text: import tensorflow as tf
  import cv2
  import os, random
  import numpy as np
  
  class ImageData:
  
      def __init__(self, load_size, channels, augment_flag):
          self.load_size = load_size
          self.channels = channels
          self.augment_flag = augment_flag
  
      def image_processing(self, filename):
          x = tf.io.read_file(filename)
          x_decode = tf.image.decode_jpeg(x, channels=self.channels)
          img = tf.image.resize(x_decode, [self.load_size, self.load_size])
          img = tf.cast(img, tf.float32) / 127.5 - 1
  
          if self.augment_flag :
              augment_size = self.load_size + (30 if self.load_size == 256 else 15)
              p = random.random()
              if p > 0.5:
                  img = augmentation(img, augment_size)
  
          return img
  
  def load_test_data(image_path, size=256):
      img = cv2.imread(image_path, flags=cv2.IMREAD_COLOR)
      img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
  
      img = cv2.resize(img, dsize=(size, size))
  
      img = np.expand_dims(img, axis=0)
      img = img/127.5 - 1
  
      return img
  
  def augmentation(image, augment_size):
      seed = random.randint(0, 2 ** 31 - 1)
      ori_image_shape = tf.shape(image)
      image = tf.image.random_flip_left_right(image, seed=seed)
      image = tf.image.resize_images(image, [augment_size, augment_size])
      image = tf.random_crop(image, ori_image_shape, seed=seed)
      return image
  
  def save_images(images, size, image_path):
      return imsave(inverse_transform(images), size, image_path)
  
  def inverse_transform(images):
      return ((images+1.) / 2) * 255.0
  
  
  def imsave(images, size, path):
      images = merge(images, size)
      images = cv2.cvtColor(images.astype('uint8'), cv2.COLOR_RGB2BGR)
  
      return cv2.imwrite(path, images)
  
  def merge(images, size):
      h, w = images.shape[1], images.shape[2]
      img = np.zeros((h * size[0], w * size[1], 3))
      for idx, image in enumerate(images):
          i = idx % size[1]
          j = idx // size[1]
          img[h*j:h*(j+1), w*i:w*(i+1), :] = image
  
      return img
  
  def check_folder(log_dir):
      if not os.path.exists(log_dir):
          os.makedirs(log_dir)
      return log_dir
  
  def str2bool(x):
      return x.lower() in ('true')
  