def _init_beamNG(self) -> None:
"""
Initialize beamNG:
Create the scenario, vehicle, sensors, and load everything
"""
self.scenario = Scenario(self.OPTIONS['scenario_map'],
self.OPTIONS['scenario_name'],
description=self.OPTIONS['scenario_desc'])
self.vehicle = Vehicle(self.OPTIONS['vehicle_name'],
model=self.OPTIONS['vehicle_model'],
license=self.OPTIONS['vehicle_license'])
self.lidar_sensor = Lidar(max_dist=180, vres=24, vangle=25)
self.vehicle.attach_sensor('lidar', self.lidar_sensor)
self.front_camera = Camera(self.OPTIONS['f_cam_pos'],
self.OPTIONS['f_cam_dir'],
self.OPTIONS['f_cam_fov'],
self.OPTIONS['f_cam_res'],
colour=True,
annotation=True)
self.vehicle.attach_sensor('front_camera', self.front_camera)
self.scenario.add_vehicle(self.vehicle, self.OPTIONS['vehicle_pos'],
self.OPTIONS['vehicle_rot'],
self.OPTIONS['vehicle_rot_quat'])
self.scenario.make(self.beamng)
self.beamng.load_scenario(self.scenario)
def main():
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost', 64256)
scenario = Scenario('west_coast_usa', 'lidar_demo',
description='Spanning the map with a lidar sensor')
vehicle = Vehicle('ego_vehicle', model='etk800',
licence='RED', color='Red')
lidar = Lidar(offset=(0, 0, 1.6))
vehicle.attach_sensor('lidar', lidar)
scenario.add_vehicle(vehicle, pos=(-717.121, 101, 118.675), rot=(0, 0, 45))
scenario.make(beamng)
bng = beamng.open(launch=True)
try:
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(60) # With 60hz temporal resolution
bng.load_scenario(scenario)
bng.hide_hud()
bng.start_scenario()
vehicle.ai_set_mode('span')
print('Driving around for 60 seconds...')
sleep(60)
finally:
bng.close()
def test_noise(beamng):
with beamng as bng:
scenario = Scenario('west_coast_usa', 'camera_test')
vehicle = Vehicle('test_car', model='etk800')
pos = (-0.3, 1, 1.0)
direction = (0, 1, 0)
fov = 120
resolution = (64, 64)
cam = Camera(pos, direction, fov, resolution, colour=True, depth=True)
noise_cam = RandomImageNoise(cam)
vehicle.attach_sensor('noise_cam', noise_cam)
lidar = Lidar()
noise_lidar = RandomLIDARNoise(lidar, mean=0, var=.5)
vehicle.attach_sensor('noise_lidar', noise_lidar)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=(0, 0, 45))
scenario.make(beamng)
bng.load_scenario(scenario)
bng.step(120)
time.sleep(20)
vehicle.poll_sensors()
reference_img = np.array(noise_cam._sensor.data['colour'])
noise_img = np.array(noise_cam.data['colour'])
assert (not (np.array_equal(noise_img, reference_img)))
ref_pc = lidar.data['points']
noise_pc = noise_lidar.data['points']
assert (not (np.array_equal(ref_pc, noise_pc)))
def main():
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
#beamng.change_setting('research', True)
scenario = Scenario('west_coast_usa',
'lidar_tour',
description='Tour through the west coast gathering '
'Lidar data')
vehicle = Vehicle('ego_vehicle', model='etk800', licence='LIDAR')
lidar = Lidar()
vehicle.attach_sensor('lidar', lidar)
#beamng.open_lidar('lidar', vehicle, 'shmem', 8000)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=None,
rot_quat=(0, 0, 0.3826834, 0.9238795))
scenario.make(beamng)
bng = beamng.open(launch=True)
#bng.open_lidar('lidar', vehicle, 'shmem', 8000)
#lidar.connect(bng, vehicle)
try:
bng.load_scenario(scenario) # this is where the error happens
window = open_window(SIZE, SIZE)
lidar_vis = LidarVisualiser(Lidar.max_points)
lidar_vis.open(SIZE, SIZE)
bng.set_steps_per_second(60)
bng.set_deterministic()
bng.hide_hud()
bng.start_scenario()
bng.pause()
vehicle.ai_set_mode('span')
def update():
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
bng.step(3, wait=False)
lidar_vis.update_points(points, vehicle.state)
glutPostRedisplay()
glutReshapeFunc(lidar_resize)
glutIdleFunc(update)
glutMainLoop()
except Exception as e:
print(e)
finally:
bng.close()
def main():
setup_logging()
beamng = BeamNGpy('localhost', 64256)
scenario = Scenario('west_coast_usa',
'lidar_tour',
description='Tour through the west coast gathering '
'Lidar data')
vehicle = Vehicle('ego_vehicle', model='etk800', licence='LIDAR')
lidar = Lidar()
vehicle.attach_sensor('lidar', lidar)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=None,
rot_quat=(0, 0, 0.3826834, 0.9238795))
scenario.make(beamng)
bng = beamng.open(launch=True)
try:
bng.load_scenario(scenario)
window = open_window(SIZE, SIZE)
lidar_vis = LidarVisualiser(Lidar.max_points)
lidar_vis.open(SIZE, SIZE)
bng.set_steps_per_second(60)
bng.set_deterministic()
bng.hide_hud()
bng.start_scenario()
bng.pause()
vehicle.ai_set_mode('span')
def update():
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
bng.step(3, wait=False)
lidar_vis.update_points(points, vehicle.state)
glutPostRedisplay()
glutReshapeFunc(lidar_resize)
glutIdleFunc(update)
glutMainLoop()
finally:
bng.close()
def _init_beamNG(self) -> None:
self.scenario = Scenario(self.scenario_map,
self.scenario_name,
description=self.scenario_desc)
self.vehicle = Vehicle(self.vehicle_name,
model=self.vehicle_model,
license=self.vehicle_license)
self.lidar_sensor = Lidar()
self.vehicle.attach_sensor('lidar', self.lidar_sensor)
self.scenario.add_vehicle(self.vehicle, self.vehicle_pos,
self.vehicle_rot, self.vehicle_rot_quat)
self.scenario.make(self.beamng)
self.beamng.load_scenario(self.scenario)
def test_lidar(beamng):
scenario = Scenario('west_coast_usa', 'lidar_test')
vehicle = Vehicle('test_car', model='etk800')
lidar = Lidar()
vehicle.attach_sensor('lidar', lidar)
scenario.add_vehicle(vehicle, pos=(-717.121, 101, 118.675), rot=(0, 0, 45))
scenario.make(beamng)
with beamng as bng:
bng.load_scenario(scenario)
bng.step(120)
sensors = bng.poll_sensors(vehicle)
arr = sensors['lidar']['points']
ref = arr[0]
eq = arr[np.where(arr == ref)]
assert eq.size != arr.size
def setup_lidar(mountpoint='roof'):
lidar = Lidar()
if mountpoint == 'hood':
#lidar.__init__(offset=(-0.3, 1.0, 1.0), direction=(-0.707, -0.707, 0), vres=2,
#lidar.__init__(offset=(-5, 0, 1.0), direction=(-0.707, -0.707, 0), vres=2,
lidar.__init__(offset=(2.4, 1, 1),
direction=(-0.707, -0.707, 0),
vres=2,
vangle=0.1,
rps=10000,
hz=60,
angle=180,
max_dist=200,
visualized=True)
elif mountpoint == 'roof':
lidar.__init__(offset=(0, 0, 1.7),
direction=(-0.707, -0.707, -0.0625),
vres=3,
vangle=0.1,
rps=100000,
hz=20,
angle=180,
max_dist=200,
visualized=True)
elif mountpoint == 'orig':
lidar.__init__(offset=(0, 0, 1.7),
direction=(0, -1, 0),
vres=32,
vangle=26.9,
rps=2200000,
hz=20,
angle=360,
max_dist=200,
visualized=True)
else:
print("returning lidar")
return lidar
return lidar
def setup_sensors(vehicle):
# Set up sensors
pos = (-0.3, 1, 1.0)
direction = (0, 1, 0)
fov = 120
resolution = (512, 512)
front_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
pos = (0.0, 3, 1.0)
direction = (0, -1, 0)
fov = 90
resolution = (512, 512)
back_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
gforces = GForces()
electrics = Electrics()
damage = Damage()
damage.encode_vehicle_request()
lidar = Lidar(visualized=False)
timer = Timer()
# Attach them
vehicle.attach_sensor('front_cam', front_camera)
vehicle.attach_sensor('back_cam', back_camera)
vehicle.attach_sensor('gforces', gforces)
vehicle.attach_sensor('electrics', electrics)
vehicle.attach_sensor('damage', damage)
vehicle.attach_sensor('timer', timer)
return vehicle
def test_lidar(beamng, shmem):
with beamng as bng:
scenario = Scenario('west_coast_usa', 'lidar_test')
vehicle = Vehicle('test_car', model='etk800')
lidar = Lidar(shmem=shmem)
vehicle.attach_sensor('lidar', lidar)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=(0, 0, 45))
scenario.make(beamng)
bng.load_scenario(scenario)
bng.step(120)
time.sleep(20)
vehicle.poll_sensors()
arr = lidar.data['points']
ref = arr[0]
eq = arr[np.where(arr == ref)]
assert eq.size != arr.size
def setup_lidar(mountpoint='roof'):
lidar = Lidar()
if mountpoint == 'hood':
lidar.__init__(offset=(-0.4, 1, 1.0),
direction=(-0.707, -0.707, 0),
vres=2,
vangle=0.1,
rps=10000,
hz=20,
angle=180,
max_dist=200,
visualized=True)
else:
lidar.__init__(offset=(0, 0, 1.7),
direction=(-0.707, -0.707, -0.0625),
vres=2,
vangle=0.1,
rps=10000,
hz=20,
angle=180,
max_dist=200,
visualized=True)
return lidar
import mmap
def main():
random.seed(1703)
setup_logging()
# Plotting code setting up a 3x2 figure
fig = plt.figure(1, figsize=(10, 5))
axarr = fig.subplots(2, 3)
a_colour = axarr[0, 0]
b_colour = axarr[1, 0]
a_depth = axarr[0, 1]
b_depth = axarr[1, 1]
a_annot = axarr[0, 2]
b_annot = axarr[1, 2]
plt.ion()
beamng = BeamNGpy('localhost', 64256)
bng = beamng.open(launch=True)
# Create a scenario in west_coast_usa
scenario = Scenario('west_coast_usa',
'tech_test',
description='Random driving for research')
# Set up first vehicle, with two cameras, gforces sensor, lidar, electrical
# sensors, and damage sensors
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='RED',
color='Red')
# Set up sensors
pos = (-0.3, 1, 1.0)
direction = (0, 1, 0)
fov = 120
resolution = (512, 512)
front_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
pos = (0.0, 3, 1.0)
direction = (0, -1, 0)
fov = 90
resolution = (512, 512)
back_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
gforces = GForces()
electrics = Electrics()
damage = Damage()
lidar = Lidar(visualized=False)
timer = Timer()
# Attach them
vehicle.attach_sensor('front_cam', front_camera)
vehicle.attach_sensor('back_cam', back_camera)
vehicle.attach_sensor('gforces', gforces)
vehicle.attach_sensor('electrics', electrics)
vehicle.attach_sensor('damage', damage)
vehicle.attach_sensor('timer', timer)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=None,
rot_quat=(0, 0, 0.3826834, 0.9238795))
# Compile the scenario and place it in BeamNG's map folder
scenario.make(bng)
# Start BeamNG and enter the main loop
try:
bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(60) # With 60hz temporal resolution
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# Send random inputs to vehice and advance the simulation 20 steps
for _ in range(1024):
throttle = random.uniform(0.0, 1.0)
steering = random.uniform(-1.0, 1.0)
brake = random.choice([0, 0, 0, 1])
vehicle.control(throttle=throttle, steering=steering, brake=brake)
bng.step(20)
# Retrieve sensor data and show the camera data.
sensors = bng.poll_sensors(vehicle)
print('{} seconds passed.'.format(sensors['timer']['time']))
a_colour.imshow(sensors['front_cam']['colour'].convert('RGB'))
a_depth.imshow(sensors['front_cam']['depth'].convert('L'))
a_annot.imshow(sensors['front_cam']['annotation'].convert('RGB'))
b_colour.imshow(sensors['back_cam']['colour'].convert('RGB'))
b_depth.imshow(sensors['back_cam']['depth'].convert('L'))
b_annot.imshow(sensors['back_cam']['annotation'].convert('RGB'))
plt.pause(0.00001)
finally:
bng.close()
def main():
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
config = Config()
loaded_config = config.load("{}/config.json".format(beamng.home))
# Create a scenario in west_coast_usa
scenario = Scenario('west_coast_usa',
'research_test',
description='Random driving for research')
# Set up first vehicle, with two cameras, gforces sensor, lidar, electrical
# sensors, and damage sensors
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='RED',
color='Red')
# Set up sensors
pos = (-0.3, 1, 1.0)
direction = (0, 1, 0)
fov = 120
resolution = (512, 512)
front_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
pos = (0.0, 3, 1.0)
direction = (0, -1, 0)
fov = 90
resolution = (512, 512)
back_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
gforces = GForces()
electrics = Electrics()
damage = Damage()
lidar = Lidar(visualized=False)
timer = Timer()
# Attach them
vehicle.attach_sensor('front_cam', front_camera)
vehicle.attach_sensor('back_cam', back_camera)
vehicle.attach_sensor('gforces', gforces)
vehicle.attach_sensor('electrics', electrics)
vehicle.attach_sensor('damage', damage)
vehicle.attach_sensor('timer', timer)
#scenario.add_vehicle(vehicle, pos=(-717.121, 101, 118.675), rot=None, rot_quat=(0, 0, 0.3826834, 0.9238795))
#config_rot_quat = beamngpy.angle_to_quat(config.dir)
# N.B. using rot is deprecated in favor of rot_quat
#scenario.add_vehicle(vehicle, pos=tuple(config.pos), rot=tuple(config.dir), rot_quat=None)
scenario.add_vehicle(vehicle,
pos=tuple(config.pos),
rot=None,
rot_quat=beamngpy.angle_to_quat(config.dir))
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
try:
bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(60) # With 60hz temporal resolution
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# Send random inputs to vehicle and advance the simulation 20 steps
#for _ in range(1024):
for _ in range(100):
throttle = random.uniform(0.0, 1.0)
steering = random.uniform(-1.0, 1.0)
brake = random.choice([0, 0, 0, 1])
vehicle.control(throttle=throttle, steering=steering, brake=brake)
# bng.step(20)
bng.step(20)
# Retrieve sensor data and show the camera data.
sensors = bng.poll_sensors(vehicle)
print('\n{} seconds passed.'.format(sensors['timer']['time']))
print("step in loop {}".format(_))
for s in sensors.keys():
print("{} : {}".format(s, sensors[s]))
damage_dict = vehicle.sensors['damage'].encode_vehicle_request()
damage_dict = sensors['damage']
config.update(sensors['damage'])
config.update(vehicle.state)
config.save('{}/config.json'.format(beamng.home))
gamestate = beamng.get_gamestate()
if _ > 990:
print("late in sim")
finally:
bng.close()
def main():
global sp
setup_logging()
beamng = BeamNGpy('localhost', 64256, home='H:/BeamNG.research.v1.7.0.1clean')
scenario = Scenario('west_coast_usa', 'lidar_tour',
description='Tour through the west coast gathering '
'Lidar data')
vehicle = Vehicle('ego_vehicle', model='etk800', licence='LIDAR', color='Red')
lidar = Lidar()
lidar.__init__(offset=(0, 0, 1.7), direction=(-0.707, -0.707, 0), vres=32,
vangle=0.01, rps=2200000, hz=20, angle=360, max_dist=200,
visualized=True)
vehicle.attach_sensor('lidar', lidar)
ego_sp = spawn_point(sp)
ego_pos = ego_sp['pos']
ego_rot_quat = ego_sp['rot_quat']
scenario.add_vehicle(vehicle, pos=ego_pos, rot=None, rot_quat=ego_rot_quat)
tvs = traffic_vehicles()
#ps = [(-722.121, 103, 118.675), (-714.121, 101, 118.675), (-715.121, 105, 118.675), (-721.121, 100, 118.675)]
ps = [(ego_pos[0]-10, ego_pos[1]+2, ego_pos[2]),
(ego_pos[0]-10, ego_pos[1]-2, ego_pos[2]),
(ego_pos[0]-14, ego_pos[1]+5, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+8, ego_pos[1]-4, ego_pos[2]),
(ego_pos[0]+10, ego_pos[1]+9, ego_pos[2]),
(ego_pos[0]+1, ego_pos[1]+11, ego_pos[2])]
ps1 = [(ego_pos[0] - 10, ego_pos[1] + 3, ego_pos[2]),
(ego_pos[0] - 10, ego_pos[1] - 1, ego_pos[2]),
(ego_pos[0] - 14, ego_pos[1] + 5, ego_pos[2]),
(ego_pos[0] + 5, ego_pos[1] - 1, ego_pos[2]),
(ego_pos[0] + 8, ego_pos[1] - 4, ego_pos[2]),
(ego_pos[0] + 10, ego_pos[1] + 9, ego_pos[2]),
(ego_pos[0] + 1, ego_pos[1] + 11, ego_pos[2])]
ps_oldorig = [(ego_pos[0]-7, ego_pos[1]+3, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+4, ego_pos[1]+5, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+8, ego_pos[1]-4, ego_pos[2]),
(ego_pos[0]+10, ego_pos[1]+9, ego_pos[2]),
(ego_pos[0]+1, ego_pos[1]+11, ego_pos[2])]
for i in range(len(tvs)):
scenario.add_vehicle(tvs[i], pos=ps[i], rot_quat=ego_rot_quat)
scenario.make(beamng)
bng = beamng.open(launch=True)
try:
bng.load_scenario(scenario)
bng.set_steps_per_second(60)
bng.set_deterministic()
bng.hide_hud()
bng.start_scenario()
vehicle.ai_set_mode('traffic')
vehicle.ai_drive_in_lane(True)
#vehicle.ai_set_speed(16, mode='limit')
#vehicle.ai_set_target('traffic')
vehicle.ai_set_aggression(0.5) #0.7)
bng.start_traffic(tvs)
bng.switch_vehicle(vehicle)
start = time.time()
end = time.time()
damage_prev = bng.poll_sensors(vehicle)
with open('lidar_data.csv', 'w') as f:
for _ in range(1024):
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
#print(points.tolist())
#print()
#f.write("{}\n".format(points.tolist()))
print("Time passed since last step: {}".format(time.time() - end))
end = time.time()
print("Time passed since scenario begun: {}\n".format(end - start))
if end - start >= 30:
bng.close()
bng.step(30, wait=False)
except Exception as e:
print(e)
finally:
bng.close()
def add_sensor_to(self, vehicle: Vehicle) -> None:
from beamngpy.sensors import Lidar
vehicle.attach_sensor(self.rid, Lidar(max_dist=self.max_dist))
