#!/usr/bin/env python 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 os import re class RosTensorFlow(): def __init__(self): self._session = tf.Session() self._cv_bridge = CvBridge() self._sub = rospy.Subscriber('usb_cam/image_raw', Image, self.callback, queue_size=1) self._pub = rospy.Publisher('/result_ripe', 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 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) # 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 callback(self, image_msg): cv_image = self._cv_bridge.imgmsg_to_cv2(image_msg, "bgr8") image_data = cv2.imencode('.jpg', cv_image)[1].tostring() # Creates graph from saved GraphDef. softmax_tensor = self._session.graph.get_tensor_by_name('softmax:0') predictions = self._session.run( softmax_tensor, {'DecodeJpeg/contents:0': image_data}) predictions = np.squeeze(predictions) # Creates node ID --> English string lookup. node_lookup = self.load(PATH_TO_LABELS, PATH_TO_UID) top_k = predictions.argsort()[-self.use_top_k:][::-1] for node_id in top_k: if node_id not in node_lookup: human_string = '' else: human_string = node_lookup[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__': ROOT_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) PATH_TO_CKPT = ROOT_PATH + '/include/classifier/classify_image_graph_def.pb' PATH_TO_LABELS = ROOT_PATH + '/include/classifier/imagenet_2012_challenge_label_map_proto.pbtxt' PATH_TO_UID = ROOT_PATH + '/include/classifier/imagenet_synset_to_human_label_map.txt' with tf.gfile.FastGFile(PATH_TO_CKPT, 'rb') as f: graph_def = tf.GraphDef() graph_def.ParseFromString(f.read()) _ = tf.import_graph_def(graph_def, name='') rospy.init_node('ros_tensorflow_classify') tensor = RosTensorFlow() tensor.main()