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 classify_image
class RosTensorFlow():
def __init__(self):
classify_image.maybe_download_and_extract()
self._session = tf.Session()
classify_image.create_graph()
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('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 = classify_image.NodeLookup()
top_k = predictions.argsort()[-self.use_top_k:][::-1]
for node_id in top_k:
human_string = node_lookup.id_to_string(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__':
classify_image.setup_args()
rospy.init_node('rostensorflow')
tensor = RosTensorFlow()
tensor.main()
