classifier.py

# Copyright 2015 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.

# ==============================================================================



"""Simple image classification with Inception.



Run image classification with Inception trained on ImageNet 2012 Challenge data

set.



This program creates a graph from a saved GraphDef protocol buffer,

and runs inference on an input JPEG image. It outputs human readable

strings of the top 5 predictions along with their probabilities.



Change the --image_file argument to any jpg image to compute a

classification of that image.



Please see the tutorial and website for a detailed description of how

to use this script to perform image recognition.



https://tensorflow.org/tutorials/image_recognition/

"""



from __future__ import absolute_import

from __future__ import division

from __future__ import print_function



import argparse

import os

import re

import sys



import numpy as np



import tensorflow as tf



FLAGS = None



current_dir_path = os.path.dirname(os.path.realpath(__file__))



class NodeLookup(object):

  """Converts integer node ID's to human readable labels."""



  def __init__(self,

               label_lookup_path=None,

               uid_lookup_path=None):

    if not label_lookup_path:

      label_lookup_path = os.path.join(

          FLAGS.model_dir, 'imagenet/imagenet_2012_challenge_label_map_proto.pbtxt')

    if not uid_lookup_path:

      uid_lookup_path = os.path.join(

          FLAGS.model_dir, 'output_labels.txt')

    self.node_lookup = self.load(label_lookup_path, uid_lookup_path)



  def load(self, label_lookup_path, uid_lookup_path):

    """Loads a human readable English name for each softmax node.



    Args:

      label_lookup_path: string UID to integer node ID.

      uid_lookup_path: string UID to human-readable string.



    Returns:

      dict from integer node ID to human-readable string.

    """

    if not tf.gfile.Exists(uid_lookup_path):

      tf.logging.fatal('File does not exist %s', uid_lookup_path)

    if not tf.gfile.Exists(label_lookup_path):

      tf.logging.fatal('File does not exist %s', label_lookup_path)



    # Loads mapping from string UID to human-readable string

    proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()

    uid_to_human = {}

    p = re.compile(r'[n\d]*[ \S,]*')

    for line in proto_as_ascii_lines:

      parsed_items = p.findall(line)

      uid = parsed_items[0]

      human_string = parsed_items[2]

      uid_to_human[uid] = human_string



    # Loads mapping from string UID to integer node ID.

    node_id_to_uid = {}

    proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()

    for line in proto_as_ascii:

      if line.startswith('  target_class:'):

        target_class = int(line.split(': ')[1])

      if line.startswith('  target_class_string:'):

        target_class_string = line.split(': ')[1]

        node_id_to_uid[target_class] = target_class_string[1:-2]



    # Loads the final mapping of integer node ID to human-readable string

    node_id_to_name = {}

    for key, val in node_id_to_uid.items():

      if val not in uid_to_human:

        tf.logging.fatal('Failed to locate: %s', val)

      name = uid_to_human[val]

      node_id_to_name[key] = name



    return node_id_to_name



  def id_to_string(self, node_id):

    if node_id not in self.node_lookup:

      return ''

    return self.node_lookup[node_id]





def create_graph():

  """Creates a graph from saved GraphDef file and returns a saver."""

  # Creates graph from saved graph_def.pb.

  with tf.gfile.FastGFile(os.path.join(

      FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:

    graph_def = tf.GraphDef()

    graph_def.ParseFromString(f.read())

    _ = tf.import_graph_def(graph_def, name='')





def run_inference_on_image(image):

  """Runs inference on an image.



  Args:

    image: Image file name.



  Returns:

    Nothing

  """

  if not tf.gfile.Exists(image):

    tf.logging.fatal('File does not exist %s', image)

  image_data = tf.gfile.FastGFile(image, 'rb').read()



  # Creates graph from saved GraphDef.

  create_graph()



  with tf.Session() as sess:

    # Some useful tensors:

    # 'softmax:0': A tensor containing the normalized prediction across

    #   1000 labels.

    # 'pool_3:0': A tensor containing the next-to-last layer containing 2048

    #   float description of the image.

    # 'DecodeJpeg/contents:0': A tensor containing a string providing JPEG

    #   encoding of the image.

    # Runs the softmax tensor by feeding the image_data as input to the graph.

    softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')

    predictions = sess.run(softmax_tensor,

                           {'DecodeJpeg/contents:0': image_data})

    predictions = np.squeeze(predictions)



    # Creates node ID --> English string lookup.

   # node_lookup = NodeLookup()

	a=open('/tmp/output_labels.txt')
	lines=a.readlines()
    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]

    for node_id in top_k:

      #human_string = node_lookup.id_to_string(node_id)
	  human_string = lines[node_id]
      score = predictions[node_id]

      print('%s (score = %.5f)' % (human_string, score))





def main(_):

  image = (FLAGS.image_file if FLAGS.image_file else

           os.path.join(FLAGS.model_dir, 'cropped_panda.jpg'))

  run_inference_on_image(image)





if __name__ == '__main__':

  parser = argparse.ArgumentParser()

  # classify_image_graph_def.pb:

  #   Binary representation of the GraphDef protocol buffer.

  # imagenet_synset_to_human_label_map.txt:

  #   Map from synset ID to a human readable string.

  # imagenet_2012_challenge_label_map_proto.pbtxt:

  #   Text representation of a protocol buffer mapping a label to synset ID.

  parser.add_argument(

      '--model_dir',

      type=str,

      default= current_dir_path + '/inception',

      help="""\

      Path to classify_image_graph_def.pb,

      imagenet_synset_to_human_label_map.txt, and

      imagenet_2012_challenge_label_map_proto.pbtxt.\

      """

  )

  parser.add_argument(

      '--image_file',

      type=str,

      default='',

      help='Absolute path to image file.'

  )

  parser.add_argument(

      '--num_top_predictions',

      type=int,

      default=1,

      help='Display this many predictions.'

  )

  FLAGS, unparsed = parser.parse_known_args()

  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)