save_length = 0
total_data = 0
write_list = []
file = open('training_data.txt', 'a')
#caminfo = 88.0000991821289, -449.00006103515625, 5.179999828338623, (0.26104751229286194, 0.9333941340446472, 13.440003395080566)
# yaw, pitch, dist, target
#def test():
# print(p.getDebugVisualizerCamera())
#
#t = Timer(15.0, test)
#t.start()
nn_direction, nn_velocity = TrainData.get_train_weights(100)
print()
while 1:
sensors_list = np.transpose(
np.array([robot.get_output_sensor_for_near_road_points(road)[1:5]]))
if sensors_list.shape[0] == 4:
output_sensor_direction = np.transpose(
nn_direction.model_output(sensors_list))[0]
output_sensor_velocity = np.transpose(
nn_velocity.model_output(sensors_list))[0]
if output_sensor_direction[0] > output_sensor_direction[1]:
save_length = 0
total_data = 0
write_list = []
file = open('training_data.txt', 'a')
#caminfo = 88.0000991821289, -449.00006103515625, 5.179999828338623, (0.26104751229286194, 0.9333941340446472, 13.440003395080566)
# yaw, pitch, dist, target
#def test():
# print(p.getDebugVisualizerCamera())
#
#t = Timer(15.0, test)
#t.start()
nn = TrainData.get_train_weights()
print()
while 1:
sensors_list = robot.get_output_sensor_for_near_road_points(road)
output_sensor = np.transpose(
nn.model_output(
np.transpose(
np.array(
robot.get_output_sensor_for_near_road_points(road)[1:5]))))
is_right = 0
is_left = 0
is_forward = 0