text: import os
  import cv2
  import numpy as np
  import tensorflow as tf 
  import network
  import guided_filter
  from tqdm import tqdm
  
  
  
  def resize_crop(image):
      h, w, c = np.shape(image)
      if min(h, w) > 720:
          if h > w:
              h, w = int(720*h/w), 720
          else:
              h, w = 720, int(720*w/h)
      image = cv2.resize(image, (w, h),
                      interpolation=cv2.INTER_AREA)
      h, w = (h//8)*8, (w//8)*8
      image = image[:h, :w, :]
      return image
  
  
  
  
  def cartoonize(load_folder, save_folder, model_path):
      input_photo = tf.placeholder(tf.float32, [1, None, None, 3])
      network_out = network.unet_generator(input_photo)
      final_out = guided_filter.guided_filter(input_photo, network_out, r=1, eps=5e-3)
  
      all_vars = tf.trainable_variables()
      gene_vars = [var for var in all_vars if 'generator' in var.name]
      saver = tf.train.Saver(var_list=gene_vars)
      
      config = tf.ConfigProto()
      config.gpu_options.allow_growth = True
      sess = tf.Session(config=config)
  
      sess.run(tf.global_variables_initializer())
      saver.restore(sess, tf.train.latest_checkpoint(model_path))
      name_list = os.listdir(load_folder)
      for name in tqdm(name_list):
          try:
              load_path = os.path.join(load_folder, name)
              save_path = os.path.join(save_folder, name)
              image = cv2.imread(load_path)
              image = resize_crop(image)
              batch_image = image.astype(np.float32)/127.5 - 1
              batch_image = np.expand_dims(batch_image, axis=0)
              output = sess.run(final_out, feed_dict={input_photo: batch_image})
              output = (np.squeeze(output)+1)*127.5
              output = np.clip(output, 0, 255).astype(np.uint8)
              cv2.imwrite(save_path, output)
          except:
              print('cartoonize {} failed'.format(load_path))
  
  
  def cartoonizeImg():
      model_path = 'saved_models'
      load_folder = 'test_images'
      save_folder = 'cartoonized_images'
      if not os.path.exists(save_folder):
          os.mkdir(save_folder)
      cartoonize(load_folder, save_folder, model_path)
  
  # if __name__ == '__main__':
  #     model_path = 'saved_models'
  #     load_folder = 'test_images'
  #     save_folder = 'cartoonized_images'
  #     if not os.path.exists(save_folder):
  #         os.mkdir(save_folder)
  #     cartoonize(load_folder, save_folder, model_path)