text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import argparse
  import sys
  import time
  import json
  
  import numpy as np
  import tensorflow as tf
  
  def load_graph(model_file):
    graph = tf.Graph()
    graph_def = tf.GraphDef()
  
    with open(model_file, "rb") as f:
      graph_def.ParseFromString(f.read())
    with graph.as_default():
      tf.import_graph_def(graph_def)
  
    return graph
  
  def read_tensor_from_image_file(file_name, input_height=299, input_width=299,
  				input_mean=0, input_std=255):
    input_name = "file_reader"
    output_name = "normalized"
    file_reader = tf.read_file(file_name, input_name)
    if file_name.endswith(".png"):
      image_reader = tf.image.decode_png(file_reader, channels = 3,
                                         name='png_reader')
    elif file_name.endswith(".gif"):
      image_reader = tf.squeeze(tf.image.decode_gif(file_reader,
                                                    name='gif_reader'))
    elif file_name.endswith(".bmp"):
      image_reader = tf.image.decode_bmp(file_reader, name='bmp_reader')
    else:
      image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
                                          name='jpeg_reader')
    float_caster = tf.cast(image_reader, tf.float32)
    dims_expander = tf.expand_dims(float_caster, 0);
    resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
    normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
    sess = tf.Session()
    result = sess.run(normalized)
  
    return result
  
  def load_labels(label_file):
    label = []
    proto_as_ascii_lines = tf.gfile.GFile(label_file).readlines()
    for l in proto_as_ascii_lines:
      label.append(l.rstrip())
    return label
  
  if __name__ == "__main__":
    file_name = "tf_files/flower_photos/daisy/3475870145_685a19116d.jpg"
    model_file = "../../libraries/tensorflow/tf_files/retrained_graph.pb"
    label_file = "../../libraries/tensorflow/tf_files/retrained_labels.txt"
    input_height = 299
    input_width = 299
    input_mean = 128
    input_std = 128
    input_layer = "Mul"
    output_layer = "final_result"
  
    parser = argparse.ArgumentParser()
    parser.add_argument("--image", help="image to be processed")
    parser.add_argument("--graph", help="graph/model to be executed")
    parser.add_argument("--labels", help="name of file containing labels")
    parser.add_argument("--input_height", type=int, help="input height")
    parser.add_argument("--input_width", type=int, help="input width")
    parser.add_argument("--input_mean", type=int, help="input mean")
    parser.add_argument("--input_std", type=int, help="input std")
    parser.add_argument("--input_layer", help="name of input layer")
    parser.add_argument("--output_layer", help="name of output layer")
    args = parser.parse_args()
  
    if args.graph:
      model_file = args.graph
    if args.image:
      file_name = args.image
    if args.labels:
      label_file = args.labels
    if args.input_height:
      input_height = args.input_height
    if args.input_width:
      input_width = args.input_width
    if args.input_mean:
      input_mean = args.input_mean
    if args.input_std:
      input_std = args.input_std
    if args.input_layer:
      input_layer = args.input_layer
    if args.output_layer:
      output_layer = args.output_layer
  
    graph = load_graph(model_file)
    t = read_tensor_from_image_file(file_name,
                                    input_height=input_height,
                                    input_width=input_width,
                                    input_mean=input_mean,
                                    input_std=input_std)
  
    input_name = "import/" + input_layer
    output_name = "import/" + output_layer
    input_operation = graph.get_operation_by_name(input_name);
    output_operation = graph.get_operation_by_name(output_name);
  
    with tf.Session(graph=graph) as sess:
      start = time.time()
      results = sess.run(output_operation.outputs[0],
                        {input_operation.outputs[0]: t})
      end=time.time()
    results = np.squeeze(results)
  
    top_k = results.argsort()[-5:][::-1]
    labels = load_labels(label_file)
    
  #  print('\nEvaluation time (1-image): {:.3f}s\n'.format(end-start))
    template = "{} (score={:0.5f})"
  #  for i in top_k:
  #    print(template.format(labels[i], results[i]))
  
    sortida="{ "
    for i in range(0, len(top_k)):
      if i < len(top_k)-1:
        sortida+="\""+labels[i]+'\": '+str(results[i])+', '
    else:
        sortida+="\""+labels[i]+'\": '+str(results[i])+' }'
  #  print("---------------------")
    print(json.dumps(sortida))
  