inception.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import re
import sys
import numpy as np
import tensorflow as tf

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string(
  'model_dir', './',
  """Path to classify_image_graph_def.pb, """
  """imagenet_synset_to_human_label_map.txt, and """
  """imagenet_2012_challenge_label_map_proto.pbtxt.""")

tf.app.flags.DEFINE_integer('num_top_predictions', 5,
                            """Display this many predictions.""")

def NodeLookup(object):
  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):
    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)
      
    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
      
    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]
        
    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 create_graph():
  with tf.gfile.FastGFile(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_path):
  create_graph()
  if not tf.gfile.Exists(image_path):
    tf.logging.fatal('File does not exist %s', image_path)
  image_data = tf.gfile.FastGFile(image_path, 'rb').read()
    
  with tf.Session() as sess:
    softmax_tensor = sess.graph.get_tensor_by_name('softmax:0')
    predictions = sess.run(softmax_tensor,
                           {'DecodeJpeg/contents:0': image_data})
    predictions = np.squeeze(predictions)
    
    node_lookup = NodeLookup()
    
    top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
    for node_id in top_k:
      human_strings.append(node_lookup.id_to_string(node_id))
      scores.append(predictions[node_id])
      print('%s (score = %.5f)' % (human_string, score))
      
    

def main(argv=None):
  if argv is None:
    argv = sys.argv
    
    run_inference_on_image(argv[1])
    
if __name__ == '__main__':
  tf.app.run()