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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
+import logging
+import warnings
+
+warnings.filterwarnings("ignore")
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+tf.logging.set_verbosity(tf.logging.ERROR)
+logger = logging.getLogger()
+logger.setLevel(logging.ERROR)
+
+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_2012_challenge_label_map_proto.pbtxt')
+    if not uid_lookup_path:
+      uid_lookup_path = os.path.join(
+          FLAGS.model_dir, 'imagenet_synset_to_human_label_map.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.GFile(os.path.join(
+      FLAGS.model_dir, 'classify_image_graph_def.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('softmax:0')
+    predictions = sess.run(softmax_tensor,
+                           {'DecodeJpeg/contents:0': image_data})
+    predictions = np.squeeze(predictions)
+
+    # Creates node ID --> English string lookup.
+    node_lookup = NodeLookup()
+
+    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
+    for node_id in top_k:
+      human_string = node_lookup.id_to_string(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)