text: import os
  from io import BytesIO
  import tarfile
  import tempfile
  from six.moves import urllib
  
  import numpy as np
  from PIL import Image
  import cv2, pdb, glob, argparse
  
  import tensorflow as tf
  
  
  
  class DeepLabModel(object):
  	"""Class to load deeplab model and run inference."""
  
  	INPUT_TENSOR_NAME = 'ImageTensor:0'
  	OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'
  	INPUT_SIZE = 513
  	FROZEN_GRAPH_NAME = 'frozen_inference_graph'
  
  	def __init__(self, tarball_path):
  		#"""Creates and loads pretrained deeplab model."""
  		self.graph = tf.Graph()
  		graph_def = None
  		# Extract frozen graph from tar archive.
  		tar_file = tarfile.open(tarball_path)
  		for tar_info in tar_file.getmembers():
  			if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):
  				file_handle = tar_file.extractfile(tar_info)
  				graph_def = tf.GraphDef.FromString(file_handle.read())
  				break
  
  		tar_file.close()
  
  		if graph_def is None:
  			raise RuntimeError('Cannot find inference graph in tar archive.')
  
  		with self.graph.as_default():
  			tf.import_graph_def(graph_def, name='')
  
  		# Hack
  		config = tf.ConfigProto()
  		#config.gpu_options.per_process_gpu_memory_fraction = 0.9
  		config.gpu_options.allow_growth = True
  
  		self.sess = tf.Session(graph=self.graph, config=config)
  
  	def run(self, image):
  		"""Runs inference on a single image.
  
  		Args:
  		  image: A PIL.Image object, raw input image.
  
  		Returns:
  		  resized_image: RGB image resized from original input image.
  		  seg_map: Segmentation map of `resized_image`.
  		"""
  		width, height = image.size
  		resize_ratio = 1.0 * self.INPUT_SIZE / max(width, height)
  		target_size = (int(resize_ratio * width), int(resize_ratio * height))
  		resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)
  		batch_seg_map = self.sess.run(
  			self.OUTPUT_TENSOR_NAME,
  			feed_dict={self.INPUT_TENSOR_NAME: [np.asarray(resized_image)]})
  		seg_map = batch_seg_map[0]
  		return resized_image, seg_map
  
  def create_pascal_label_colormap():
  	"""Creates a label colormap used in PASCAL VOC segmentation benchmark.
  
  	Returns:
  	A Colormap for visualizing segmentation results.
  	"""
  	colormap = np.zeros((256, 3), dtype=int)
  	ind = np.arange(256, dtype=int)
  
  	for shift in reversed(range(8)):
  		for channel in range(3):
  		  colormap[:, channel] |= ((ind >> channel) & 1) << shift
  		ind >>= 3
  
  	return colormap
  
  def label_to_color_image(label):
  	"""Adds color defined by the dataset colormap to the label.
  
  	Args:
  	label: A 2D array with integer type, storing the segmentation label.
  
  	Returns:
  	result: A 2D array with floating type. The element of the array
  	  is the color indexed by the corresponding element in the input label
  	  to the PASCAL color map.
  
  	Raises:
  	ValueError: If label is not of rank 2 or its value is larger than color
  	  map maximum entry.
  	"""
  	if label.ndim != 2:
  		raise ValueError('Expect 2-D input label')
  
  	colormap = create_pascal_label_colormap()
  
  	if np.max(label) >= len(colormap):
  		raise ValueError('label value too large.')
  
  	return colormap[label]
  
  
  
  parser = argparse.ArgumentParser(description='Deeplab Segmentation')
  parser.add_argument('-i', '--input_dir', type=str, required=True,help='Directory to save the output results. (required)')
  args=parser.parse_args()
  
  dir_name=args.input_dir;
  
  
  ## setup ####################
  
  LABEL_NAMES = np.asarray([
  	'background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
  	'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
  	'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tv'
  ])
  
  FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)
  FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)
  
  
  MODEL_NAME = 'xception_coco_voctrainval'  # @param ['mobilenetv2_coco_voctrainaug', 'mobilenetv2_coco_voctrainval', 'xception_coco_voctrainaug', 'xception_coco_voctrainval']
  
  _DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'
  _MODEL_URLS = {
  	'mobilenetv2_coco_voctrainaug':
  		'deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz',
  	'mobilenetv2_coco_voctrainval':
  		'deeplabv3_mnv2_pascal_trainval_2018_01_29.tar.gz',
  	'xception_coco_voctrainaug':
  		'deeplabv3_pascal_train_aug_2018_01_04.tar.gz',
  	'xception_coco_voctrainval':
  		'deeplabv3_pascal_trainval_2018_01_04.tar.gz',
  }
  _TARBALL_NAME = _MODEL_URLS[MODEL_NAME]
  
  model_dir = 'deeplab_model'
  if not os.path.exists(model_dir):
    tf.gfile.MakeDirs(model_dir)
  
  download_path = os.path.join(model_dir, _TARBALL_NAME)
  if not os.path.exists(download_path):
    print('downloading model to %s, this might take a while...' % download_path)
    urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME],
  			     download_path)
    print('download completed! loading DeepLab model...')
  
  MODEL = DeepLabModel(download_path)
  print('model loaded successfully!')
  
  #######################################################################################
  
  
  list_im=glob.glob(dir_name + '/*_img.png'); list_im.sort()
  
  
  for i in range(0,len(list_im)):
  
  	image = Image.open(list_im[i])
  
  	res_im,seg=MODEL.run(image)
  
  	seg=cv2.resize(seg.astype(np.uint8),image.size)
  
  	mask_sel=(seg==15).astype(np.float32)
  
  
  	name=list_im[i].replace('img','masksDL')
  	cv2.imwrite(name,(255*mask_sel).astype(np.uint8))
  
  str_msg='\nDone: ' + dir_name
  print(str_msg)
  
  
  