def world_loop(opts_dict):
params = {
'spawn': 15,
'weather': 'clear_noon',
'n_vehicles': 0
}
with cu.CarlaWrapper(TOWN, cu.VEHICLE_NAME, PORT) as env:
env.init(**params)
agent = RoamingAgent(env._player, False, opts_dict)
# Hack: fill up controller experience.
for _ in range(30):
env.tick()
env.apply_control(agent.run_step()[0])
for _ in tqdm.tqdm(range(125)):
env.tick()
observations = env.get_observations()
inputs = cu.get_inputs(observations)
debug = dict()
control, command = agent.run_step(inputs, debug_info=debug)
env.apply_control(control)
observations.update({'control': control, 'command': command})
processed = cu.process(observations)
yield debug
bzu.show_image('rgb', processed['rgb'])
bzu.show_image('birdview', cu.visualize_birdview(processed['birdview']))
def get_episode(env, params):
data = list()
progress = tqdm.tqdm(range(params.frames_per_episode), desc='Frame')
start, target = env.pose_tasks[np.random.randint(len(env.pose_tasks))]
env_params = {
'weather': np.random.choice(list(cu.TRAIN_WEATHERS.keys())),
'start': start,
'target': target,
'n_pedestrians': params.n_pedestrians,
'n_vehicles': params.n_vehicles,
}
env.init(**env_params)
env.success_dist = 5.0
agent = NoisyAgent(env)
agent.set_route(env._start_pose.location, env._target_pose.location)
# Real loop.
while len(data) < params.frames_per_episode and not env.is_success(
) and not env.collided:
for _ in range(params.frame_skip):
env.tick()
observations = env.get_observations()
control, command, last_status, real_control = agent.run_step(
observations)
agent_debug = agent.debug
env.apply_control(control)
observations['command'] = command
observations['control'] = control
observations['real_control'] = real_control
if not params.nodisplay:
_debug(observations, agent_debug)
observations['control'] = real_control
processed = cu.process(observations)
data.append(processed)
progress.update(1)
progress.close()
if (not env.is_success() and not env.collided) or len(data) < 500:
return None
return data
def _debug(observations, agent_debug):
import cv2
processed = cu.process(observations)
control = observations['control']
control = [control.steer, control.throttle, control.brake]
control = ' '.join(str('%.2f' % x).rjust(5, ' ') for x in control)
real_control = observations['real_control']
real_control = [
real_control.steer, real_control.throttle, real_control.brake
]
real_control = ' '.join(
str('%.2f' % x).rjust(5, ' ') for x in real_control)
canvas = np.uint8(observations['rgb']).copy()
rows = [x * (canvas.shape[0] // 10) for x in range(10 + 1)]
cols = [x * (canvas.shape[1] // 10) for x in range(10 + 1)]
WHITE = (255, 255, 255)
CROP_SIZE = 192
X = 176
Y = 192 // 2
R = 2
def _write(text, i, j):
cv2.putText(canvas, text, (cols[j], rows[i]), cv2.FONT_HERSHEY_SIMPLEX,
0.4, WHITE, 1)
_command = {
1: 'LEFT',
2: 'RIGHT',
3: 'STRAIGHT',
4: 'FOLLOW',
}.get(int(observations['command']), '???')
_write('Command: ' + _command, 1, 0)
_write('Velocity: %.1f' % np.linalg.norm(observations['velocity']), 2, 0)
_write('Real: %s' % control, -5, 0)
_write('Control: %s' % control, -4, 0)
r = 2
birdview = cu.visualize_birdview(crop_birdview(processed['birdview']))
def _dot(x, y, color):
x = int(x)
y = int(y)
birdview[176 - r - x:176 + r + 1 - x,
96 - r + y:96 + r + 1 + y] = color
_dot(0, 0, [255, 255, 255])
ox, oy = observations['orientation']
R = np.array([[ox, oy], [-oy, ox]])
u = np.array(agent_debug['waypoint']) - np.array(agent_debug['vehicle'])
u = R.dot(u[:2])
u = u * 4
_dot(u[0], u[1], [255, 255, 255])
def _stick_together(a, b):
h = min(a.shape[0], b.shape[0])
r1 = h / a.shape[0]
r2 = h / b.shape[0]
a = cv2.resize(a, (int(r1 * a.shape[1]), int(r1 * a.shape[0])))
b = cv2.resize(b, (int(r2 * b.shape[1]), int(r2 * b.shape[0])))
return np.concatenate([a, b], 1)
full = _stick_together(canvas, birdview)
bu.show_image('full', full)
def get_episode(env, params):
data = list()
progress = tqdm.tqdm(range(params.frames_per_episode), desc='Frame')
start, target = env.pose_tasks[np.random.randint(len(env.pose_tasks))]
env_params = {
'weather': np.random.choice(list(cu.TRAIN_WEATHERS.keys())),
'start': start,
'target': target,
'n_pedestrians': params.n_pedestrians,
'n_vehicles': params.n_vehicles,
}
env.init(**env_params)
env.success_dist = 5.0
agent = NoisyAgent(env)
agent.set_route(env._start_pose.location, env._target_pose.location)
world = env._client.get_world()
traffic_light_list = world.get_actors().filter('*traffic_light*')
# Real loop.
while len(data) < params.frames_per_episode and not env.is_success(
) and not env.collided:
for _ in range(params.frame_skip):
env.tick()
observations = env.get_observations()
control, command, last_status, real_control = agent.run_step(
observations)
agent_debug = agent.debug
env.apply_control(control)
observations['command'] = command
observations['control'] = control
observations['real_control'] = real_control
# Traffic lights
is_intersection_marin, traffic_light, light_state, distance_to_traffic_light = \
agent.detect_traffic_light(traffic_light_list)
if traffic_light:
print("INTERSECTION MARIN = ", is_intersection_marin)
print(
"incoming traffic light is at " +
str(distance_to_traffic_light) + "meters and is of color ",
agent.LightState(light_state).name)
# visualise traffic lights with a green rectangle
# green = carla.Color(0, 255, 0)
# size_plot_point = 0.1
# life_time_plot_point = 1.0
# world.debug.draw_point(traffic_light.get_location(), color=green, size=size_plot_point * 10,
# life_time=life_time_plot_point)
observations['is_traffic_light'] = True
observations['traffic_light_color'] = light_state - 1
observations[
'traffic_light_distance'] = distance_to_traffic_light
else:
observations['is_traffic_light'] = False
observations['traffic_light_color'] = 0
observations['traffic_light_distance'] = 0
print(f'is traffic light: {observations["is_traffic_light"]}')
print(f'traffic color: {observations["traffic_light_color"]}')
print(
f'traffic distance: {observations["traffic_light_distance"]}')
if not params.nodisplay:
_debug(observations, agent_debug)
observations['control'] = real_control
processed = cu.process(observations)
data.append(processed)
progress.update(1)
progress.close()
if (not env.is_success() and not env.collided) or len(data) < 500:
return None
return data