocess_input, width_shift_range=30,
                                        height_shift_range=30,
                                        rotation_range=20, brightness_range=[0.90, 1.10],
                                        shear_range=10, fill_mode='constant', cval=0, zoom_range=0.075, horizontal_flip=True)
  
      generator = imageDataGen.flow_from_directory(
          '../../data/train/fungus/',
          target_size=(512, 512),
          batch_size=1,
          shuffle=False,
          class_mode=None)
  
      for i in range(100):
          batch = generator.next()
          image = batch[0]
          image += 1
          image *= 128
          image = image.astype(int)
          print(image[255, :])
          plt.imshow(image)
          plt.show()
  