g4a-tensorflow.py 文件源码

def run_inference_on_image(image):
  """Runs inference on an image.

  Args:
    image: Image file name.

  Returns:
    Nothing
  """
  #image_data = tf.gfile.FastGFile(image, 'rb').read()
  image_data = image

  # 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.tostring()})
    predictions = np.squeeze(predictions)
    sess.close()

    # Creates node ID --> English string lookup.
    node_lookup = NodeLookup()

    top_k = predictions.argsort()[1:][::-1]
    human_string = node_lookup.id_to_string(top_k[0])
    score = predictions[top_k[0]]
    return {
      'autocategory': human_string,
      'autocategoryconfidence': str(score)
      }
      #print('%s (score = %.5f)' % (human_string, score))