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()
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()