def callback(self,data):
with session.as_default():
assert tf.get_default_session() is session
cv_image = self.bridge.imgmsg_to_cv2(data, desired_encoding="mono8")
np_image = (cv_image.astype(np.float32) - pixel_depth / 2) / pixel_depth
self.input_image = np_image.reshape((1,image_size,image_size,num_channels)).astype(np.float32)
prediction = tf.nn.softmax(model(self.input_image))
pre_dict = dict(zip(list(range(num_labels)),prediction.eval()[0]))
sorted_pre_dict = sorted(pre_dict.items(), key=operator.itemgetter(1))
name1 = value2name[sorted_pre_dict[-1][0]]
value1 = sorted_pre_dict[-1][1]
name2 = value2name[sorted_pre_dict[-2][0]]
value2 = sorted_pre_dict[-2][1]
pre = PredictionMSG()
pre.name1, pre.value1, pre.name2, pre.value2 = name1, value1, name2, value2
self.pub1.publish(pre)
sys.stdout.write(".")
sys.stdout.flush()
def valuate(self, data):
config = tf.ConfigProto()
# config.log_device_placement = True
with tf.Session(graph=graph, config=config) as session:
saver.restore(session, "model.ckpt")
while self.got_image:
self.got_image = False
prediction = tf.nn.softmax(model(self.input_image))
pre_dict = dict(zip(list(range(num_labels)), prediction.eval()[0]))
sorted_pre_dict = sorted(pre_dict.items(), key=operator.itemgetter(1))
name1 = value2name[sorted_pre_dict[-1][0]]
value1 = sorted_pre_dict[-1][1]
name2 = value2name[sorted_pre_dict[-2][0]]
value2 = sorted_pre_dict[-2][1]
pre = PredictionMSG()
pre.name1, pre.value1, pre.name2, pre.value2 = name1, value1, name2, value2
self.pub1.publish(pre)
sys.stdout.write(".")
sys.stdout.flush()
"""cv_image = self.bridge.imgmsg_to_cv2(data, desired_encoding="mono8")
def valuate(self, data):
config = tf.ConfigProto()
#config.log_device_placement = True
with tf.Session(graph=graph, config=config) as session:
saver.restore(session, "new_model.ckpt")
while self.got_image:
self.got_image = False
prediction = tf.nn.softmax(model(self.input_image))
pre_dict = dict(
zip(list(range(num_labels)),
prediction.eval()[0]))
sorted_pre_dict = sorted(pre_dict.items(),
key=operator.itemgetter(1))
name1 = value2name[sorted_pre_dict[-1][0]]
value1 = sorted_pre_dict[-1][1]
name2 = value2name[sorted_pre_dict[-2][0]]
value2 = sorted_pre_dict[-2][1]
pre = PredictionMSG()
pre.name1, pre.value1, pre.name2, pre.value2 = name1, value1, name2, value2
self.pub1.publish(pre)
sys.stdout.write(".")
sys.stdout.flush()
'''cv_image = self.bridge.imgmsg_to_cv2(data, desired_encoding="mono8")
