image_recognition.py

import rospy
from sensor_msgs.msg import Image
from std_msgs.msg import String
from cv_bridge import CvBridge
import cv2
import numpy as np
import tensorflow as tf
import argparse

## get graph
with tf.gfile.FastGFile("./output_graph.pb", 'rb') as f:
    graph_def = tf.GraphDef()
    graph_def.ParseFromString(f.read())
    _ = tf.import_graph_def(graph_def, name='')

## get label
label_lines = [line.rstrip() for line
                   	in tf.gfile.GFile("./output_labels.txt")]

def setup_args():
  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='/tmp/imagenet',
      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=5,
      help='Display this many predictions.'
  )
  global FLAGS
  FLAGS, unparsed = parser.parse_known_args()
  return unparsed

class RosTensorFlow():
    def __init__(self):
        self._session = tf.Session()

        self._cv_bridge = CvBridge()

        self._sub = rospy.Subscriber('image', Image, self.callback, queue_size=1)
        self._pub = rospy.Publisher('result', String, queue_size=1)
        self.score_threshold = rospy.get_param('~score_threshold', 0.1)
        self.use_top_k = rospy.get_param('~use_top_k', 5)

    def callback(self, image_msg):
        cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8")
        # copy from
        # classify_image.py
        image_data = cv2.imencode('.jpg', cv_image)[1].tostring()
        # Creates graph from saved GraphDef.
        softmax_tensor = self._session.graph.get_tensor_by_name('final_result:0')
        predictions = self._session.run(
            softmax_tensor, {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)
        # Creates node ID --> English string lookup.
       
        top_k = predictions.argsort()[-self.use_top_k:][::-1]
        for node_id in top_k:
            human_string = label_lines[node_id]
            score = predictions[node_id]
            if score > self.score_threshold:
                rospy.loginfo('%s (score = %.5f)' % (human_string, score))
                self._pub.publish(human_string)

    def main(self):
        rospy.spin()

if __name__ == '__main__':
    setup_args()
    rospy.init_node('rostensorflow')
    tensor = RosTensorFlow()
    tensor.main()