class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0):
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
data = OrderedDict()
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
cols = [
'Start_position', 'End_position', 'Total-Distance', 'Time',
"Distance-Travelled", "Follow_lane", "Straight", "Left", "Right",
"Avg-Speed", "Collision-Pedestrian", "Collision-Vehicle",
"Collision-Other", "Intersection-Lane", "Intersection-Offroad",
"Traffic_Light_Infraction", "Success"
]
df = pd.DataFrame(columns=cols)
ts = str(int(time.time()))
for experiment in experiment_suite.get_experiments(
)[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
data['Start_position'] = pose[0]
data['End_position'] = pose[1]
#print(df)
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
data["Total-Distance"] = initial_distance
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance, data) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index) , data)
data["Time"] = final_time
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result)
# Write the details of this episode.
self._recording.write_measurements_results(
experiment, rep, pose, reward_vec, control_vec)
data['Success'] = result
if result > 0:
logging.info(
'+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
#data['Start_position'] = start_index
#data['End_position'] = end_index
#df = df.append(data , ignore_index=True)
#print(df)
data["Avg-Speed"] = 3.6 * (data['Total-Distance'] /
data['Time'])
df = df.append(data, ignore_index=True)
df = df[cols]
try:
df.to_csv("Test_result_" + ts + '.csv',
columns=cols,
index=True)
except:
print("File being used will save later")
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
def _run_navigation_episode(self, agent, client, time_out, target,
episode_name, data):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
"""
# Send an initial command.
direction2text = {
0: 'Follow_lane',
1: 'Follow_lane',
2: 'Follow_lane',
3: 'Left',
4: 'Right',
5: 'Straight'
}
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
success = False
# Initialize lane and Offroad fields
data["Intersection-Lane"] = 0
data["Intersection-Offroad"] = 0
data["Directions"] = []
agent.command_follower.controller.params[
'target_speed'] = agent.command_follower.controller.params[
'default_speed_limit']
agent.traffic_light_infraction = False
for x in range(6):
data[direction2text[x]] = 0
data["Collision-Pedestrian"] = 0
data["Collision-Vehicle"] = 0
data["Collision-Other"] = 0
while True: #
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(
measurements.player_measurements.transform, target)
# Agent process the data.
if data[direction2text[directions]] == 0:
data[direction2text[directions]] = 1
control, controller_state = agent.run_step(measurements,
sensor_data, directions,
target)
# Send the control commands to the vehicle
client.send_control(control)
if measurements.player_measurements.intersection_otherlane > data[
"Intersection-Lane"]:
data[
"Intersection-Lane"] = measurements.player_measurements.intersection_otherlane
if measurements.player_measurements.intersection_offroad > data[
"Intersection-Offroad"]:
data[
"Intersection-Offroad"] = measurements.player_measurements.intersection_offroad
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
# If vehicle has stopped for pedestiran, vehicle or traffic light increase timeout by 1 unit
if min(controller_state['stop_pedestrian'], controller_state['stop_vehicle'],\
controller_state['stop_traffic_lights']) <= 0.3 :
time_out += (measurements.game_timestamp -
current_timestamp) / 1000
#logging.info("Controller sis Inputting:")
#logging.info('Steer = %f Throttle = %f Brake = %f ',
# control.steer, control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
#logging.info('Status:')
print(
f"Distance to target: {float(distance):.2f} \t Time_taken: {(current_timestamp -initial_timestamp)/1000 :.2f}\t Timeout : {time_out :.2f}"
)
# Check if reach the target
data["Distance-Travelled"] = data["Total-Distance"] - distance
if distance < self._distance_for_success:
success = True
break
if (current_timestamp - initial_timestamp) / 1000 > time_out:
data["Intersection-Offroad"] = 0
data["Intersection-Lane"] = 0
data["Collision-Pedestrian"] = 0
data["Collision-Vehicle"] = 0
data["Collision-Other"] = 0
success = False
print("Timeout")
break
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
#Stop in case of collision or excessive lane infraction
if measurements.player_measurements.collision_pedestrians >300 or \
measurements.player_measurements.collision_vehicles > 300 or \
measurements.player_measurements.collision_other>300:
if measurements.player_measurements.collision_pedestrians > 300:
data["Collision-Pedestrian"] = 1
elif measurements.player_measurements.collision_vehicles > 300:
data["Collision-Vehicle"] = 1
elif measurements.player_measurements.collision_other > 300:
data["Collision-Other"] = 1
data["Intersection-Offroad"] = 0
data["Intersection-Lane"] = 0
print("Collison detected")
success = False
break
if data["Intersection-Lane"] > 0.5:
data["Intersection-Lane"] = 1
data["Intersection-Offroad"] = 0
data["Collision-Pedestrian"] = 0
data["Collision-Vehicle"] = 0
data["Collision-Other"] = 0
print("Lane Infraction")
success = False
break
elif data["Intersection-Offroad"] > 0.5:
data["Intersection-Offroad"] = 1
data["Intersection-Lane"] = 0
data["Collision-Pedestrian"] = 0
data["Collision-Vehicle"] = 0
data["Collision-Other"] = 0
print("Lane Infraction")
success = False
break
if success:
data["Intersection-Offroad"] = 0
data["Intersection-Lane"] = 0
data["Collision-Pedestrian"] = 0
data["Collision-Vehicle"] = 0
data["Collision-Other"] = 0
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp) / 1000.0, distance, data
return 0, measurement_vec, control_vec, time_out, distance, data
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0):
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(dir_to_save=city_name,
name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
self._episode_number = 0
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments(
)[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
self._episode_number += 1
# Print information on
logging.info('======== !!!! ==========')
logging.info('Episode Number: %d', self._episode_number)
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index))
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result)
# Write the details of this episode.
self._recording.write_measurements_results(
experiment, rep, pose, reward_vec, control_vec)
if result > 0:
logging.info(
'+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
def _run_navigation_episode(self, agent, client, time_out, target,
episode_name):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
"""
# Send an initial command.
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
success = False
while (current_timestamp - initial_timestamp) < (time_out *
1000) and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(
measurements.player_measurements.transform, target)
# Agent process the data.
# control = agent.run_step(measurements, sensor_data, directions, target)
control = agent.run_step(measurements, sensor_data, directions,
target)
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ', control.steer,
control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
logging.info('Status:')
logging.info('[d=%f] c_x = %f, c_y = %f ---> t_x = %f, t_y = %f',
float(distance), current_x, current_y,
target.location.x, target.location.y)
# Check if reach the target
if distance < self._distance_for_success:
success = True
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp) / 1000.0, distance
return 0, measurement_vec, control_vec, time_out, distance
class CarlaEnvironment(Environment):
def __init__(self, level: LevelSelection, seed: int, frame_skip: int,
human_control: bool, custom_reward_threshold: Union[int,
float],
visualization_parameters: VisualizationParameters,
server_height: int, server_width: int, camera_height: int,
camera_width: int, verbose: bool,
experiment_suite: ExperimentSuite, config: str,
episode_max_time: int, allow_braking: bool,
quality: CarlaEnvironmentParameters.Quality,
cameras: List[CameraTypes], weather_id: List[int],
experiment_path: str,
separate_actions_for_throttle_and_brake: bool,
num_speedup_steps: int, max_speed: float, **kwargs):
super().__init__(level, seed, frame_skip, human_control,
custom_reward_threshold, visualization_parameters)
# server configuration
self.server_height = server_height
self.server_width = server_width
self.port = get_open_port()
self.host = 'localhost'
self.map_name = CarlaLevel[level.upper()].value['map_name']
self.map_path = CarlaLevel[level.upper()].value['map_path']
self.experiment_path = experiment_path
# client configuration
self.verbose = verbose
self.quality = quality
self.cameras = cameras
self.weather_id = weather_id
self.episode_max_time = episode_max_time
self.allow_braking = allow_braking
self.separate_actions_for_throttle_and_brake = separate_actions_for_throttle_and_brake
self.camera_width = camera_width
self.camera_height = camera_height
# setup server settings
self.experiment_suite = experiment_suite
self.config = config
if self.config:
# load settings from file
with open(self.config, 'r') as fp:
self.settings = fp.read()
else:
# hard coded settings
self.settings = CarlaSettings()
self.settings.set(SynchronousMode=True,
SendNonPlayerAgentsInfo=False,
NumberOfVehicles=15,
NumberOfPedestrians=30,
WeatherId=random.choice(
force_list(self.weather_id)),
QualityLevel=self.quality.value,
SeedVehicles=seed,
SeedPedestrians=seed)
if seed is None:
self.settings.randomize_seeds()
self.settings = self._add_cameras(self.settings, self.cameras,
self.camera_width,
self.camera_height)
# open the server
self.server = self._open_server()
logging.disable(40)
# open the client
self.game = CarlaClient(self.host, self.port, timeout=99999999)
self.game.connect()
if self.experiment_suite:
self.current_experiment_idx = 0
self.current_experiment = self.experiment_suite.get_experiments()[
self.current_experiment_idx]
self.scene = self.game.load_settings(
self.current_experiment.conditions)
else:
self.scene = self.game.load_settings(self.settings)
# get available start positions
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
# state space
self.state_space = StateSpace({
"measurements":
VectorObservationSpace(
4, measurements_names=["forward_speed", "x", "y", "z"])
})
for camera in self.scene.sensors:
self.state_space[camera.name] = ImageObservationSpace(
shape=np.array([self.camera_height, self.camera_width, 3]),
high=255)
# action space
if self.separate_actions_for_throttle_and_brake:
self.action_space = BoxActionSpace(
shape=3,
low=np.array([-1, 0, 0]),
high=np.array([1, 1, 1]),
descriptions=["steer", "gas", "brake"])
else:
self.action_space = BoxActionSpace(
shape=2,
low=np.array([-1, -1]),
high=np.array([1, 1]),
descriptions=["steer", "gas_and_brake"])
# human control
if self.human_control:
# convert continuous action space to discrete
self.steering_strength = 0.5
self.gas_strength = 1.0
self.brake_strength = 0.5
# TODO: reverse order of actions
self.action_space = PartialDiscreteActionSpaceMap(
target_actions=[[0., 0.], [0., -self.steering_strength],
[0., self.steering_strength],
[self.gas_strength, 0.],
[-self.brake_strength, 0],
[self.gas_strength, -self.steering_strength],
[self.gas_strength, self.steering_strength],
[self.brake_strength, -self.steering_strength],
[self.brake_strength, self.steering_strength]],
descriptions=[
'NO-OP', 'TURN_LEFT', 'TURN_RIGHT', 'GAS', 'BRAKE',
'GAS_AND_TURN_LEFT', 'GAS_AND_TURN_RIGHT',
'BRAKE_AND_TURN_LEFT', 'BRAKE_AND_TURN_RIGHT'
])
# map keyboard keys to actions
for idx, action in enumerate(self.action_space.descriptions):
for key in key_map.keys():
if action == key:
self.key_to_action[key_map[key]] = idx
self.num_speedup_steps = num_speedup_steps
self.max_speed = max_speed
# measurements
self.autopilot = None
self.planner = Planner(self.map_name)
# env initialization
self.reset_internal_state(True)
# render
if self.is_rendered:
image = self.get_rendered_image()
self.renderer.create_screen(image.shape[1], image.shape[0])
def _add_cameras(self, settings, cameras, camera_width, camera_height):
# add a front facing camera
if CameraTypes.FRONT in cameras:
camera = Camera(CameraTypes.FRONT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 0, 0)
settings.add_sensor(camera)
# add a left facing camera
if CameraTypes.LEFT in cameras:
camera = Camera(CameraTypes.LEFT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, -30, 0)
settings.add_sensor(camera)
# add a right facing camera
if CameraTypes.RIGHT in cameras:
camera = Camera(CameraTypes.RIGHT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 30, 0)
settings.add_sensor(camera)
# add a front facing depth camera
if CameraTypes.DEPTH in cameras:
camera = Camera(CameraTypes.DEPTH.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'Depth'
settings.add_sensor(camera)
# add a front facing semantic segmentation camera
if CameraTypes.SEGMENTATION in cameras:
camera = Camera(CameraTypes.SEGMENTATION.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'SemanticSegmentation'
settings.add_sensor(camera)
return settings
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self.planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _open_server(self):
log_path = path.join(
self.experiment_path if self.experiment_path is not None else '.',
'logs', "CARLA_LOG_{}.txt".format(self.port))
if not os.path.exists(os.path.dirname(log_path)):
os.makedirs(os.path.dirname(log_path))
with open(log_path, "wb") as out:
cmd = [
path.join(environ.get('CARLA_ROOT'),
'CarlaUE4.sh'), self.map_path, "-benchmark",
"-carla-server", "-fps={}".format(30 / self.frame_skip),
"-world-port={}".format(self.port),
"-windowed -ResX={} -ResY={}".format(self.server_width,
self.server_height),
"-carla-no-hud"
]
if self.config:
cmd.append("-carla-settings={}".format(self.config))
p = subprocess.Popen(cmd, stdout=out, stderr=out)
return p
def _close_server(self):
os.killpg(os.getpgid(self.server.pid), signal.SIGKILL)
def _update_state(self):
# get measurements and observations
measurements = []
while type(measurements) == list:
measurements, sensor_data = self.game.read_data()
self.state = {}
for camera in self.scene.sensors:
self.state[camera.name] = sensor_data[camera.name].data
self.location = [
measurements.player_measurements.transform.location.x,
measurements.player_measurements.transform.location.y,
measurements.player_measurements.transform.location.z
]
is_collision = measurements.player_measurements.collision_vehicles != 0 \
or measurements.player_measurements.collision_pedestrians != 0 \
or measurements.player_measurements.collision_other != 0
speed_reward = measurements.player_measurements.forward_speed - 1
if speed_reward > 30.:
speed_reward = 30.
self.reward = speed_reward \
- (measurements.player_measurements.intersection_otherlane * 5) \
- (measurements.player_measurements.intersection_offroad * 5) \
- is_collision * 100 \
- np.abs(self.control.steer) * 10
# update measurements
self.measurements = [measurements.player_measurements.forward_speed
] + self.location
self.autopilot = measurements.player_measurements.autopilot_control
# The directions to reach the goal (0 Follow lane, 1 Left, 2 Right, 3 Straight)
directions = int(
self._get_directions(measurements.player_measurements.transform,
self.current_goal) - 2)
self.state['high_level_command'] = directions
if (measurements.game_timestamp >=
self.episode_max_time) or is_collision:
# screen.success('EPISODE IS DONE. GameTime: {}, Collision: {}'.format(str(measurements.game_timestamp),
# str(is_collision)))
self.done = True
self.state['measurements'] = np.array(self.measurements)
def _take_action(self, action):
self.control = VehicleControl()
if self.separate_actions_for_throttle_and_brake:
self.control.steer = np.clip(action[0], -1, 1)
self.control.throttle = np.clip(action[1], 0, 1)
self.control.brake = np.clip(action[2], 0, 1)
else:
# transform the 2 value action (steer, throttle - brake) into a 3 value action (steer, throttle, brake)
self.control.steer = np.clip(action[0], -1, 1)
self.control.throttle = np.clip(action[1], 0, 1)
self.control.brake = np.abs(np.clip(action[1], -1, 0))
# prevent braking
if not self.allow_braking or self.control.brake < 0.1 or self.control.throttle > self.control.brake:
self.control.brake = 0
# prevent over speeding
if hasattr(self, 'measurements') and self.measurements[
0] * 3.6 > self.max_speed and self.control.brake == 0:
self.control.throttle = 0.0
self.control.hand_brake = False
self.control.reverse = False
self.game.send_control(self.control)
def _load_experiment(self, experiment_idx):
self.current_experiment = self.experiment_suite.get_experiments(
)[experiment_idx]
self.scene = self.game.load_settings(
self.current_experiment.conditions)
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
def _restart_environment_episode(self, force_environment_reset=False):
# select start and end positions
if self.experiment_suite:
# if an expeirent suite is available, follow its given poses
if self.current_pose >= len(self.current_experiment.poses):
# load a new experiment
self.current_experiment_idx = (
self.current_experiment_idx + 1) % len(
self.experiment_suite.get_experiments())
self._load_experiment(self.current_experiment_idx)
self.current_start_position_idx = self.current_experiment.poses[
self.current_pose][0]
self.current_goal = self.positions[self.current_experiment.poses[
self.current_pose][1]]
self.current_pose += 1
else:
# go over all the possible positions in a cyclic manner
self.current_start_position_idx = (
self.current_start_position_idx + 1) % self.num_positions
# choose a random goal destination
self.current_goal = random.choice(self.positions)
try:
self.game.start_episode(self.current_start_position_idx)
except:
self.game.connect()
self.game.start_episode(self.current_start_position_idx)
# start the game with some initial speed
for i in range(self.num_speedup_steps):
self.control = VehicleControl(throttle=1.0,
brake=0,
steer=0,
hand_brake=False,
reverse=False)
self.game.send_control(VehicleControl())
def get_rendered_image(self) -> np.ndarray:
"""
Return a numpy array containing the image that will be rendered to the screen.
This can be different from the observation. For example, mujoco's observation is a measurements vector.
:return: numpy array containing the image that will be rendered to the screen
"""
image = [self.state[camera.name] for camera in self.scene.sensors]
image = np.vstack(image)
return image
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG, enable_autopilot=False):
self.enable_autopilot = enable_autopilot
self.config = config
self.config["x_res"], self.config["y_res"] = IMG_SIZE
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
self.intersections_node = self.planner._city_track._map.get_intersection_nodes(
)
self.intersections_pos = np.array([
self.planner._city_track._map.convert_to_world(
intersection_node)
for intersection_node in self.intersections_node
])
self.pre_intersection = np.array([0.0, 0.0])
# # Cartesian coordinates
self.headings = np.array([[1, 0], [-1, 0], [0, 1], [0, -1]])
# self.lrs_matrix = {"GO_STRAIGHT": np.array([[1,0],[0,1]]),\
# "TURN_RIGHT": np.array([[0,-1],[1,0]]),\
# "TURN_LEFT": np.array([[0,1],[-1,0]])}
# self.goal_heading = np.array([0.0,0.0])
# self.current_heading = None
# self.pre_heading = None
# self.angular_speed = None
# Angular degree
self.headings_degree = np.array(
[0.0, 180.0, 90.0, -90.0]) # one-one mapping to self.headings
self.lrs_degree = {
"GO_STRAIGHT": 0.0,
"TURN_LEFT": -90.0,
"TURN_RIGHT": 90.0
}
self.goal_heading_degree = 0.0
self.current_heading_degree = None
self.pre_heading_degree = None
self.angular_speed_degree = np.array(0.0)
# The Action Space
if config["discrete_actions"]:
self.action_space = Discrete(
len(DISCRETE_ACTIONS
)) # It will be transformed to continuous 2D action.
else:
self.action_space = Box(-1.0, 1.0, shape=(2, ),
dtype=np.float32) # 2D action.
if config["use_sensor"] == 'use_semantic':
image_space = Box(0.0,
255.0,
shape=(config["y_res"], config["x_res"],
1 * config["framestack"]),
dtype=np.float32)
elif config["use_sensor"] in ['use_depth', 'use_logdepth']:
image_space = Box(0.0,
255.0,
shape=(config["y_res"], config["x_res"],
1 * config["framestack"]),
dtype=np.float32)
elif config["use_sensor"] == 'use_rgb':
image_space = Box(0,
255,
shape=(config["y_res"], config["x_res"],
3 * config["framestack"]),
dtype=np.uint8)
elif config["use_sensor"] == 'use_2rgb':
image_space = Box(0,
255,
shape=(config["y_res"], config["x_res"],
2 * 3 * config["framestack"]),
dtype=np.uint8)
# The Observation Space
self.observation_space = Tuple([
image_space,
Discrete(len(COMMANDS_ENUM)), # next_command
Box(-128.0, 128.0, shape=(2, ),
dtype=np.float32) # forward_speed, dist to goal
])
# TODO(ekl) this isn't really a proper gym spec
self._spec = lambda: None
self._spec.id = "Carla-v0"
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
self.cnt1 = None
self.displacement = None
self.next_command = "LANE_FOLLOW"
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(10000, 60000)
self.server_process = subprocess.Popen(
[
SERVER_BINARY,
self.config["server_map"],
"-windowed",
"-ResX=480",
"-ResY=360",
"-carla-server",
"-benchmark -fps=10", # "-benchmark -fps=10": to run the simulation at a fixed time-step of 0.1 seconds
"-carla-world-port={}".format(self.server_port)
],
preexec_fn=os.setsid,
stdout=open(os.devnull, "w")
) # ResourceWarning: unclosed file <_io.TextIOWrapper name='/dev/null' mode='w' encoding='UTF-8'>
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(
self
): # the __del__ method will be called when the instance of the class is deleted.(memory is freed.)
self.clear_server_state()
def reset(self):
error = None
for _ in range(RETRIES_ON_ERROR):
try:
if not self.server_process:
self.init_server()
return self._reset()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
raise error
# @set_timeout(15)
def _reset(self):
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
self.pre_intersection = np.array([0.0, 0.0])
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(
SynchronousMode=True,
# ServerTimeOut=10000, # CarlaSettings: no key named 'ServerTimeOut'
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather)
settings.randomize_seeds()
if self.config["use_sensor"] == 'use_semantic':
camera0 = Camera("CameraSemantic",
PostProcessing="SemanticSegmentation")
camera0.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
# camera0.set_position(30, 0, 130)
camera0.set(FOV=120)
camera0.set_position(2.0, 0.0, 1.4)
camera0.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera0)
if self.config["use_sensor"] in ['use_depth', 'use_logdepth']:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
# camera1.set_position(30, 0, 130)
camera1.set(FOV=120)
camera1.set_position(2.0, 0.0, 1.4)
camera1.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera1)
if self.config["use_sensor"] == 'use_rgb':
camera2 = Camera("CameraRGB")
camera2.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
# camera2.set_position(30, 0, 130)
# camera2.set_position(0.3, 0.0, 1.3)
camera2.set(FOV=120)
camera2.set_position(2.0, 0.0, 1.4)
camera2.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera2)
if self.config["use_sensor"] == 'use_2rgb':
camera_l = Camera("CameraRGB_L")
camera_l.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera_l.set(FOV=120)
camera_l.set_position(2.0, -0.1, 1.4)
camera_l.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera_l)
camera_r = Camera("CameraRGB_R")
camera_r.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera_r.set(FOV=120)
camera_r.set_position(2.0, 0.1, 1.4)
camera_r.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera_r)
# Setup start and end positions
scene = self.client.load_settings(settings)
self.positions = positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.pre_pos = self.start_pos.location
self.cnt1 = 0
self.displacement = 1000.0
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x // 1, self.start_pos.location.y // 1
]
self.end_coord = [
self.end_pos.location.x // 1, self.end_pos.location.y // 1
]
print("Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], self.start_coord,
self.scenario["end_pos_id"], self.end_coord))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
# remove the vehicle dropping when starting a new episode.
cnt = 1
z1 = 0
zero_control = VehicleControl()
while (cnt < 3):
self.client.send_control(
zero_control
) # VehicleControl().steer = 0, VehicleControl().throttle = 0, VehicleControl().reverse = False
z0 = z1
z1 = self.client.read_data(
)[0].player_measurements.transform.location.z
print(z1)
if z0 - z1 == 0:
cnt += 1
print('Starting new episode done.\n')
# Process observations: self._read_observation() returns image and py_measurements.
image, py_measurements = self._read_observation()
self.prev_measurement = py_measurements
# self.current_heading = self.pre_heading = np.array([py_measurements["x_orient"], py_measurements["y_orient"]])
# self.angular_speed = 0.0
self.pre_heading_degree = self.current_heading_degree = py_measurements[
"current_heading_degree"]
self.angular_speed_degree = np.array(0.0)
return self.encode_obs(self.preprocess_image(image),
py_measurements), py_measurements
def encode_obs(self, image, py_measurements):
assert self.config["framestack"] in [1, 2]
prev_image = self.prev_image
self.prev_image = image
if prev_image is None:
prev_image = image
if self.config["framestack"] == 2:
image = np.concatenate([prev_image, image], axis=2)
# obs = (image, COMMAND_ORDINAL[py_measurements["next_command"]], [
# py_measurements["forward_speed"],
# py_measurements["distance_to_goal"]
# ])
obs = image
self.last_obs = obs
return obs
def step(self, action):
try:
obs = self._step(action)
return obs
except Exception:
print("Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_obs, 0.0, True, self.prev_measurement)
def delta_degree(self, deltaxy):
return deltaxy if np.abs(
deltaxy) < 180 else deltaxy - np.sign(deltaxy) * 360
# image, py_measurements = self._read_observation() ---> self.preprocess_image(image) ---> step observation output
# @set_timeout(10)
def _step(self, action):
if self.config["discrete_actions"]:
action = DISCRETE_ACTIONS[int(action)] # Carla action is 2D.
assert len(action) == 2, "Invalid action {}".format(action)
if self.enable_autopilot:
action[
0] = self.autopilot.brake if self.autopilot.brake < 0 else self.autopilot.throttle
action[1] = self.autopilot.steer
if self.config["squash_action_logits"]:
forward = 2 * float(sigmoid(action[0]) - 0.5)
throttle = float(np.clip(forward, 0, 1))
brake = float(np.abs(np.clip(forward, -1, 0)))
steer = 2 * float(sigmoid(action[1]) - 0.5)
else:
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
# reverse and hand_brake are disabled.
reverse = False
hand_brake = False
if self.config["verbose"]:
print("steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
# print(self.client)
self.client.send_control(steer=steer,
throttle=throttle,
brake=brake,
hand_brake=hand_brake,
reverse=reverse)
# Process observations: self._read_observation() returns image and py_measurements.
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
print("Next command", py_measurements["next_command"])
print("dist_to_intersection:", py_measurements["dist_to_intersection"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
# compute angular_speed
self.current_heading_degree = py_measurements["current_heading_degree"]
self.angular_speed_degree = np.array(
self.delta_degree(self.current_heading_degree -
self.pre_heading_degree))
self.pre_heading_degree = py_measurements["current_heading_degree"]
# compute reward
reward = compute_reward(self, self.prev_measurement, py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
# done or not
# done = False
# done = self.cnt1 > 50 and (py_measurements["collision_vehicles"] or py_measurements["collision_pedestrians"] or py_measurements["collision_other"] or self.displacement < 0.5)
done = self.cnt1 > 50 and self.displacement < 0.2
# done = (self.num_steps > self.scenario["max_steps"]
# or py_measurements["next_command"] == "REACH_GOAL" or py_measurements["intersection_offroad"] or py_measurements["intersection_otherlane"]
# or (self.config["early_terminate_on_collision"]
# and collided_done(py_measurements)))
py_measurements["done"] = done
self.prev_measurement = py_measurements
# Write out measurements to file
if self.config["verbose"] and CARLA_OUT_PATH:
if not self.measurements_file:
self.measurements_file = open(
os.path.join(
CARLA_OUT_PATH,
"measurements_{}.json".format(self.episode_id)), "w")
self.measurements_file.write(json.dumps(py_measurements))
self.measurements_file.write("\n")
if done:
self.measurements_file.close()
self.measurements_file = None
if self.config["convert_images_to_video"]:
self.images_to_video()
self.num_steps += 1
image = self.preprocess_image(image)
return (self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def images_to_video(self):
videos_dir = os.path.join(CARLA_OUT_PATH, "Videos")
if not os.path.exists(videos_dir):
os.makedirs(videos_dir)
ffmpeg_cmd = (
"ffmpeg -loglevel -8 -r 60 -f image2 -s {x_res}x{y_res} "
"-start_number 0 -i "
"{img}_%04d.jpg -vcodec libx264 {vid}.mp4 && rm -f {img}_*.jpg "
).format(x_res=self.config["render_x_res"],
y_res=self.config["render_y_res"],
vid=os.path.join(videos_dir, self.episode_id),
img=os.path.join(CARLA_OUT_PATH, "CameraRGB",
self.episode_id))
print("Executing ffmpeg command", ffmpeg_cmd)
subprocess.call(ffmpeg_cmd, shell=True)
def preprocess_image(self, image):
if self.config[
"use_sensor"] == 'use_semantic': # image.data: uint8(0 ~ 12)
data = image.data * 21 # data: uint8(0 ~ 255)
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 1)
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.expand_dims(
data, 2) # data: uint8(0 ~ 255), shape(y_res, x_res, 1)
elif self.config["use_sensor"] == 'use_depth': # depth: float64(0 ~ 1)
# data = (image.data - 0.5) * 2
data = image.data * 255 # data: float64(0 ~ 255)
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"],
1) # shape(render_y_res,render_x_res,1)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA) # shape(y_res, x_res)
data = np.expand_dims(
data, 2) # data: float64(0 ~ 255), shape(y_res, x_res, 1)
elif self.config[
"use_sensor"] == 'use_logdepth': # depth: float64(0 ~ 1)
data = (np.log(image.data) +
7.0) * 255.0 / 7.0 # data: float64(0 ~ 255)
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"],
1) # shape(render_y_res,render_x_res,1)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA) # shape(y_res, x_res)
data = np.expand_dims(
data, 2) # data: float64(0 ~ 255), shape(y_res, x_res, 1)
elif self.config["use_sensor"] == 'use_rgb':
data = image.data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data = cv2.resize(
data,
(self.config["x_res"], self.config["y_res"]
), # data: uint8(0 ~ 255), shape(y_res, x_res, 3)
interpolation=cv2.INTER_AREA)
# data = (data.astype(np.float32) - 128) / 128
elif self.config["use_sensor"] == 'use_2rgb':
data_l, data_r = image[0].data, image[0].data
data_l = data_l.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data_r = data_r.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data_l = cv2.resize(data_l,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data_r = cv2.resize(data_r,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.concatenate(
(data_l, data_r),
axis=2) # data: uint8(0 ~ 255), shape(y_res, x_res, 6)
return data
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
if self.enable_autopilot:
self.autopilot = measurements.player_measurements.autopilot_control
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
observation = None
if self.config["use_sensor"] == 'use_semantic':
camera_name = "CameraSemantic"
elif self.config["use_sensor"] in ['use_depth', 'use_logdepth']:
camera_name = "CameraDepth"
elif self.config["use_sensor"] == 'use_rgb':
camera_name = "CameraRGB"
elif self.config["use_sensor"] == 'use_2rgb':
camera_name = ["CameraRGB_L", "CameraRGB_R"]
# for name, image in sensor_data.items():
# if name == camera_name:
# observation = image
if camera_name == ["CameraRGB_L", "CameraRGB_R"]:
observation = [
sensor_data["CameraRGB_L"], sensor_data["CameraRGB_R"]
]
else:
observation = sensor_data[camera_name]
cur = measurements.player_measurements
if self.config["enable_planner"]:
# modify next_command
current_pos = np.array([cur.transform.location.x, cur.transform.location.y])\
+ np.array([cur.transform.orientation.x, cur.transform.orientation.y]) * 5.0
dist_intersection_current_pos = np.linalg.norm(
self.intersections_pos[:, :2] - current_pos, axis=1)
is_near_intersection = np.min(dist_intersection_current_pos) < 18.0
if not is_near_intersection:
self.next_command = "LANE_FOLLOW"
# goal_heading
if is_near_intersection:
cur_intersection = self.intersections_pos[
dist_intersection_current_pos.argmin(), :2]
self.dist_to_intersection = np.linalg.norm(cur_intersection -
current_pos)
else:
self.goal_heading_degree = 0.0
self.dist_to_intersection = 1000.0
if is_near_intersection and np.linalg.norm(self.pre_intersection -
cur_intersection) > 0.1:
self.next_command = COMMANDS_ENUM[self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z], \
[cur.transform.orientation.x, cur.transform.orientation.y, GROUND_Z], \
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], \
[self.end_pos.orientation.x, self.end_pos.orientation.y, GROUND_Z] \
)]
if self.next_command == "LANE_FOLLOW":
self.next_command = random.choice(
["GO_STRAIGHT", "TURN_LEFT", "TURN_RIGHT"])
cur_heading0 = cur_intersection - current_pos
cur_heading_1 = cur_heading0 / np.linalg.norm(cur_heading0)
cur_heading_degree = self.headings_degree[np.linalg.norm(
cur_heading_1 - self.headings, axis=1).argmin()]
self.goal_heading_degree = self.delta_degree(
cur_heading_degree + self.lrs_degree[self.next_command])
self.pre_intersection = cur_intersection
else:
self.next_command = "LANE_FOLLOW"
if self.next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
self.end_pos = self.positions[random.choice(
self.config["scenarios"])['end_pos_id']]
# elif self.config["enable_planner"]:
# distance_to_goal = self.planner.get_shortest_path_distance([
# cur.transform.location.x, cur.transform.location.y, GROUND_Z
# ], [
# cur.transform.orientation.x, cur.transform.orientation.y,
# GROUND_Z
# ], [self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
# self.end_pos.orientation.x, self.end_pos.orientation.y,
# GROUND_Z
# ])
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(
np.linalg.norm([ # default norm: L2 norm
cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y
]))
# displacement
if self.cnt1 > 70 and self.cnt1 % 30 == 0:
self.displacement = float(
np.linalg.norm([
cur.transform.location.x - self.pre_pos.x,
cur.transform.location.y - self.pre_pos.y
]))
self.pre_pos = cur.transform.location
self.cnt1 += 1
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": np.array(cur.forward_speed), ###
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"collided": collision_(cur), ###
"intersection_offroad": np.array(cur.intersection_offroad), ###
"intersection_otherlane":
np.array(cur.intersection_otherlane), ###
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command": self.next_command,
"next_command_id": COMMAND_ORDINAL[self.next_command],
"displacement": self.displacement,
"is_near_intersection": is_near_intersection,
"goal_heading_degree": self.goal_heading_degree,
"angular_speed_degree": self.angular_speed_degree, ###
"current_heading_degree":
cur.transform.rotation.yaw, # left-, right+, (-180 ~ 180) degrees
"dist_to_intersection": self.dist_to_intersection
}
if CARLA_OUT_PATH and self.config["log_images"]:
for name, image in sensor_data.items():
out_dir = os.path.join(CARLA_OUT_PATH, name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(
out_dir, "{}_{:>04}.jpg".format(self.episode_id,
self.num_steps))
scipy.misc.imsave(out_file,
image.data) # image.data without preprocess.
assert observation is not None, sensor_data
return observation, py_measurements
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0):
"""
Args
city_name:
name_to_save:
continue_experiment:
save_images:
distance_for_success:
collisions_as_failure: if this flag is set to true, episodes will terminate as failure, when the car collides.
"""
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
# TO keep track of the previous collisions
self._previous_pedestrian_collision = 0
self._previous_vehicle_collision = 0
self._previous_other_collision = 0
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments(
)[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance, col_ped, col_veh, col_oth) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index), experiment_suite.metrics_parameters,
experiment_suite.collision_as_failure)
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result,
col_ped, col_veh, col_oth)
# Write the details of this episode.
self._recording.write_measurements_results(
experiment, rep, pose, reward_vec, control_vec)
if result > 0:
logging.info(
'+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
def _has_agent_collided(self, measurement, metrics_parameters):
"""
This function must have a certain state and only look to one measurement.
"""
collided_veh = 0
collided_ped = 0
collided_oth = 0
if (measurement.collision_vehicles - self._previous_vehicle_collision) \
> metrics_parameters['collision_vehicles']['threshold']/2.0:
collided_veh = 1
if (measurement.collision_pedestrians - self._previous_pedestrian_collision) \
> metrics_parameters['collision_pedestrians']['threshold']/2.0:
collided_ped = 1
if (measurement.collision_other - self._previous_other_collision) \
> metrics_parameters['collision_other']['threshold']/2.0:
collided_oth = 1
self._previous_pedestrian_collision = measurement.collision_pedestrians
self._previous_vehicle_collision = measurement.collision_other
return collided_ped, collided_veh, collided_oth
def _is_agent_stuck(self, measurements, stuck_vec, old_coll):
# break the episode when the agent is stuck on a static object
coll_other = measurements.collision_other
coll_other -= old_coll
otherlane = measurements.intersection_otherlane > 0.4
offroad = measurements.intersection_offroad > 0.3
logging.info(
"offroad: {}, otherlane: {}, coll_other: {}, old_coll: {}".format(
offroad, otherlane, coll_other, old_coll))
# if still driving or got unstuck (v > 4km/h)
if measurements.forward_speed * 3.6 > 4:
cycle_signal(stuck_vec, 0)
if coll_other:
old_coll += coll_other
elif offroad or otherlane or coll_other:
cycle_signal(stuck_vec, 1)
else:
cycle_signal(stuck_vec, 0)
return all(stuck_vec), stuck_vec, old_coll
def _run_navigation_episode(self, agent, client, time_out, target,
episode_name, metrics_parameters,
collision_as_failure):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
metrics_object: The metrics object to check for collisions
"""
# Send an initial command.
time.sleep(2)
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
### Reset CAL agent
agent.reset_state()
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
col_ped, col_veh, col_oth = 0, 0, 0
fail = False
success = False
### own metrics
stuck_vec = [0] * 60 # measure for 60 frames (6 seconds)
center_distance_vec = []
old_collision_value = 0
direction_vec = []
while not fail and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(
measurements.player_measurements.transform, target)
# Agent process the data.
control = agent.run_step(measurements, sensor_data, directions,
target)
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ', control.steer,
control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
logging.info('Distance to target: %f', float(distance))
# Check if reach the target
col_ped, col_veh, col_oth = self._has_agent_collided(
measurements.player_measurements, metrics_parameters)
### CHANGE TO ORIGINAL CODE #####################
is_stuck, stuck_vec, old_collision_value = self._is_agent_stuck(
measurements.player_measurements, stuck_vec,
old_collision_value)
if distance < self._distance_for_success:
success = True
elif (current_timestamp - initial_timestamp) > (time_out * 1000):
fail = True
elif is_stuck:
fail = True
elif collision_as_failure and (col_ped or col_veh or col_oth):
fail = True
time_remain = (time_out * 1000 -
(current_timestamp - initial_timestamp)) / 1000
logging.info('Time remaining: %i m %i s', time_remain / 60,
time_remain % 60)
logging.info('')
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp
) / 1000.0, distance, col_ped, col_veh, col_oth
return 0, measurement_vec, control_vec, time_out, distance, col_ped, col_veh, col_oth
class CarlaEnvironmentWrapper(EnvironmentWrapper):
def __init__(self,
num_speedup_steps=30,
require_explicit_reset=True,
is_render_enabled=False,
early_termination_enabled=False,
run_offscreen=False,
save_screens=False,
port=2000,
gpu=0,
discrete_control=True,
kill_when_connection_lost=True,
city_name="Town01",
channel_last=True):
EnvironmentWrapper.__init__(self, is_render_enabled, save_screens)
print("port:", port)
self.episode_max_time = 1000000
self.allow_braking = True
self.log_path = os.path.join(DEFAULT_CARLA_LOG_DIR, "CarlaLogs.txt")
self.num_speedup_steps = num_speedup_steps
self.is_game_ready_for_input = False
self.run_offscreen = run_offscreen
self.kill_when_connection_lost = kill_when_connection_lost
# server configuration
self.port = port
self.gpu = gpu
self.host = 'localhost'
self.level = 'town1'
self.map = CarlaLevel().get(self.level)
# experiment = basic_experiment_suite.BasicExperimentSuite(city_name)
experiment = CoRL2017(city_name)
self.experiments = experiment.get_experiments()
self.experiment_type = 0
self.planner = Planner(city_name)
self.car_speed = 0
self.is_game_setup = False # Will be true only when setup_client_and_server() is called, either explicitly, or by reset()
# action space
self.discrete_controls = discrete_control
self.action_space_size = 3
self.action_space_high = np.array([1, 1, 1])
self.action_space_low = np.array([-1, -1, -1])
self.action_space_abs_range = np.maximum(
np.abs(self.action_space_low), np.abs(self.action_space_high))
self.steering_strength = 0.35
self.gas_strength = 1.0
self.brake_strength = 0.6
self.actions = {
0: [0., 0.],
1: [0., -self.steering_strength],
2: [0., self.steering_strength],
3: [self.gas_strength - 0.15, 0.],
4: [-self.brake_strength, 0],
5: [self.gas_strength - 0.3, -self.steering_strength],
6: [self.gas_strength - 0.3, self.steering_strength],
7: [-self.brake_strength, -self.steering_strength],
8: [-self.brake_strength, self.steering_strength]
}
self.actions_description = [
'NO-OP', 'TURN_LEFT', 'TURN_RIGHT', 'GAS', 'BRAKE',
'GAS_AND_TURN_LEFT', 'GAS_AND_TURN_RIGHT', 'BRAKE_AND_TURN_LEFT',
'BRAKE_AND_TURN_RIGHT'
]
if discrete_control:
self.action_space = Discrete(len(self.actions))
else:
self.action_space = Box(low=self.action_space_low,
high=self.action_space_high)
self.observation_space = Box(low=-np.inf,
high=np.inf,
shape=[88, 200, 3])
# measurements
self.measurements_size = (1, )
self.pre_image = None
self.first_debug = True
self.channel_last = channel_last
def setup_client_and_server(self, reconnect_client_only=False):
# open the server
if not reconnect_client_only:
self.server, self.out = self._open_server()
self.server_pid = self.server.pid # To kill incase child process gets lost
print("setup server, out:", self.server_pid, "dockerid", self.out)
while True:
try:
self.game = CarlaClient(self.host, self.port, timeout=99999999)
self.game.connect(
connection_attempts=100
) # It's taking a very long time for the server process to spawn, so the client needs to wait or try sufficient no. of times lol
self.game.load_settings(CarlaSettings())
self.game.start_episode(0)
self.is_game_setup = self.server and self.game
print("setup client")
# self.out = shell("docker ps -q")
# print("dockerid", self.out)
return
except TCPConnectionError as error:
print(error)
time.sleep(1)
def close_client_and_server(self):
self._close_server()
print("Disconnecting the client")
self.game.disconnect()
self.game = None
self.server = None
self.is_game_setup = False
return
def _open_server(self):
# Note: There is no way to disable rendering in CARLA as of now
# https://github.com/carla-simulator/carla/issues/286
# decrease the window resolution if you want to see if performance increases
# Command: $CARLA_ROOT/CarlaUE4.sh /Game/Maps/Town02 -benchmark -carla-server -fps=15 -world-port=9876 -windowed -ResX=480 -ResY=360 -carla-no-hud
# To run off_screen: SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE=0 <command> #https://github.com/carla-simulator/carla/issues/225
my_env = None
if self.run_offscreen:
my_env = {
**os.environ,
'SDL_VIDEODRIVER': 'offscreen',
'SDL_HINT_CUDA_DEVICE': '0',
}
with open(self.log_path, "wb") as out:
#docker <19.03
# p = subprocess.Popen(['docker', 'run', '--rm', '-d', '-p',
# str(self.port) + '-' + str(self.port + 2) + ':' + str(self.port) + '-' + str(self.port + 2),
# '--runtime=nvidia', '-e', 'NVIDIA_VISIBLE_DEVICES='+str(self.gpu), "carlasim/carla:0.8.4",
# '/bin/bash', 'CarlaUE4.sh', self.map, '-windowed',
# '-benchmark', '-fps=10', '-world-port=' + str(self.port)], shell=False,
# stdout=subprocess.PIPE)
#docker=19.03
p = subprocess.Popen([
'docker', 'run', '--rm', '-d', '-p',
str(self.port) + '-' + str(self.port + 2) + ':' +
str(self.port) + '-' + str(self.port + 2),
'--gpus=' + str(self.gpu), "carlasim/carla:0.8.4", '/bin/bash',
'CarlaUE4.sh', self.map, '-windowed', '-benchmark', '-fps=10',
'-world-port=' + str(self.port)
],
shell=False,
stdout=subprocess.PIPE)
# docker_id = shell("docker ps -q")
docker_out, err = p.communicate()
# cmd = [path.join(environ.get('CARLA_ROOT'), 'CarlaUE4.sh'), self.map,
# "-benchmark", "-carla-server", "-fps=10", "-world-port={}".format(self.port),
# "-windowed -ResX={} -ResY={}".format(carla_config.server_width, carla_config.server_height),
# "-carla-no-hud"]
# p = subprocess.Popen(cmd, stdout=out, stderr=out, env=my_env, preexec_fn=os.setsid)
# p = subprocess.Popen(cmd, env=my_env, preexec_fn=os.setsid)
return p, docker_out
def _close_server(self):
if self.kill_when_connection_lost:
print("kill before")
# os.kill(os.getpgid(self.server.pid), signal.SIGKILL)
# self.server.kill()
print(self.out)
subprocess.call(['docker', 'stop', self.out[:-1]])
while True:
cmd = shell("docker ps -q")
if cmd == "":
break
print("kill after")
return
# no_of_attempts = 0
# while is_process_alive(self.server_pid):
# try:
# self.server.terminate()
# self.server.wait()
# os.killpg(self.server.pid, signal.SIGTERM)
# time.sleep(10)
# except Exception as error:
# print(error)
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self.planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _update_state(self):
# get measurements and observations
measurements = []
while type(measurements) == list:
try:
measurements, sensor_data = self.game.read_data()
except:
print(
"Connection to server lost while reading state. Reconnecting..........."
)
# self.game.disconnect()
# self.setup_client_and_server(reconnect_client_only=True)
import psutil
info = psutil.virtual_memory()
print("memory used", str(info.percent))
self.close_client_and_server()
self.setup_client_and_server(reconnect_client_only=False)
scene = self.game.load_settings(self.cur_experiment.conditions)
self.positions = scene.player_start_spots
self.start_point = self.positions[self.pose[0]]
self.end_point = self.positions[self.pose[1]]
self.game.start_episode(self.pose[0])
self.done = True
current_point = measurements.player_measurements.transform
direction = self._get_directions(current_point, self.end_point)
speed = measurements.player_measurements.forward_speed
self.reward = 0
dist = sldist([current_point.location.x, current_point.location.y],
[self.end_point.location.x, self.end_point.location.y])
if direction == 5: #go straight
if abs(self.control.steer) > 0.2:
self.reward -= 20
self.reward += min(35, speed * 3.6)
elif direction == 2: #follow lane
self.reward += min(35, speed * 3.6)
elif direction == 3: #turn left ,steer negtive
if self.control.steer > 0:
self.reward -= 15
if speed * 3.6 <= 20:
self.reward += speed * 3.6
else:
self.reward += 40 - speed * 3.6
elif direction == 4: #turn right
if self.control.steer < 0:
self.reward -= 15
if speed * 3.6 <= 20:
self.reward += speed * 3.6
else:
self.reward += 40 - speed * 3.6
elif direction == 0:
self.done = True
self.reward += 100
direction = 2
print("success", dist)
else:
print("error direction")
direction = 5
direction -= 2
direction = int(direction)
intersection_offroad = measurements.player_measurements.intersection_offroad
intersection_otherlane = measurements.player_measurements.intersection_otherlane
collision_veh = measurements.player_measurements.collision_vehicles
collision_ped = measurements.player_measurements.collision_pedestrians
collision_other = measurements.player_measurements.collision_other
if intersection_otherlane > 0 or intersection_offroad > 0 or collision_ped > 0 or collision_veh > 0:
self.reward -= 100
elif collision_other > 0:
self.reward -= 50
if collision_other > 0 or collision_veh > 0 or collision_ped > 0:
self.done = True
if intersection_offroad > 0.2 or intersection_otherlane > 0.2:
self.done = True
if speed * 3.6 <= 1:
self.nospeed_time += 1
if self.nospeed_time > 100:
self.done = True
self.reward -= 1
else:
self.nospeed_time = 0
# speed = min(1, speed/10.0)
speed = speed / 25
# print(measurements)
# print(sensor_data)
self.observation = {
"img": self.process_image(sensor_data['CameraRGB'].data),
"speed": speed,
"direction": direction
}
return self.observation, self.reward, self.done
def encode_obs(self, image):
if self.pre_image is None:
self.pre_image = image
img = np.concatenate([self.pre_image, image], axis=2)
self.pre_image = image
return img
def process_image(self, data):
rgb_img = data[115:510, :]
rgb_img = cv2.resize(rgb_img, (200, 88), interpolation=cv2.INTER_AREA)
if not self.channel_last:
rgb_img = np.transpose(rgb_img, (2, 0, 1))
# return cv2.resize(
# data, (80, 80),
# interpolation=cv2.INTER_AREA)
return rgb_img
def _take_action(self, action_idx):
if not self.is_game_setup:
print("Reset the environment duh by reset() before calling step()")
sys.exit(1)
if type(action_idx) == np.int64 or type(action_idx) == np.int:
action = self.actions[action_idx]
else:
action = action_idx
self.control = VehicleControl()
if len(action) == 3:
self.control.throttle = np.clip(action[1], 0, 1)
self.control.steer = np.clip(action[0], -1, 1)
self.control.brake = np.clip(action[2], 0, 1)
else:
self.control.throttle = np.clip(action[0], 0, 1)
self.control.steer = np.clip(action[1], -1, 1)
self.control.brake = np.abs(np.clip(action[0], -1, 0))
if not self.allow_braking:
self.control.brake = 0
self.control.hand_brake = False
self.control.reverse = False
controls_sent = False
while not controls_sent:
try:
self.game.send_control(self.control)
controls_sent = True
# print(self.control)
# #
# rand = random.randint(0, 4)
# if rand == 0 and self.first_debug:
# self.first_debug = False
# raise Exception
except:
print(
"Connection to server lost while sending controls. Reconnecting..........."
)
import psutil
info = psutil.virtual_memory()
print("memory used", str(info.percent))
self.close_client_and_server()
self.setup_client_and_server(reconnect_client_only=False)
scene = self.game.load_settings(self.cur_experiment.conditions)
self.positions = scene.player_start_spots
self.start_point = self.positions[self.pose[0]]
self.end_point = self.positions[self.pose[1]]
self.game.start_episode(self.pose[0])
self.done = True
controls_sent = False
return
def _restart_environment_episode(self, force_environment_reset=True):
if not force_environment_reset and not self.done and self.is_game_setup:
print("Can't reset dude, episode ain't over yet")
return None # User should handle this
self.is_game_ready_for_input = False
if not self.is_game_setup:
self.setup_client_and_server()
experiment_type = random.randint(0, 0)
self.cur_experiment = self.experiments[experiment_type]
self.pose = random.choice(self.cur_experiment.poses)
# self.cur_experiment = self.experiments[0]
# self.pose = self.cur_experiment.poses[0]
scene = self.game.load_settings(self.cur_experiment.conditions)
self.positions = scene.player_start_spots
self.start_point = self.positions[self.pose[0]]
self.end_point = self.positions[self.pose[1]]
# print("start episode")
while True:
try:
self.game.start_episode(self.pose[0])
# start the game with some initial speed
self.car_speed = 0
self.nospeed_time = 0
observation = None
for i in range(self.num_speedup_steps):
observation, reward, done, _ = self.step([0, 1.0, 0])
self.observation = observation
self.is_game_ready_for_input = True
break
except Exception as error:
print(error)
self.game.connect()
self.game.start_episode(self.pose[0])
return observation
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0):
"""
Args
city_name:
name_to_save:
continue_experiment:
save_images:
distance_for_success:
collisions_as_failure: if this flag is set to true, episodes will terminate as failure, when the car collides.
"""
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
self._map = self._planner._city_track.get_map()
# TO keep track of the previous collisions
self._previous_pedestrian_collision = 0
self._previous_vehicle_collision = 0
self._previous_other_collision = 0
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments(
)[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
logging.info('Timeout for Episode: %f', time_out)
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance, col_ped,
col_veh, col_oth, number_of_red_lights, number_of_green_lights) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index), experiment_suite.metrics_parameters,
experiment_suite.collision_as_failure,
experiment_suite.traffic_light_as_failure)
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result,
col_ped, col_veh, col_oth, number_of_red_lights,
number_of_green_lights)
# Write the details of this episode.
self._recording.write_measurements_results(
experiment, rep, pose, reward_vec, control_vec)
if result > 0:
logging.info(
'+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
def _has_agent_collided(self, measurement, metrics_parameters):
"""
This function must have a certain state and only look to one measurement.
"""
collided_veh = 0
collided_ped = 0
collided_oth = 0
if (measurement.collision_vehicles - self._previous_vehicle_collision) \
> metrics_parameters['collision_vehicles']['threshold']/2.0:
collided_veh = 1
if (measurement.collision_pedestrians - self._previous_pedestrian_collision) \
> metrics_parameters['collision_pedestrians']['threshold']/2.0:
collided_ped = 1
if (measurement.collision_other - self._previous_other_collision) \
> metrics_parameters['collision_other']['threshold']/2.0:
collided_oth = 1
self._previous_pedestrian_collision = measurement.collision_pedestrians
self._previous_vehicle_collision = measurement.collision_vehicles
self._previous_other_collision = measurement.collision_other
return collided_ped, collided_veh, collided_oth
def _is_traffic_light_active(self, agent, orientation):
x_agent = agent.traffic_light.transform.location.x
y_agent = agent.traffic_light.transform.location.y
def search_closest_lane_point(x_agent, y_agent, depth):
step_size = 4
if depth > 1:
return None
try:
degrees = self._map.get_lane_orientation_degrees(
[x_agent, y_agent, 38])
#print (degrees)
except:
return None
if not self._map.is_point_on_lane([x_agent, y_agent, 38]):
#print (" Not on lane ")
result = search_closest_lane_point(x_agent + step_size,
y_agent, depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent,
y_agent + step_size,
depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent + step_size,
y_agent + step_size,
depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent + step_size,
y_agent - step_size,
depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent - step_size,
y_agent + step_size,
depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent - step_size,
y_agent, depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent,
y_agent - step_size,
depth + 1)
if result is not None:
return result
result = search_closest_lane_point(x_agent - step_size,
y_agent - step_size,
depth + 1)
if result is not None:
return result
else:
#print(" ON Lane ")
if degrees < 6:
return [x_agent, y_agent]
else:
return None
closest_lane_point = search_closest_lane_point(x_agent, y_agent, 0)
car_direction = math.atan2(orientation.y, orientation.x) + 3.1415
if car_direction > 6.0:
car_direction -= 6.0
return math.fabs(
car_direction - self._map.get_lane_orientation_degrees(
[closest_lane_point[0], closest_lane_point[1], 38])) < 1
def _test_for_traffic_lights(self, measurement):
"""
This function tests if the car passed into a traffic light, returning 'red'
if it crossed a red light , 'green' if it crossed a green light or none otherwise
Args:
measurement: all the measurements collected by carla 0.8.4
Returns:
"""
def is_on_burning_point(_map, location):
# We get the current lane orientation
ori_x, ori_y = _map.get_lane_orientation(
[location.x, location.y, 38])
# We test to walk in direction of the lane
future_location_x = location.x
future_location_y = location.y
for i in range(3):
future_location_x += ori_x
future_location_y += ori_y
# Take a point on a intersection in the future
location_on_intersection_x = future_location_x + 2 * ori_x
location_on_intersection_y = future_location_y + 2 * ori_y
if not _map.is_point_on_intersection([future_location_x,
future_location_y,
38]) and \
_map.is_point_on_intersection([location_on_intersection_x,
location_on_intersection_y,
38]):
return True
return False
# Check nearest traffic light with the correct orientation state.
player_x = measurement.player_measurements.transform.location.x
player_y = measurement.player_measurements.transform.location.y
# The vehicle is on an intersection
# THIS IS THE PLACE TO VERIFY FOR A TL BURN
for agent in measurement.non_player_agents:
if agent.HasField('traffic_light'):
if not self._map.is_point_on_intersection(
[player_x, player_y, 38]):
x_agent = agent.traffic_light.transform.location.x
y_agent = agent.traffic_light.transform.location.y
tl_vector, tl_dist = get_vec_dist(x_agent, y_agent,
player_x, player_y)
if self._is_traffic_light_active(
agent, measurement.player_measurements.transform.
orientation):
if is_on_burning_point(self._map,
measurement.player_measurements.transform.location)\
and tl_dist < 6.0:
if agent.traffic_light.state != 0: # Not green
return 'red'
else:
return 'green'
return None
def _run_navigation_episode(self, agent, client, time_out, target,
episode_name, metrics_parameters,
collision_as_failure,
traffic_light_as_failure):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
metrics_object: The metrics object to check for collisions
"""
# Send an initial command.
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
col_ped, col_veh, col_oth = 0, 0, 0
traffic_light_state, number_red_lights, number_green_lights = None, 0, 0
fail = False
success = False
not_count = 0
while not fail and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(
measurements.player_measurements.transform, target)
# Agent process the data.
control = agent.run_step(measurements, sensor_data, directions,
target)
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ', control.steer,
control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
logging.info('Timestamp %f', current_timestamp)
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
logging.info('Status:')
logging.info('[d=%f] c_x = %f, c_y = %f ---> t_x = %f, t_y = %f',
float(distance), current_x, current_y,
target.location.x, target.location.y)
# Check if reach the target
col_ped, col_veh, col_oth = self._has_agent_collided(
measurements.player_measurements, metrics_parameters)
# test if car crossed the traffic light
traffic_light_state = self._test_for_traffic_lights(measurements)
if traffic_light_state == 'red' and not_count == 0:
number_red_lights += 1
not_count = 20
elif traffic_light_state == 'green' and not_count == 0:
number_green_lights += 1
not_count = 20
else:
not_count -= 1
not_count = max(0, not_count)
if distance < self._distance_for_success:
success = True
elif (current_timestamp - initial_timestamp) > (time_out * 1000):
fail = True
elif collision_as_failure and (col_ped or col_veh or col_oth):
fail = True
elif traffic_light_as_failure and traffic_light_state == 'red':
fail = True
logging.info('Traffic Lights:')
logging.info('red %f green %f, total %f', number_red_lights,
number_green_lights,
number_red_lights + number_green_lights)
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp) / 1000.0, distance, col_ped, col_veh, col_oth, \
number_red_lights, number_green_lights
return 0, measurement_vec, control_vec, time_out, distance, col_ped, col_veh, col_oth, \
number_red_lights, number_green_lights
class CarlaEnv(object):
'''
An OpenAI Gym Environment for CARLA.
'''
def __init__(self,
obs_converter,
action_converter,
env_id,
random_seed=0,
exp_suite_name='TrainingSuite',
reward_class_name='RewardCarla',
host='127.0.0.1',
port=2000,
city_name='Town01',
subset=None,
video_every=100,
video_dir='./video/',
distance_for_success=2.0,
benchmark=False):
self.logger = get_carla_logger()
self.logger.info('Environment {} running in port {}'.format(
env_id, port))
self.host, self.port = host, port
self.id = env_id
self._obs_converter = obs_converter
self.observation_space = obs_converter.get_observation_space()
self._action_converter = action_converter
self.action_space = self._action_converter.get_action_space()
if benchmark:
self._experiment_suite = getattr(experiment_suites_benchmark,
exp_suite_name)(city_name)
else:
self._experiment_suite = getattr(experiment_suites,
exp_suite_name)(city_name, subset)
self._reward = getattr(rewards, reward_class_name)()
self._experiments = self._experiment_suite.get_experiments()
self.subset = subset
self._make_carla_client(host, port)
self._distance_for_success = distance_for_success
self._planner = Planner(city_name)
self.done = False
self.last_obs = None
self.last_distance_to_goal = None
self.last_direction = None
self.last_measurements = None
np.random.seed(random_seed)
self.video_every = video_every
self.video_dir = video_dir
self.video_writer = None
self._success = False
self._failure_timeout = False
self._failure_collision = False
self.benchmark = benchmark
self.benchmark_index = [0, 0, 0]
try:
if not os.path.isdir(self.video_dir):
os.makedirs(self.video_dir)
except OSError:
pass
self.steps = 0
self.num_episodes = 0
def step(self, action):
if self.done:
raise ValueError(
'self.done should always be False when calling step')
while True:
try:
# Send control
control = self._action_converter.action_to_control(
action, self.last_measurements)
self._client.send_control(control)
# Gather the observations (including measurements, sensor and directions)
measurements, sensor_data = self._client.read_data()
self.last_measurements = measurements
current_timestamp = measurements.game_timestamp
distance_to_goal = self._get_distance_to_goal(
measurements, self._target)
self.last_distance_to_goal = distance_to_goal
directions = self._get_directions(
measurements.player_measurements.transform, self._target)
self.last_direction = directions
obs = self._obs_converter.convert(measurements, sensor_data,
directions, self._target,
self.id)
if self.video_writer is not None and self.steps % 2 == 0:
self._raster_frame(sensor_data, measurements, directions,
obs)
self.last_obs = obs
except CameraException:
self.logger.debug('Camera Exception in step()')
obs = self.last_obs
distance_to_goal = self.last_distance_to_goal
current_timestamp = self.last_measurements.game_timestamp
except TCPConnectionError as e:
self.logger.debug(
'TCPConnectionError inside step(): {}'.format(e))
self.done = True
return self.last_obs, 0.0, True, {'carla-reward': 0.0}
break
# Check if terminal state
timeout = (current_timestamp -
self._initial_timestamp) > (self._time_out * 1000)
collision, _ = self._is_collision(measurements)
success = distance_to_goal < self._distance_for_success
if timeout:
self.logger.debug('Timeout')
self._failure_timeout = True
if collision:
self.logger.debug('Collision')
self._failure_collision = True
if success:
self.logger.debug('Success')
self.done = timeout or collision or success
# Get the reward
env_state = {
'timeout': timeout,
'collision': collision,
'success': success
}
reward = self._reward.get_reward(measurements, self._target,
self.last_direction, control,
env_state)
# Additional information
info = {'carla-reward': reward}
self.steps += 1
return obs, reward, self.done, info
def reset(self):
# Loop forever due to TCPConnectionErrors
while True:
try:
self._reward.reset_reward()
self.done = False
if self.video_writer is not None:
try:
self.video_writer.close()
except Exception as e:
self.logger.debug(
'Error when closing video writer in reset')
self.logger.error(e)
self.video_writer = None
if self.benchmark:
end_indicator = self._new_episode_benchmark()
if end_indicator is False:
return False
else:
self._new_episode()
# Hack: Try sleeping so that the server is ready. Reduces the number of TCPErrors
time.sleep(4)
# measurements, sensor_data = self._client.read_data()
self._client.send_control(VehicleControl())
measurements, sensor_data = self._client.read_data()
self._initial_timestamp = measurements.game_timestamp
self.last_measurements = measurements
self.last_distance_to_goal = self._get_distance_to_goal(
measurements, self._target)
directions = self._get_directions(
measurements.player_measurements.transform, self._target)
self.last_direction = directions
obs = self._obs_converter.convert(measurements, sensor_data,
directions, self._target,
self.id)
self.last_obs = obs
self.done = False
self._success = False
self._failure_timeout = False
self._failure_collision = False
return obs
except CameraException:
self.logger.debug('Camera Exception in reset()')
continue
except TCPConnectionError as e:
self.logger.debug('TCPConnectionError in reset()')
self.logger.error(e)
# Disconnect and reconnect
self.disconnect()
time.sleep(5)
self._make_carla_client(self.host, self.port)
def disconnect(self):
if self.video_writer is not None:
try:
self.video_writer.close()
except Exception as e:
self.logger.debug(
'Error when closing video writer in disconnect')
self.logger.error(e)
self.video_writer = None
self._client.disconnect()
def _raster_frame(self, sensor_data, measurements, directions, obs):
frame = sensor_data['CameraRGB'].data.copy()
cv2.putText(frame,
text='Episode number: {:,}'.format(self.num_episodes - 1),
org=(50, 50),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
color=[0, 0, 0],
thickness=2)
cv2.putText(frame,
text='Environment steps: {:,}'.format(self.steps),
org=(50, 80),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
color=[0, 0, 0],
thickness=2)
REACH_GOAL = 0.0
GO_STRAIGHT = 5.0
TURN_RIGHT = 4.0
TURN_LEFT = 3.0
LANE_FOLLOW = 2.0
if np.isclose(directions, REACH_GOAL):
dir_str = 'REACH GOAL'
elif np.isclose(directions, GO_STRAIGHT):
dir_str = 'GO STRAIGHT'
elif np.isclose(directions, TURN_RIGHT):
dir_str = 'TURN RIGHT'
elif np.isclose(directions, TURN_LEFT):
dir_str = 'TURN LEFT'
elif np.isclose(directions, LANE_FOLLOW):
dir_str = 'LANE FOLLOW'
else:
raise ValueError(directions)
cv2.putText(frame,
text='Direction: {}'.format(dir_str),
org=(50, 110),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
color=[0, 0, 0],
thickness=2)
cv2.putText(frame,
text='Speed: {:.02f}'.format(
measurements.player_measurements.forward_speed * 3.6),
org=(50, 140),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
color=[0, 0, 0],
thickness=2)
cv2.putText(frame,
text='rel_x: {:.02f}, rel_y: {:.02f}'.format(
obs['v'][-2].item(), obs['v'][-1].item()),
org=(50, 170),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=1.0,
color=[0, 0, 0],
thickness=2)
self.video_writer.writeFrame(frame)
def _get_distance_to_goal(self, measurements, target):
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
distance_to_goal = np.linalg.norm(np.array([current_x, current_y]) - \
np.array([target.location.x, target.location.y]))
return distance_to_goal
def _new_episode(self):
experiment_idx = np.random.randint(0, len(self._experiments))
experiment = self._experiments[experiment_idx]
exp_settings = experiment.conditions
exp_settings.set(QualityLevel='Low')
positions = self._client.load_settings(exp_settings).player_start_spots
idx_pose = np.random.randint(0, len(experiment.poses))
pose = experiment.poses[idx_pose]
self.logger.info('Env {} gets experiment {} with pose {}'.format(
self.id, experiment_idx, idx_pose))
start_index = pose[0]
end_index = pose[1]
self._client.start_episode(start_index)
self._time_out = self._experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
self._target = positions[end_index]
self._episode_name = str(experiment.Conditions.WeatherId) + '_' \
+ str(experiment.task) + '_' + str(start_index) \
+ '_' + str(end_index)
if ((self.num_episodes % self.video_every) == 0) and (self.id == 0):
video_path = os.path.join(
self.video_dir, '{:08d}_'.format(self.num_episodes) +
self._episode_name + '.mp4')
self.logger.info('Writing video at {}'.format(video_path))
self.video_writer = skvideo.io.FFmpegWriter(
video_path, inputdict={'-r': '30'}, outputdict={'-r': '30'})
else:
self.video_writer = None
self.num_episodes += 1
def _new_episode_benchmark(self):
experiment_idx_past = self.benchmark_index[0]
pose_idx_past = self.benchmark_index[1]
repetition_idx_past = self.benchmark_index[2]
experiment_past = self._experiments[experiment_idx_past]
poses_past = experiment_past.poses[0:]
repetition_past = experiment_past.repetitions
if repetition_idx_past == repetition_past:
if pose_idx_past == len(poses_past) - 1:
if experiment_idx_past == len(self._experiments) - 1:
return False
else:
experiment = self._experiments[experiment_idx_past + 1]
pose = experiment.poses[0:][0]
self.benchmark_index = [experiment_idx_past + 1, 0, 1]
else:
experiment = experiment_past
pose = poses_past[pose_idx_past + 1]
self.benchmark_index = [
experiment_idx_past, pose_idx_past + 1, 1
]
else:
experiment = experiment_past
pose = poses_past[pose_idx_past]
self.benchmark_index = [
experiment_idx_past, pose_idx_past, repetition_idx_past + 1
]
exp_settings = experiment.Conditions
exp_settings.set(QualityLevel='Low')
positions = self._client.load_settings(exp_settings).player_start_spots
start_index = pose[0]
end_index = pose[1]
self._client.start_episode(start_index)
self._time_out = self._experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
self._target = positions[end_index]
self._episode_name = str(experiment.Conditions.WeatherId) + '_' \
+ str(experiment.task) + '_' + str(start_index) \
+ '_' + str(end_index)
if ((self.num_episodes % self.video_every) == 0) and (self.id == 0):
video_path = os.path.join(
self.video_dir, '{:08d}_'.format(self.num_episodes) +
self._episode_name + '.mp4')
self.logger.info('Writing video at {}'.format(video_path))
self.video_writer = skvideo.io.FFmpegWriter(
video_path, inputdict={'-r': '30'}, outputdict={'-r': '30'})
else:
self.video_writer = None
self.num_episodes += 1
def _get_directions(self, current_point, end_point):
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
@staticmethod
def _is_collision(measurements):
c = 0
c += measurements.player_measurements.collision_vehicles
c += measurements.player_measurements.collision_pedestrians
c += measurements.player_measurements.collision_other
sidewalk_intersection = measurements.player_measurements.intersection_offroad
otherlane_intersection = measurements.player_measurements.intersection_otherlane
return (c > 1e-9) or (sidewalk_intersection >
0.01) or (otherlane_intersection > 0.9), c
def _make_carla_client(self, host, port):
while True:
try:
self.logger.info(
"Trying to make client on port {}".format(port))
self._client = CarlaClient(host, port, timeout=100)
self._client.connect()
self._client.load_settings(CarlaSettings(QualityLevel='Low'))
self._client.start_episode(0)
self.logger.info(
"Successfully made client on port {}".format(port))
break
except TCPConnectionError as error:
self.logger.debug('Got TCPConnectionError..sleeping for 1')
self.logger.error(error)
time.sleep(1)
class CarlaEnvironment(EnvironmentInterface):
def __init__(self,
experiment_path=None,
frame_skip=1,
server_height=512,
server_width=720,
camera_height=88,
camera_width=200,
experiment_suite=None,
quality="low",
cameras=[CameraTypes.FRONT],
weather_id=[1],
episode_max_time=30000,
max_speed=35.0,
port=2000,
map_name="Town01",
verbose=True,
seed=None,
is_rendered=True,
num_speedup_steps=30,
separate_actions_for_throttle_and_brake=False,
rendred_image_type='forward_camera'):
self.frame_skip = frame_skip # the frame skip affects the fps of the server directly. fps = 30 / frameskip
self.server_height = server_height
self.server_width = server_width
self.camera_height = camera_height
self.camera_width = camera_width
self.experiment_suite = experiment_suite # an optional CARLA experiment suite to use
self.quality = quality
self.cameras = cameras
self.weather_id = weather_id
self.episode_max_time = episode_max_time # miliseconds for each episode
self.max_speed = max_speed # km/h
self.port = port
self.host = 'localhost'
self.map_name = map_name
self.map_path = map_path_mapper[self.map_name]
self.experiment_path = experiment_path
self.current_episode_steps_counter = 1
# client configuration
self.verbose = verbose
self.episode_idx = 0
self.num_speedup_steps = num_speedup_steps
self.max_speed = max_speed
self.is_rendered = is_rendered
# setup server settings
self.experiment_suite = experiment_suite
self.separate_actions_for_throttle_and_brake = separate_actions_for_throttle_and_brake
self.rendred_image_type = rendred_image_type
self.left_poses = []
self.right_poses = []
self.follow_poses = []
self.Straight_poses = []
self.settings = CarlaSettings()
self.settings.set(SynchronousMode=True,
SendNonPlayerAgentsInfo=False,
NumberOfVehicles=15,
NumberOfPedestrians=30,
WeatherId=self.weather_id,
QualityLevel=self.quality,
SeedVehicles=seed,
SeedPedestrians=seed)
if seed is None:
self.settings.randomize_seeds()
self.settings = self.add_cameras(self.settings, self.cameras,
self.camera_width, self.camera_height)
# open the server
self.server = self.open_server()
logging.disable(40)
print("Successfully opened the server")
# open the client
self.game = CarlaClient(self.host, self.port, timeout=99999999)
print("Successfully opened the client")
self.game.connect()
print("Successfull Connection")
if self.experiment_suite:
self.current_experiment_idx = 0
self.current_experiment = self.experiment_suite.get_experiments()[
self.current_experiment_idx]
self.scene = self.game.load_settings(
self.current_experiment.conditions)
else:
self.scene = self.game.load_settings(self.settings)
# get available start positions
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
self.action_space = ActionSpace(
shape=2,
low=np.array([-1, -1]),
high=np.array([1, 1]),
descriptions=["steer", "gas_and_brake"])
# state space
#define all measurments
self.state_space = {
"measurements": {
"forward_speed": np.array([1]),
"x": np.array([1]),
"y": np.array([1]),
"z": np.array([1])
}
}
#define all cameras
for camera in self.scene.sensors:
self.state_space[camera.name] = {
"data": np.array([self.camera_height, self.camera_width, 3])
}
print("Define ", camera.name, " Camera")
# measurements
self.autopilot = None
self.planner = Planner(self.map_name)
#rendering
if self.is_rendered:
pygame.init()
pygame.font.init()
self.display = pygame.display.set_mode(
(self.camera_width, self.camera_height))
# env initialization
self.reset(True)
def reset(self, force_environment_reset=False):
"""
Reset the environment and all the variable of the wrapper
:param force_environment_reset: forces environment reset even when the game did not end
:return: A dictionary containing the observation, reward, done flag, action and measurements
"""
self.reset_ep = True
self.restart_environment_episode(force_environment_reset)
self.last_episode_time = time.time()
if self.current_episode_steps_counter > 0:
self.episode_idx += 1
self.done = False
self.total_reward_in_current_episode = self.reward = 0.0
self.last_action = 0
self.current_episode_steps_counter = 0
self.last_episode_images = []
self.step([0, 1, 0])
self.last_env_response = {
"reward": self.reward,
"next_state": self.state,
"goal": self.current_goal,
"game_over": self.done
}
return self.last_env_response
def add_cameras(self, settings, cameras, camera_width, camera_height):
# add a front facing camera
print("Available Cameras are ", cameras)
if CameraTypes.FRONT in cameras:
camera = Camera(CameraTypes.FRONT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 0, 0)
settings.add_sensor(camera)
# add a left facing camera
if CameraTypes.LEFT in cameras:
camera = Camera(CameraTypes.LEFT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, -30, 0)
settings.add_sensor(camera)
# add a right facing camera
if CameraTypes.RIGHT in cameras:
camera = Camera(CameraTypes.RIGHT.value)
camera.set(FOV=100)
camera.set_image_size(camera_width, camera_height)
camera.set_position(2.0, 0, 1.4)
camera.set_rotation(-15.0, 30, 0)
settings.add_sensor(camera)
# add a front facing depth camera
if CameraTypes.DEPTH in cameras:
camera = Camera(CameraTypes.DEPTH.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'Depth'
settings.add_sensor(camera)
# add a front facing semantic segmentation camera
if CameraTypes.SEGMENTATION in cameras:
camera = Camera(CameraTypes.SEGMENTATION.value)
camera.set_image_size(camera_width, camera_height)
camera.set_position(0.2, 0, 1.3)
camera.set_rotation(8, 30, 0)
camera.PostProcessing = 'SemanticSegmentation'
settings.add_sensor(camera)
print("Successfully adding a SemanticSegmentation camera")
return settings
def get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self.planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def open_server(self):
log_path = path.join(
self.experiment_path if self.experiment_path is not None else '.',
'logs', "CARLA_LOG_{}.txt".format(self.port))
if not os.path.exists(os.path.dirname(log_path)):
os.makedirs(os.path.dirname(log_path))
with open(log_path, "wb") as out:
cmd = [
path.join(environ.get('CARLA_ROOT'),
'CarlaUE4.sh'), self.map_path, "-benchmark",
"-carla-server", "-fps={}".format(30 / self.frame_skip),
"-world-port={}".format(self.port),
"-windowed -ResX={} -ResY={}".format(self.server_width,
self.server_height),
"-carla-no-hud"
]
print("CMD is : ", cmd)
# if self.config:
# cmd.append("-carla-settings={}".format(self.config))
p = subprocess.Popen(cmd, stdout=out, stderr=out)
return p
def close_server(self):
os.killpg(os.getpgid(self.server.pid), signal.SIGKILL)
def step(self, action):
# get measurements and observations
measurements = []
while type(measurements) == list:
measurements, sensor_data = self.game.read_data()
self.state = {}
for camera in self.scene.sensors:
if camera.name == 'segmentation':
#labels_to_road_noroad taker Sensor.Image not numpy array
self.state[camera.name] = labels_to_road_noroad(
sensor_data[camera.name])
else:
self.state[camera.name] = sensor_data[camera.name].data
#self.state[camera.name] = sensor_data[camera.name].data
self.location = [
measurements.player_measurements.transform.location.x,
measurements.player_measurements.transform.location.y,
measurements.player_measurements.transform.location.z
]
self.distance_from_goal = np.linalg.norm(
np.array(self.location[:2]) -
[self.current_goal.location.x, self.current_goal.location.y])
is_collision = measurements.player_measurements.collision_vehicles != 0 \
or measurements.player_measurements.collision_pedestrians != 0 \
or measurements.player_measurements.collision_other != 0
speed_reward = measurements.player_measurements.forward_speed - 1
if speed_reward > 30.:
speed_reward = 30.
self.reward = speed_reward \
- (measurements.player_measurements.intersection_otherlane * 5) \
- (measurements.player_measurements.intersection_offroad * 5) \
- is_collision * 100 \
- np.abs(self.control.steer) * 10
# update measurements
#self.measurements = [measurements.player_measurements.forward_speed] + self.location
#TODO, Add control signals to measurements
control_signals = [
np.clip(action[0], -1, 1),
np.clip(action[1], 0, 1),
np.clip(action[2], 0, 1)
] #steer, throttle, brake
self.measurements = [measurements.player_measurements.forward_speed
] + self.location + control_signals
self.autopilot = measurements.player_measurements.autopilot_control
# The directions to reach the goal (0 Follow lane, 1 Left, 2 Right, 3 Straight)
directions = int(
self.get_directions(measurements.player_measurements.transform,
self.current_goal) - 2)
# if directions == 0:
# if self.reset_ep:
# self.follow_poses.append((self.current_start,self.current_goal))
# self.reset_ep = False
# elif directions == 1:
# self.left_poses.append((self.current_start,self.current_goal))
# elif directions == 2:
# self.right_poses.append((self.current_start,self.current_goal))
# elif directions == 1:
# if self.reset_ep:
# self.left_poses.append((self.current_start,self.current_goal))
# self.reset_ep = False
# elif directions == 2:
# if self.reset_ep:
# self.right_poses.append((self.current_start,self.current_goal))
# self.reset_ep = False
# elif directions == 3:
# if self.reset_ep:
# self.Straight_poses.append((self.current_start,self.current_goal))
# self.reset_ep = False
# else:
# self.reset_ep = False
self.state['high_level_command'] = directions
if (measurements.game_timestamp >=
self.episode_max_time) or is_collision:
self.done = True
self.state['measurements'] = np.array(self.measurements)
#prepare rendered image and update display
if self.is_rendered:
#check if user wants to close rendering, else continue rendering
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
self.is_rendered = False
self.surface = pygame.surfarray.make_surface(
self.state[self.rendred_image_type].swapaxes(0, 1))
self.display.blit(self.surface, (0, 0))
pygame.display.flip()
self.take_action(action)
def take_action(self, action):
self.control = VehicleControl()
# transform the 2 value action (steer, throttle - brake) into a 3 value action (steer, throttle, brake)
self.control.steer = np.clip(action[0], -1, 1)
self.control.throttle = np.clip(action[1], 0, 1)
self.control.brake = np.abs(np.clip(action[2], 0, 1))
# prevent braking
# if not self.allow_braking or self.control.brake < 0.1 or self.control.throttle > self.control.brake:
# self.control.brake = 0
# prevent over speeding
if hasattr(self, 'measurements') and self.measurements[
0] * 3.6 > self.max_speed and self.control.brake == 0:
self.control.throttle = 0.0
self.control.hand_brake = False
self.control.reverse = False
self.game.send_control(self.control)
def load_experiment(self, experiment_idx):
print("Loading the experiment")
self.current_experiment = self.experiment_suite.get_experiments(
)[experiment_idx]
self.scene = self.game.load_settings(
self.current_experiment.conditions)
self.positions = self.scene.player_start_spots
self.num_positions = len(self.positions)
self.current_start_position_idx = 0
self.current_pose = 0
def restart_environment_episode(self, force_environment_reset=False):
# select start and end positions
print("restarting new Episode")
if self.experiment_suite:
# if an expeirent suite is available, follow its given poses
if self.current_pose >= len(self.current_experiment.poses):
# load a new experiment
self.current_experiment_idx = (
self.current_experiment_idx + 1) % len(
self.experiment_suite.get_experiments())
self.load_experiment(self.current_experiment_idx)
self.current_start_position_idx = self.current_experiment.poses[
self.current_pose][0]
self.current_start = self.positions[self.current_experiment.poses[
self.current_pose][0]]
self.current_goal = self.positions[self.current_experiment.poses[
self.current_pose][1]]
self.current_pose += 1
else:
# go over all the possible positions in a cyclic manner
self.current_start_position_idx = (
self.current_start_position_idx + 1) % self.num_positions
self.current_start = self.positions[
self.current_start_position_idx]
# choose a random goal destination
self.current_goal = random.choice(self.positions)
try:
self.game.start_episode(self.current_start_position_idx)
except:
self.game.connect()
self.game.start_episode(self.current_start_position_idx)
# start the game with some initial speed
for i in range(self.num_speedup_steps):
self.control = VehicleControl(throttle=1.0,
brake=0,
steer=0,
hand_brake=False,
reverse=False)
self.game.send_control(VehicleControl())
def get_rendered_image(self) -> np.ndarray:
"""
Return a numpy array containing the image that will be rendered to the screen.
This can be different from the observation. For example, mujoco's observation is a measurements vector.
:return: numpy array containing the image that will be rendered to the screen
"""
image = [self.state[camera.name] for camera in self.scene.sensors]
image = np.vstack(image)
return image
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(
self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0
):
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images
)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments()[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index], positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index))
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result)
# Write the details of this episode.
self._recording.write_measurements_results(experiment, rep, pose, reward_vec,
control_vec)
if result > 0:
logging.info('+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x,
current_point.location.y, 0.22),
(current_point.orientation.x,
current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y, end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[
start_point.location.x, start_point.location.y, 0.22], [
start_point.orientation.x, start_point.orientation.y, 0.22], [
end_point.location.x, end_point.location.y, end_point.location.z], [
end_point.orientation.x, end_point.orientation.y, end_point.orientation.z])
def _run_navigation_episode(
self,
agent,
client,
time_out,
target,
episode_name):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
"""
# Send an initial command.
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
success = False
while (current_timestamp - initial_timestamp) < (time_out * 1000) and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(measurements.player_measurements.transform, target)
# Agent process the data.
control = agent.run_step(measurements, sensor_data, directions, target)
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ',
control.steer, control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
logging.info('Status:')
logging.info(
'[d=%f] c_x = %f, c_y = %f ---> t_x = %f, t_y = %f',
float(distance), current_x, current_y, target.location.x,
target.location.y)
# Check if reach the target
if distance < self._distance_for_success:
success = True
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp) / 1000.0, distance
return 0, measurement_vec, control_vec, time_out, distance
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0):
"""
Args
city_name:
name_to_save:
continue_experiment:
save_images:
distance_for_success:
collisions_as_failure: if this flag is set to true, episodes will terminate as failure, when the car collides.
"""
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
# TO keep track of the previous collisions
self._previous_pedestrian_collision = 0
self._previous_vehicle_collision = 0
self._previous_other_collision = 0
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.sldi
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments(
)[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
t = len(positions)
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index],
positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance, col_ped, col_veh, col_oth,metList) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index), experiment_suite.metrics_parameters,
experiment_suite.collision_as_failure)
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result,
col_ped, col_veh, col_oth)
# Write the details of this episode.
self._recording.write_measurements_results(
experiment, rep, pose, reward_vec, control_vec,
metList)
if result > 0:
logging.info(
'+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x, current_point.location.y, 0.22),
(current_point.orientation.x, current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 0.22],
[start_point.orientation.x, start_point.orientation.y, 0.22],
[end_point.location.x, end_point.location.y, end_point.location.z],
[
end_point.orientation.x, end_point.orientation.y,
end_point.orientation.z
])
def _has_agent_collided(self, measurement, metrics_parameters):
"""
This function must have a certain state and only look to one measurement.
"""
collided_veh = 0
collided_ped = 0
collided_oth = 0
if (measurement.collision_vehicles - self._previous_vehicle_collision) \
> metrics_parameters['collision_vehicles']['threshold']/2.0:
collided_veh = 1
if (measurement.collision_pedestrians - self._previous_pedestrian_collision) \
> metrics_parameters['collision_pedestrians']['threshold']/2.0:
collided_ped = 1
if (measurement.collision_other - self._previous_other_collision) \
> metrics_parameters['collision_other']['threshold']/2.0:
collided_oth = 1
self._previous_pedestrian_collision = measurement.collision_pedestrians
self._previous_vehicle_collision = measurement.collision_other
return collided_ped, collided_veh, collided_oth
def _is_agent_stuck(self, measurements, stuck_vec, old_coll):
# break the episode when the agent is stuck on a static object
coll_other = measurements.collision_other
coll_other -= old_coll
otherlane = measurements.intersection_otherlane > 0.4
offroad = measurements.intersection_offroad > 0.3
logging.info(
"offroad: {}, otherlane: {}, coll_other: {}, old_coll: {}".format(
offroad, otherlane, coll_other, old_coll))
# if still driving or got unstuck (v > 4km/h)
if measurements.forward_speed * 3.6 > 4:
cycle_signal(stuck_vec, 0)
if coll_other:
old_coll += coll_other
elif offroad or otherlane or coll_other:
cycle_signal(stuck_vec, 1)
else:
cycle_signal(stuck_vec, 0)
return all(stuck_vec), stuck_vec, old_coll
def _run_navigation_episode(self, agent, client, time_out, target,
episode_name, metrics_parameters,
collision_as_failure):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
metrics_object: The metrics object to check for collisions
"""
# Send an initial command.
time.sleep(2)
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
### Reset CAL agent
agent.reset_state()
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
col_ped, col_veh, col_oth = 0, 0, 0
fail = False
success = False
### own metrics
stuck_vec = [0] * 60 # measure for 60 frames (6 seconds)
center_distance_vec = []
old_collision_value = 0
direction_vec = []
#edited ash
metList = []
while not fail and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(
measurements.player_measurements.transform, target)
# Agent process the data.
curr_time = time.time()
control, prediction = agent.run_step(measurements, sensor_data,
directions, target)
control.steer = measurements.player_measurements.autopilot_control.steer
curr_time = time.time() - curr_time
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ', control.steer,
control.throttle, control.brake)
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
# Write status of the run on verbose mode
logging.info('Distance to target: %f', float(distance))
# Check if reach the target
col_ped, col_veh, col_oth = self._has_agent_collided(
measurements.player_measurements, metrics_parameters)
### CHANGE TO ORIGINAL CODE #####################
is_stuck, stuck_vec, old_collision_value = self._is_agent_stuck(
measurements.player_measurements, stuck_vec,
old_collision_value)
if distance < self._distance_for_success:
success = True
elif (current_timestamp - initial_timestamp) > (time_out * 1000):
fail = True
elif is_stuck:
fail = True
elif collision_as_failure and (col_ped or col_veh or col_oth):
fail = True
time_remain = (time_out * 1000 -
(current_timestamp - initial_timestamp)) / 1000
logging.info('Time remaining: %i m %i s', time_remain / 60,
time_remain % 60)
logging.info('')
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
temp = agent.getMetData()
temp['game_timestamp'] = measurements.game_timestamp
metList.append(temp)
print("stp number: ", frame, " ", temp, " elapsed time: ",
curr_time)
#my code...
player = {}
peds = []
px = measurements.player_measurements.transform.location.x
py = measurements.player_measurements.transform.location.y
player['pos_x'] = px
player['pos_y'] = py
yaw = measurements.player_measurements.transform.rotation.yaw
yaw = math.radians(yaw)
sfile = open(
'/home/self-driving/Desktop/CARLA_0.8.2/Indranil/CAL-master_copy_new/python_client/_benchmarks_results/sigData.csv',
'a+')
pfile = open(
'/home/self-driving/Desktop/CARLA_0.8.2/Indranil/CAL-master_copy_new/python_client/_benchmarks_results/pData.csv',
'a+')
hazard = False
sig = False
sigState = ''
speed_post = False
speed_post_state = ''
veh_inside_box = False
veh_dist = 50.0
'''
prediction keys:
center_distance
hazard_stop
red_light
relative_angle
speed_sign
veh_distance
'''
front_axel_x = 2.33
for a in measurements.non_player_agents:
if a.HasField('traffic_light'):
llx, lly = a.traffic_light.transform.location.x, a.traffic_light.transform.location.y
nx, ny = self.getNewCord((px, py), (llx, lly), yaw)
if self.inside_a2(nx, ny):
print("\n--traffic light found---!!")
print("State :", a.traffic_light.state)
sig = True
sigState = str(a.traffic_light.state)
if a.HasField('speed_limit_sign'):
llx, lly = a.speed_limit_sign.transform.location.x, a.speed_limit_sign.transform.location.y
nx, ny = self.getNewCord((px, py), (llx, lly), yaw)
if self.inside_a2(nx, ny):
print("\n--Speed Sign found---!!")
print("Limit :", a.speed_limit_sign.speed_limit * 3.6)
speed_post = True
speed_post_state = str(
math.floor(a.speed_limit_sign.speed_limit * 3.6))
if a.HasField('pedestrian'):
llx, lly = a.pedestrian.transform.location.x, a.pedestrian.transform.location.y
ped = {}
ped['pos_x'] = llx
ped['pos_y'] = lly
peds.append(ped)
nx, ny = self.getNewCord((px, py), (llx, lly), yaw)
if self.inside_a1(nx, ny):
print("\n--Pedestrian Hazard---!!")
print("Pedestrian in front ")
hazard = True
if a.HasField('vehicle'):
llx, lly = a.vehicle.transform.location.x, a.vehicle.transform.location.y
nx, ny = self.getNewCord((px, py), (llx, lly), yaw)
hlen = a.vehicle.bounding_box.extent.x
if self.inside_a3(nx, ny):
distan = self.getdis(0 + front_axel_x, 0, nx,
ny - hlen)
veh_inside_box = True
veh_dist = min(distan, veh_dist)
print("Vehicle in front distance,", distan,
" Predicted ", prediction['veh_distance'])
player['peds'] = peds
#write to file if hazard stop is true in current frame...
if hazard is True:
sfile.write(
str('{:0>6d}'.format(frame)) + ",Hazard," + str(hazard) +
"," + str(prediction['hazard_stop'][0]) + "," +
str(prediction['hazard_stop'][1]) + "\n")
else:
sfile.write(
str('{:0>6d}'.format(frame)) + ",Hazard," + str(False) +
"," + str(prediction['hazard_stop'][0]) + "," +
str(prediction['hazard_stop'][1]) + "\n")
if sig is True:
sfile.write(
str('{:0>6d}'.format(frame)) + "," + "Traffic," +
sigState + "," + str(prediction['red_light'][0]) + "," +
str(prediction['red_light'][1]) + "\n")
print("Traffic light.. Actual:", a.traffic_light.state,
" Predicted", prediction['red_light'][0])
else:
sfile.write(
str('{:0>6d}'.format(frame)) + "," + "Traffic," + '0' +
"," + str(prediction['red_light'][0]) + "," +
str(prediction['red_light'][1]) + "\n")
if speed_post is True:
sfile.write(
str('{:0>6d}'.format(frame)) + ",SpeedSign," +
speed_post_state + "," + str(prediction['speed_sign'][0]) +
"," + str(prediction['speed_sign'][1]) + "\n")
else:
sfile.write(
str('{:0>6d}'.format(frame)) + ",SpeedSign," + '-1' + "," +
str(prediction['speed_sign'][0]) + "," +
str(prediction['speed_sign'][1]) + "\n")
if veh_inside_box is True:
sfile.write(
str('{:0>6d}'.format(frame)) + ",Vehicle," +
str(veh_dist) + "," + str(prediction['veh_distance']) +
"\n")
else:
sfile.write(
str('{:0>6d}'.format(frame)) + ",Vehicle," + str(50.0) +
"," + str(prediction['veh_distance']) + "\n")
sfile.write(
str('{:0>6d}'.format(frame)) + ",CenterDist," +
str(temp['centerDist']) + "," +
str(prediction['center_distance']) + "\n")
sfile.write(
str('{:0>6d}'.format(frame)) + ",Angel," + str(yaw) + "," +
str(prediction['relative_angle']) + "\n")
jout = json.dumps(player)
pfile.write(jout + "\n")
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp
) / 1000.0, distance, col_ped, col_veh, col_oth, metList
return 0, measurement_vec, control_vec, time_out, distance, col_ped, col_veh, col_oth, metList
def getNewCord(self, origin, point, psi):
a = np.array([[math.cos(psi), -math.sin(psi)],
[math.sin(psi), math.cos(psi)]])
b = np.array([(point[0] - origin[0]), (point[1] - origin[1])])
x = np.linalg.solve(a, b)
return x
def getdis(self, x1, y1, x2, y2):
return math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
def inside_a2(self, x, y):
front_axel_x = 0
if (x >= (7.4 + front_axel_x) and x <=
(14.0 + front_axel_x)) and (y >= 0.8 and y <= 5.8):
return True
return False
def inside_a1(self, x, y):
front_axel_x = 2.33
if (x >= (0.0 + front_axel_x) and x <=
(8.2 + front_axel_x)) and (y >= -2.0 and y <= 2.0):
return True
return False
def inside_a3(self, x, y):
front_axel_x = 2.33
if (x >= (0 + front_axel_x) and x <=
(50 + front_axel_x)) and (y >= -1.6 and y <= 1.6):
return True
return False
class StraightDriveEnv(CarlaEnv):
def __init__(self,
client,
frame_skip=1,
cam_width=800,
cam_height=600,
town_string='Town01'):
super(StraightDriveEnv, self).__init__()
self.frame_skip = frame_skip
self.client = client
self._planner = Planner(town_string)
camera0 = Camera('CameraRGB')
camera0.set(CameraFOV=100)
camera0.set_image_size(cam_height, cam_width)
camera0.set_position(200, 0, 140)
camera0.set_rotation(-15.0, 0, 0)
self.start_goal_pairs = [[36, 40], [39, 35], [110, 114], [7,
3], [0, 4],
[68, 50], [61, 59], [47, 64], [147, 90],
[33, 87], [26, 19], [80, 76], [45, 49],
[55, 44], [29, 107], [95, 104], [84, 34],
[53, 67], [22, 17], [91, 148], [20, 107],
[78, 70], [95, 102], [68, 44], [45, 69]]
vehicles = 0
pedestrians = 0
weather = 1
settings = CarlaSettings()
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=vehicles,
NumberOfPedestrians=pedestrians,
WeatherId=weather,
)
settings.randomize_seeds()
settings.add_sensor(camera0)
self.scene = self.client.load_settings(settings)
img_shape = (cam_width, cam_height, 3)
self.observation_space = spaces.Tuple(
(spaces.Box(-np.inf, np.inf, (3, )), spaces.Box(0, 255,
img_shape)))
self.action_space = spaces.Box(-np.inf, np.inf, shape=(3, ))
self.prev_state = np.array([0., 0., 0.])
self.prev_collisions = np.array([0., 0., 0.])
self.prev_intersections = np.array([0., 0.])
def step(self, action):
steer = np.clip(action[0], -1, 1)
throttle = np.clip(action[1], 0, 1)
brake = np.clip(action[2], 0, 1)
for i in range(self.frame_skip):
self.t += 1
self.client.send_control(steer=steer,
throttle=throttle,
brake=brake,
hand_brake=False,
reverse=False)
measurements, sensor_data = self.client.read_data()
sensor_data = self._process_image(sensor_data['CameraRGB'])
current_time = measurements.game_timestamp
state, collisions, intersections, onehot = self._process_measurements(
measurements)
reward, dist_goal = self._calculate_reward(state, collisions,
intersections)
done = self._calculate_done(collisions, state, current_time)
self.prev_state = np.array(state)
self.prev_collisions = np.array(collisions)
self.prev_intersections = np.array(intersections)
measurement_obs = self._generate_obs(state, collisions, onehot,
dist_goal)
return (measurement_obs, sensor_data), reward, done, {}
def reset(self):
self._generate_start_goal_pair()
print('Starting new episode...')
# Blocking function until episode is ready
self.client.start_episode(self.start_idx)
measurements, sensor_data = self.client.read_data()
sensor_data = self._process_image(sensor_data['CameraRGB'])
state, collisions, intersections, onehot = self._process_measurements(
measurements)
self.prev_state = np.array(state)
self.prev_collisions = np.array(collisions)
self.prev_intersections = np.array(intersections)
self.start_time = measurements.game_timestamp
self.t = 0
pos = np.array(state[0:2])
dist_goal = np.linalg.norm(pos - self.goal)
measurement_obs = self._generate_obs(state, collisions, onehot,
dist_goal)
return (measurement_obs, sensor_data)
def _calculate_done(self, collisions, state, current_time):
pos = np.array(state[0:2])
dist_goal = np.linalg.norm(pos - self.goal)
return self._is_timed_out(current_time) or self._is_goal(dist_goal)
# Not described in paper, but should be there for safe driving
return self._is_timed_out() and self._collision_on_step(dist_goal)
def _calculate_planner_onehot(self, measurements):
# return np.array([0, 0, 0, 0, 1]) # TODO need to debug
print(type(self.end_point.location), type(self.end_point.orientation))
val = self._planner.get_next_command(measurements.location,
measurements.orientation,
self.end_point.location,
self.end_point.orientation)
if val == 0.0:
return np.array([1, 0, 0, 0, 0])
onehot = np.zeros(5)
val = int(val) - 1
onehot[val] = 1
return onehot
def _calculate_reward(self, state, collisions, intersections):
pos = np.array(state[0:2])
dist_goal = np.linalg.norm(pos - self.goal)
dist_goal_prev = np.linalg.norm(self.prev_state[0:2] - self.goal)
speed = state[2]
# TODO: Check this?
r = 1000 * (dist_goal_prev - dist_goal) / 10 + 0.05 * (speed - self.prev_state[2]) \
- 2 * (sum(intersections) - sum(self.prev_intersections))
return r, dist_goal
def _calculate_timeout(self):
self.timeout_t = (
(self.timeout_dist / 100000.0) / 10.0) * 3600.0 + 10.0
def _collision_on_step(self, collisions):
return sum(collisions) > 0
def _generate_obs(self, state, collisions, onehot, dist_goal):
speed = state[2]
collisions = np.sum(collisions)
return np.concatenate((np.array([speed, dist_goal,
collisions]), np.array(onehot)))
def _generate_start_goal_pair(self):
# Choose one player start at random.
self.position_index = np.random.randint(0,
len(self.start_goal_pairs) - 1)
self.start_idx = self.start_goal_pairs[self.position_index][0]
self.goal_idx = self.start_goal_pairs[self.position_index][1]
start_point = self.scene.player_start_spots[self.start_idx]
end_point = self.scene.player_start_spots[self.goal_idx]
self.end_point = end_point
self.goal = [end_point.location.x / 100,
end_point.location.y / 100] # cm -> m
self.timeout_dist = self._planner.get_shortest_path_distance(
[start_point.location.x, start_point.location.y, 22],
[start_point.orientation.x, start_point.orientation.y, 22],
[end_point.location.x, end_point.location.y, 22],
[end_point.orientation.x, end_point.orientation.y, 22])
self._calculate_timeout()
def _is_goal(self, distance):
return distance < 2.0
def _is_timed_out(self, current_time):
return (current_time - self.start_time) > (self.timeout_t * 1000)
def _process_image(self, carla_raw_img):
return resize(to_rgb_array(carla_raw_img), (84, 84))
def _process_measurements(self, measurements):
player_measurements = measurements.player_measurements
pos_x = player_measurements.transform.location.x / 100 # cm -> m
pos_y = player_measurements.transform.location.y / 100
speed = player_measurements.forward_speed
col_cars = player_measurements.collision_vehicles
col_ped = player_measurements.collision_pedestrians
col_other = player_measurements.collision_other
other_lane = player_measurements.intersection_otherlane
offroad = player_measurements.intersection_offroad
onehot = self._calculate_planner_onehot(player_measurements.transform)
return np.array([pos_x, pos_y,
speed]), np.array([col_cars, col_ped, col_other
]), np.array([other_lane,
offroad]), onehot
class CarlaHuman(Driver):
def __init__(self, driver_conf):
Driver.__init__(self)
# some behaviors
self._autopilot = driver_conf.autopilot
self._reset_period = driver_conf.reset_period # those reset period are in the actual system time, not in simulation time
self._goal_reaching_threshold = 3
self.use_planner = driver_conf.use_planner
# we need the planner to find a valid episode, so we initialize one no matter what
self._world = None
self._vehicle = None
self._agent_autopilot = None
self._camera_center = None
self._spectator = None
# (last) images store for several cameras
self._data_buffers = dict()
self.update_once = False
self._collision_events = []
self.collision_sensor = None
if __CARLA_VERSION__ == '0.8.X':
self.planner = Planner(driver_conf.city_name)
else:
self.planner = None
self.use_planner = False
# resources
self._host = driver_conf.host
self._port = driver_conf.port
# various config files
self._driver_conf = driver_conf
self._config_path = driver_conf.carla_config
# some initializations
self._straight_button = False
self._left_button = False
self._right_button = False
self._rear = False
self._recording = False
self._skiped_frames = 20
self._stucked_counter = 0
self._prev_time = datetime.now()
self._episode_t0 = datetime.now()
self._vehicle_prev_location = namedtuple("vehicle", "x y z")
self._vehicle_prev_location.x = 0.0
self._vehicle_prev_location.y = 0.0
self._vehicle_prev_location.z = 0.0
self._camera_left = None
self._camera_right = None
self._camera_center = None
self._actor_list = []
self._sensor_list = []
self._weather_list = [
'ClearNoon', 'CloudyNoon', 'WetNoon', 'WetCloudyNoon',
'MidRainyNoon', 'HardRainNoon', 'SoftRainNoon', 'ClearSunset',
'CloudySunset', 'WetSunset', 'WetCloudySunset', 'MidRainSunset',
'HardRainSunset', 'SoftRainSunset'
]
self._current_weather = 4
self._current_command = 2.0
# steering wheel
self._steering_wheel_flag = True
if self._steering_wheel_flag:
self._is_on_reverse = False
self._control = VehicleControl()
self._parser = SafeConfigParser()
self._parser.read('wheel_config.ini')
self._steer_idx = int(
self._parser.get('G29 Racing Wheel', 'steering_wheel'))
self._throttle_idx = int(
self._parser.get('G29 Racing Wheel', 'throttle'))
self._brake_idx = int(self._parser.get('G29 Racing Wheel',
'brake'))
self._reverse_idx = int(
self._parser.get('G29 Racing Wheel', 'reverse'))
self._handbrake_idx = int(
self._parser.get('G29 Racing Wheel', 'handbrake'))
self.last_timestamp = lambda x: x
self.last_timestamp.elapsed_seconds = 0.0
self.last_timestamp.delta_seconds = 0.2
self.initialize_map(driver_conf.city_name)
def start(self):
if __CARLA_VERSION__ == '0.8.X':
self.carla = CarlaClient(self._host, int(self._port), timeout=120)
self.carla.connect()
else:
self.carla = CarlaClient(self._host, int(self._port))
self.carla.set_timeout(5000)
wd = self.carla.get_world()
self.wd = wd
settings = wd.get_settings()
settings.synchronous_mode = True
wd.apply_settings(settings)
self._reset()
if not self._autopilot:
pygame.joystick.init()
joystick_count = pygame.joystick.get_count()
if joystick_count > 1:
print("Please Connect Just One Joystick")
raise ValueError()
self.joystick = pygame.joystick.Joystick(0)
self.joystick.init()
def test_alive(self):
if not hasattr(self, "wd"):
return False
wd = self.wd
wd.tick()
try:
wd.wait_for_tick(5.0)
except:
return False
return True
def __del__(self):
if hasattr(self, 'carla'):
print("destructing the connection")
if __CARLA_VERSION__ == '0.8.X':
self.carla.disconnect()
else:
alive = self.test_alive()
# destroy old actors
print('destroying actors')
if alive:
if len(self._actor_list) > 0:
for actor in self._actor_list:
actor.destroy()
self._actor_list = []
print('done.')
if self._vehicle is not None:
if alive:
self._vehicle.destroy()
self._vehicle = None
if self._camera_center is not None:
if alive:
self._camera_center.destroy()
self._camera_center = None
if self._camera_left is not None:
if alive:
self._camera_left.destroy()
self._camera_left = None
if self._camera_right is not None:
if alive:
self._camera_right.destroy()
self._camera_right = None
if self.collision_sensor is not None:
if alive:
self.collision_sensor.sensor.destroy()
self.collision_sensor = None
# pygame.quit()
# if self._camera is not None:
# self._camera.destroy()
# self._camera = None
# if self._vehicle is not None:
# self._vehicle.destroy()
# self._vehicle = None
def try_spawn_random_vehicle_at(self,
blueprints,
transform,
auto_drive=True):
blueprint = random.choice(blueprints)
if blueprint.has_attribute('color'):
color = random.choice(
blueprint.get_attribute('color').recommended_values)
blueprint.set_attribute('color', color)
vehicle = self._world.try_spawn_actor(blueprint, transform)
if vehicle is not None:
self._actor_list.append(vehicle)
if auto_drive:
# TODO: this won't work in 0.9.5 with Exp_Town
# however, we don't have traffic in that town right now, so we just ignore this
vehicle.set_autopilot()
#print('spawned %r at %s' % (vehicle.type_id, transform.location))
return True
return False
def get_parking_locations(self,
filename,
z_default=0.0,
random_perturb=False):
with open(filename, "r") as f:
lines = f.readlines()
ans = []
for line in lines:
x, y, yaw = [float(v.strip()) for v in line.split(",")]
if random_perturb:
x += np.random.normal(
0, scale=self._driver_conf.extra_explore_location_std)
y += np.random.normal(
0, scale=self._driver_conf.extra_explore_location_std)
yaw += np.random.normal(
0, scale=self._driver_conf.extra_explore_yaw_std)
ans.append(
carla.Transform(location=carla.Location(x=x,
y=y,
z=z_default),
rotation=carla.Rotation(roll=0,
pitch=0,
yaw=yaw)))
return ans
def print_transform(self, t):
print(t.location.x, t.location.y, t.location.z)
print(t.rotation.roll, t.rotation.pitch, t.rotation.yaw)
def _reset(self):
self._episode_t0 = datetime.now()
if __CARLA_VERSION__ == '0.8.X':
# create the carla config based on template and the params passed in
config = ConfigParser()
config.optionxform = str
config.read(self._config_path)
config.set('CARLA/LevelSettings', 'NumberOfVehicles',
self._driver_conf.cars)
config.set('CARLA/LevelSettings', 'NumberOfPedestrians',
self._driver_conf.pedestrians)
config.set('CARLA/LevelSettings', 'WeatherId',
self._driver_conf.weather)
output = io.StringIO()
config.write(output)
scene_descriptions = self.carla.load_settings(output.getvalue())
# based on the scene descriptions, find the start and end positions
self.positions = scene_descriptions.player_start_spots
# the episode_config saves [start_index, end_index]
self.episode_config = find_valid_episode_position(
self.positions, self.planner)
self.carla.start_episode(self.episode_config[0])
print('RESET ON POSITION ', self.episode_config[0],
", the target location is: ", self.episode_config[1])
else:
# destroy old actors
print('destroying actors')
for actor in self._actor_list:
actor.destroy()
self._actor_list = []
print('done.')
# TODO: spawn pedestrains
# TODO: spawn more vehicles
if self._autopilot:
self._current_weather = self._weather_list[
int(self._driver_conf.weather) - 1]
else:
self._current_weather = random.choice(self._weather_list)
if not self._autopilot:
# select one of the random starting points previously selected
start_positions = np.loadtxt(self._driver_conf.positions_file,
delimiter=',')
if len(start_positions.shape) == 1:
start_positions = start_positions.reshape(
1, len(start_positions))
# TODO: Assign random position from file
WINDOW_WIDTH = 768
WINDOW_HEIGHT = 576
CAMERA_FOV = 103.0
CAMERA_CENTER_T = carla.Location(x=0.7, y=-0.0, z=1.60)
CAMERA_LEFT_T = carla.Location(x=0.7, y=-0.4, z=1.60)
CAMERA_RIGHT_T = carla.Location(x=0.7, y=0.4, z=1.60)
CAMERA_CENTER_ROTATION = carla.Rotation(roll=0.0,
pitch=0.0,
yaw=0.0)
CAMERA_LEFT_ROTATION = carla.Rotation(roll=0.0,
pitch=0.0,
yaw=-45.0)
CAMERA_RIGHT_ROTATION = carla.Rotation(roll=0.0,
pitch=0.0,
yaw=45.0)
CAMERA_CENTER_TRANSFORM = carla.Transform(
location=CAMERA_CENTER_T, rotation=CAMERA_CENTER_ROTATION)
CAMERA_LEFT_TRANSFORM = carla.Transform(
location=CAMERA_LEFT_T, rotation=CAMERA_LEFT_ROTATION)
CAMERA_RIGHT_TRANSFORM = carla.Transform(
location=CAMERA_RIGHT_T, rotation=CAMERA_RIGHT_ROTATION)
self._world = self.carla.get_world()
settings = self._world.get_settings()
settings.synchronous_mode = True
self._world.apply_settings(settings)
# add traffic
blueprints_vehi = self._world.get_blueprint_library().filter(
'vehicle.*')
blueprints_vehi = [
x for x in blueprints_vehi
if int(x.get_attribute('number_of_wheels')) == 4
]
blueprints_vehi = [
x for x in blueprints_vehi if not x.id.endswith('isetta')
]
# @todo Needs to be converted to list to be shuffled.
spawn_points = list(self._world.get_map().get_spawn_points())
if len(spawn_points) == 0:
if self.city_name_demo == "Exp_Town":
spawn_points = [
carla.Transform(
location=carla.Location(x=-11.5, y=-8.0, z=2.0))
]
random.shuffle(spawn_points)
print('found %d spawn points.' % len(spawn_points))
# TODO: debug change 50 to 0
count = 0
if count > 0:
for spawn_point in spawn_points:
if self.try_spawn_random_vehicle_at(
blueprints_vehi, spawn_point):
count -= 1
if count <= 0:
break
while count > 0:
time.sleep(0.5)
if self.try_spawn_random_vehicle_at(
blueprints_vehi, random.choice(spawn_points)):
count -= 1
# end traffic addition!
# begin parking addition
if hasattr(
self._driver_conf, "parking_position_file"
) and self._driver_conf.parking_position_file is not None:
parking_points = self.get_parking_locations(
self._driver_conf.parking_position_file)
random.shuffle(parking_points)
print('found %d parking points.' % len(parking_points))
count = 200
for spawn_point in parking_points:
self.try_spawn_random_vehicle_at(blueprints_vehi,
spawn_point, False)
count -= 1
if count <= 0:
break
# end of parking addition
blueprints = self._world.get_blueprint_library().filter('vehicle')
vechile_blueprint = [
e for i, e in enumerate(blueprints)
if e.id == 'vehicle.lincoln.mkz2017'
][0]
if self._vehicle == None or self._autopilot:
if self._autopilot and self._vehicle is not None:
self._vehicle.destroy()
self._vehicle = None
while self._vehicle == None:
if self._autopilot:
# from designated points
if hasattr(self._driver_conf,
"extra_explore_prob") and random.random(
) < self._driver_conf.extra_explore_prob:
extra_positions = self.get_parking_locations(
self._driver_conf.extra_explore_position_file,
z_default=3.0,
random_perturb=True)
print(
"spawning hero vehicle from the extra exploration"
)
START_POSITION = random.choice(extra_positions)
else:
START_POSITION = random.choice(spawn_points)
else:
random_position = start_positions[
np.random.randint(start_positions.shape[0]), :]
START_POSITION = carla.Transform(
carla.Location(x=random_position[0],
y=random_position[1],
z=random_position[2] + 1.0),
carla.Rotation(pitch=random_position[3],
roll=random_position[4],
yaw=random_position[5]))
self._vehicle = self._world.try_spawn_actor(
vechile_blueprint, START_POSITION)
else:
if self._autopilot:
# from designated points
START_POSITION = random.choice(spawn_points)
else:
random_position = start_positions[
np.random.randint(start_positions.shape[0]), :]
START_POSITION = carla.Transform(
carla.Location(x=random_position[0],
y=random_position[1],
z=random_position[2] + 1.0),
carla.Rotation(pitch=random_position[3],
roll=random_position[4],
yaw=random_position[5]))
self._vehicle.set_transform(START_POSITION)
print("after spawning the ego vehicle")
print("warm up process to make the vehicle ego location correct")
wd = self._world
for i in range(25):
wd.tick()
if not wd.wait_for_tick(10.0):
continue
print("warmup finished")
if self._autopilot:
# Nope: self._vehicle.set_autopilot()
print("before roaming agent")
self._agent_autopilot = RoamingAgent(self._vehicle)
print("after roaming agent")
if self.collision_sensor is not None:
print("before destroying the sensor")
self.collision_sensor.sensor.destroy()
print("after destroying the sensor")
else:
print("collision sensor is None")
self.collision_sensor = CollisionSensor(self._vehicle, self)
print("after spawning the collision sensor")
# set weather
weather = getattr(carla.WeatherParameters, self._current_weather)
self._vehicle.get_world().set_weather(weather)
self._spectator = self._world.get_spectator()
cam_blueprint = self._world.get_blueprint_library().find(
'sensor.camera.rgb')
cam_blueprint.set_attribute('image_size_x', str(WINDOW_WIDTH))
cam_blueprint.set_attribute('image_size_y', str(WINDOW_HEIGHT))
cam_blueprint.set_attribute('fov', str(CAMERA_FOV))
if self._camera_center is not None:
self._camera_center.destroy()
self._camera_left.destroy()
self._camera_right.destroy()
self._camera_center = None
if self._camera_center == None:
self._camera_center = self._world.spawn_actor(
cam_blueprint,
CAMERA_CENTER_TRANSFORM,
attach_to=self._vehicle)
self._camera_left = self._world.spawn_actor(
cam_blueprint,
CAMERA_LEFT_TRANSFORM,
attach_to=self._vehicle)
self._camera_right = self._world.spawn_actor(
cam_blueprint,
CAMERA_RIGHT_TRANSFORM,
attach_to=self._vehicle)
self._camera_center.listen(CallBack('CameraMiddle', self))
self._camera_left.listen(CallBack('CameraLeft', self))
self._camera_right.listen(CallBack('CameraRight', self))
# spectator server camera
self._spectator = self._world.get_spectator()
self._skiped_frames = 0
self._stucked_counter = 0
self._start_time = time.time()
def get_recording(self):
if self._autopilot:
# debug: 0 for debugging
if self._skiped_frames >= 20:
return True
else:
self._skiped_frames += 1
return False
else:
'''
if (self.joystick.get_button(8)):
self._recording = True
if (self.joystick.get_button(9)):
self._recording = False
'''
if (self.joystick.get_button(6)):
self._recording = True
print("start recording!!!!!!!!!!!!!!!!!!!!!!!!1")
if (self.joystick.get_button(7)):
self._recording = False
print("end recording!!!!!!!!!!!!!!!!!!!!!!!!1")
return self._recording
def initialize_map(self, city_name):
self.city_name_demo = city_name
if city_name == "RFS_MAP":
path = get_current_folder() + "/maps_demo_area/rfs_demo_area.png"
im = cv2.imread(path)
im = im[:, :, :3]
im = im[:, :, ::-1]
self.demo_area_map = im
else:
print("do nothing since not a city with demo area")
#raise ValueError("wrong city name: " + city_name)
def loc_to_pix_rfs_sim(self, loc):
u = 3.6090651558073654 * loc[1] + 2500.541076487252
v = -3.6103367739019054 * loc[0] + 2501.862578166202
return [int(v), int(u)]
def in_valid_area(self, x, y):
if self.city_name_demo == "RFS_MAP":
pos = self.loc_to_pix_rfs_sim([x, y])
locality = 50 # 100 pixels
local_area = self.demo_area_map[pos[0] - locality:pos[0] +
locality, pos[1] -
locality:pos[1] + locality, 0] > 0
valid = np.sum(local_area) > 0
if not valid:
print(
"detect the vehicle is not in the valid demonstrated area")
return valid
else:
return True
def get_reset(self):
if self._autopilot:
if __CARLA_VERSION__ == '0.8.X':
# increase the stuck detector if conditions satisfy
if self._latest_measurements.player_measurements.forward_speed < 0.1:
self._stucked_counter += 1
else:
self._stucked_counter = 0
# if within auto pilot, reset if long enough or has collisions
if time.time() - self._start_time > self._reset_period \
or self._latest_measurements.player_measurements.collision_vehicles > 0.0 \
or self._latest_measurements.player_measurements.collision_pedestrians > 0.0 \
or self._latest_measurements.player_measurements.collision_other > 0.0 \
or (self._latest_measurements.player_measurements.intersection_otherlane > 0.0 and self._latest_measurements.player_measurements.autopilot_control.steer < -0.99) \
or self._stucked_counter > 250:
if self._stucked_counter > 250:
reset_because_stuck = True
else:
reset_because_stuck = False
# TODO: commenting out this for debugging issue
self._reset()
if reset_because_stuck:
print("resetting because getting stucked.....")
return True
else:
# TODO: implement the collision detection algorithm, based on the new API
if self.last_estimated_speed < 0.1:
self._stucked_counter += 1
else:
self._stucked_counter = 0
if time.time() - self._start_time > self._reset_period \
or self._last_collided \
or self._stucked_counter > 250 \
or not self.in_valid_area(self._latest_measurements.player_measurements.transform.location.x,
self._latest_measurements.player_measurements.transform.location.y):
#or np.abs(self._vehicle.get_vehicle_control().steer) > 0.95:
#or np.abs(self._vehicle.get_vehicle_control().brake) > 1:
# TODO intersection other lane is not available, so omit from the condition right now
if self._stucked_counter > 250:
reset_because_stuck = True
else:
reset_because_stuck = False
if self._last_collided:
print("reset becuase collision")
self._reset()
if reset_because_stuck:
print("resetting because getting stucked.....")
return True
else:
pass
return False
def get_waypoints(self):
# TODO: waiting for German Ros to expose the waypoints
wp1 = [1.0, 1.0]
wp2 = [2.0, 2.0]
return [wp1, wp2]
def action_joystick(self):
# joystick
steering_axis = self.joystick.get_axis(0)
acc_axis = self.joystick.get_axis(2)
brake_axis = self.joystick.get_axis(5)
# print("axis 0 %f, axis 2 %f, axis 3 %f" % (steering_axis, acc_axis, brake_axis))
if (self.joystick.get_button(3)):
self._rear = True
if (self.joystick.get_button(2)):
self._rear = False
control = VehicleControl()
control.steer = steering_axis
control.throttle = (acc_axis + 1) / 2.0
control.brake = (brake_axis + 1) / 2.0
if control.brake < 0.001:
control.brake = 0.0
control.hand_brake = 0
control.reverse = self._rear
control.steer -= 0.0822
#print("steer %f, throttle %f, brake %f" % (control.steer, control.throttle, control.brake))
pygame.event.pump()
return control
def action_steering_wheel(self, jsInputs, jsButtons):
control = VehicleControl()
# Custom function to map range of inputs [1, -1] to outputs [0, 1] i.e 1 from inputs means nothing is pressed
# For the steering, it seems fine as it is
K1 = 1.0 # 0.55
steerCmd = K1 * math.tan(1.1 * jsInputs[self._steer_idx])
K2 = 1.6 # 1.6
throttleCmd = K2 + (
2.05 * math.log10(-0.7 * jsInputs[self._throttle_idx] + 1.4) -
1.2) / 0.92
if throttleCmd <= 0:
throttleCmd = 0
elif throttleCmd > 1:
throttleCmd = 1
brakeCmd = 1.6 + (2.05 * math.log10(-0.7 * jsInputs[self._brake_idx] +
1.4) - 1.2) / 0.92
if brakeCmd <= 0:
brakeCmd = 0
elif brakeCmd > 1:
brakeCmd = 1
#print("Steer Cmd, ", steerCmd, "Brake Cmd", brakeCmd, "ThrottleCmd", throttleCmd)
control.steer = steerCmd
control.brake = brakeCmd
control.throttle = throttleCmd
toggle = jsButtons[self._reverse_idx]
if toggle == 1:
self._is_on_reverse += 1
if self._is_on_reverse % 2 == 0:
control.reverse = False
if self._is_on_reverse > 1:
self._is_on_reverse = True
if self._is_on_reverse:
control.reverse = True
control.hand_brake = False # jsButtons[self.handbrake_idx]
return control
def compute_action(self, sensor, speed):
if not self._autopilot:
# get pygame input
for event in pygame.event.get():
# Possible joystick actions: JOYAXISMOTION JOYBALLMOTION JOYBUTTONDOWN
# JOYBUTTONUP JOYHATMOTION
if event.type == pygame.JOYBUTTONDOWN:
if event.__dict__['button'] == 0:
self._current_command = 2.0
if event.__dict__['button'] == 1:
self._current_command = 3.0
if event.__dict__['button'] == 2:
self._current_command = 4.0
if event.__dict__['button'] == 3:
self._current_command = 5.0
if event.__dict__['button'] == 23:
self._current_command = 0.0
if event.__dict__['button'] == 4:
self._reset()
return VehicleControl()
if event.type == pygame.JOYBUTTONUP:
self._current_command = 2.0
#pygame.event.pump()
numAxes = self.joystick.get_numaxes()
jsInputs = [
float(self.joystick.get_axis(i)) for i in range(numAxes)
]
# print (jsInputs)
jsButtons = [
float(self.joystick.get_button(i))
for i in range(self.joystick.get_numbuttons())
]
if self._steering_wheel_flag:
control = self.action_steering_wheel(jsInputs, jsButtons)
else:
control = self.action_joystick()
else:
if __CARLA_VERSION__ == '0.8.X':
# This relies on the calling of get_sensor_data, otherwise self._latest_measurements are not filled
control = self._latest_measurements.player_measurements.autopilot_control
print('[Throttle = {}] [Steering = {}] [Brake = {}]'.format(
control.throttle, control.steer, control.brake))
else:
self._world.tick()
if self._world.wait_for_tick(10.0):
control, self._current_command = self._agent_autopilot.run_step(
)
print('[Throttle = {}] [Steering = {}] [Brake = {}]'.format(
control.throttle, control.steer, control.brake))
return control
def estimate_speed(self):
vel = self._vehicle.get_velocity()
speed = m.sqrt(vel.x**2 + vel.y**2 + vel.z**2)
# speed in m/s
return speed
def get_sensor_data(self, goal_pos=None, goal_ori=None):
if __CARLA_VERSION__ == '0.8.X':
# return the latest measurement and the next direction
measurements, sensor_data = self.carla.read_data()
self._latest_measurements = measurements
if self.use_planner:
player_data = measurements.player_measurements
pos = [
player_data.transform.location.x,
player_data.transform.location.y, 0.22
]
ori = [
player_data.transform.orientation.x,
player_data.transform.orientation.y,
player_data.transform.orientation.z
]
if sldist([
player_data.transform.location.x,
player_data.transform.location.y
], [
self.positions[self.episode_config[1]].location.x,
self.positions[self.episode_config[1]].location.y
]) < self._goal_reaching_threshold:
self._reset()
direction = self.planner.get_next_command(
pos, ori, [
self.positions[self.episode_config[1]].location.x,
self.positions[self.episode_config[1]].location.y, 0.22
], (1, 0, 0))
else:
direction = 2.0
else:
self.last_timestamp.elapsed_seconds += 0.2
#self.last_timestamp = self.carla.get_world().wait_for_tick(30.0)
#print(timestamp.delta_seconds, "delta seconds")
#while self.update_once == False:
# time.sleep(0.01)
self.last_estimated_speed = self.estimate_speed()
data_buffer_lock.acquire()
sensor_data = copy.deepcopy(self._data_buffers)
data_buffer_lock.release()
#self.update_once = False
collision_lock.acquire()
colllision_event = self._collision_events
self._last_collided = len(colllision_event) > 0
self._collision_events = []
collision_lock.release()
if len(colllision_event) > 0:
print(colllision_event)
# TODO: make sure those events are actually valid
if 'Static' in colllision_event:
collision_other = 1.0
else:
collision_other = 0.0
if "Vehicles" in colllision_event:
collision_vehicles = 1.0
else:
collision_vehicles = 0.0
if "Pedestrians" in colllision_event:
collision_pedestrians = 1.0
else:
collision_pedestrians = 0.0
#current_ms_offset = int(math.ceil((datetime.now() - self._episode_t0).total_seconds() * 1000))
# TODO: get a gametime stamp, instead of os timestamp
#current_ms_offset = int(self.carla.get_timestamp().elapsed_seconds * 1000)
#print(current_ms_offset, "ms offset")
current_ms_offset = self.last_timestamp.elapsed_seconds * 1000
second_level = namedtuple('second_level', [
'forward_speed', 'transform', 'collision_other',
'collision_pedestrians', 'collision_vehicles'
])
transform = namedtuple('transform', ['location', 'orientation'])
loc = namedtuple('loc', ['x', 'y'])
ori = namedtuple('ori', ['x', 'y', 'z'])
Meas = namedtuple('Meas',
['player_measurements', 'game_timestamp'])
v_transform = self._vehicle.get_transform()
measurements = Meas(
second_level(
self.last_estimated_speed,
transform(
loc(v_transform.location.x, v_transform.location.y),
ori(v_transform.rotation.pitch,
v_transform.rotation.roll,
v_transform.rotation.yaw)), collision_other,
collision_pedestrians, collision_vehicles),
current_ms_offset)
direction = self._current_command
self._latest_measurements = measurements
#print('[Speed = {} Km/h] [Direction = {}]'.format(measurements.player_measurements.forward_speed, direction))
#print(">>>>> planner output direction: ", direction)
return measurements, sensor_data, direction
def act(self, control):
if __CARLA_VERSION__ == '0.8.X':
self.carla.send_control(control)
else:
self._vehicle.apply_control(control)
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG):
self.config = config
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
if config["discrete_actions"]:
self.action_space = Discrete(len(DISCRETE_ACTIONS))
else:
self.action_space = Box(-1.0, 1.0, shape=(2, ), dtype=np.float32)
if config["use_depth_camera"]:
image_space = Box(
-1.0,
1.0,
shape=(config["y_res"], config["x_res"],
1 * config["framestack"]),
dtype=np.float32)
else:
image_space = Box(
0,
255,
shape=(config["y_res"], config["x_res"],
3 * config["framestack"]),
dtype=np.uint8)
self.observation_space = Tuple( # forward_speed, dist to goal
[
image_space,
Discrete(len(COMMANDS_ENUM)), # next_command
Box(-128.0, 128.0, shape=(2, ), dtype=np.float32)
])
# TODO(ekl) this isn't really a proper gym spec
self._spec = lambda: None
self._spec.id = "Carla-v0"
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(10000, 60000)
self.server_process = subprocess.Popen(
[
SERVER_BINARY, self.config["server_map"], "-windowed",
"-ResX=400", "-ResY=300", "-carla-server",
"-carla-world-port={}".format(self.server_port)
],
preexec_fn=os.setsid,
stdout=open(os.devnull, "w"))
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(self):
self.clear_server_state()
def reset(self):
error = None
for _ in range(RETRIES_ON_ERROR):
try:
if not self.server_process:
self.init_server()
return self._reset()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
raise error
def _reset(self):
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather)
settings.randomize_seeds()
if self.config["use_depth_camera"]:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera1.set_position(30, 0, 130)
settings.add_sensor(camera1)
camera2 = Camera("CameraRGB")
camera2.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera2.set_position(30, 0, 130)
settings.add_sensor(camera2)
# Setup start and end positions
scene = self.client.load_settings(settings)
positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x // 100, self.start_pos.location.y // 100
]
self.end_coord = [
self.end_pos.location.x // 100, self.end_pos.location.y // 100
]
print("Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], self.start_coord,
self.scenario["end_pos_id"], self.end_coord))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
image, py_measurements = self._read_observation()
self.prev_measurement = py_measurements
return self.encode_obs(self.preprocess_image(image), py_measurements)
def encode_obs(self, image, py_measurements):
assert self.config["framestack"] in [1, 2]
prev_image = self.prev_image
self.prev_image = image
if prev_image is None:
prev_image = image
if self.config["framestack"] == 2:
image = np.concatenate([prev_image, image], axis=2)
obs = (image, COMMAND_ORDINAL[py_measurements["next_command"]], [
py_measurements["forward_speed"],
py_measurements["distance_to_goal"]
])
self.last_obs = obs
return obs
def step(self, action):
try:
obs = self._step(action)
return obs
except Exception:
print("Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_obs, 0.0, True, {})
def _step(self, action):
if self.config["discrete_actions"]:
action = DISCRETE_ACTIONS[int(action)]
assert len(action) == 2, "Invalid action {}".format(action)
if self.config["squash_action_logits"]:
forward = 2 * float(sigmoid(action[0]) - 0.5)
throttle = float(np.clip(forward, 0, 1))
brake = float(np.abs(np.clip(forward, -1, 0)))
steer = 2 * float(sigmoid(action[1]) - 0.5)
else:
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
reverse = False
hand_brake = False
if self.config["verbose"]:
print("steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
self.client.send_control(
steer=steer,
throttle=throttle,
brake=brake,
hand_brake=hand_brake,
reverse=reverse)
# Process observations
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
reward = compute_reward(self, self.prev_measurement, py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
done = (self.num_steps > self.scenario["max_steps"]
or py_measurements["next_command"] == "REACH_GOAL"
or (self.config["early_terminate_on_collision"]
and collided_done(py_measurements)))
py_measurements["done"] = done
self.prev_measurement = py_measurements
# Write out measurements to file
if CARLA_OUT_PATH:
if not self.measurements_file:
self.measurements_file = open(
os.path.join(
CARLA_OUT_PATH,
"measurements_{}.json".format(self.episode_id)), "w")
self.measurements_file.write(json.dumps(py_measurements))
self.measurements_file.write("\n")
if done:
self.measurements_file.close()
self.measurements_file = None
if self.config["convert_images_to_video"]:
self.images_to_video()
self.num_steps += 1
image = self.preprocess_image(image)
return (self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def images_to_video(self):
videos_dir = os.path.join(CARLA_OUT_PATH, "Videos")
if not os.path.exists(videos_dir):
os.makedirs(videos_dir)
ffmpeg_cmd = (
"ffmpeg -loglevel -8 -r 60 -f image2 -s {x_res}x{y_res} "
"-start_number 0 -i "
"{img}_%04d.jpg -vcodec libx264 {vid}.mp4 && rm -f {img}_*.jpg "
).format(
x_res=self.config["render_x_res"],
y_res=self.config["render_y_res"],
vid=os.path.join(videos_dir, self.episode_id),
img=os.path.join(CARLA_OUT_PATH, "CameraRGB", self.episode_id))
print("Executing ffmpeg command", ffmpeg_cmd)
subprocess.call(ffmpeg_cmd, shell=True)
def preprocess_image(self, image):
if self.config["use_depth_camera"]:
assert self.config["use_depth_camera"]
data = (image.data - 0.5) * 2
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 1)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.expand_dims(data, 2)
else:
data = image.data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = (data.astype(np.float32) - 128) / 128
return data
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
observation = None
if self.config["use_depth_camera"]:
camera_name = "CameraDepth"
else:
camera_name = "CameraRGB"
for name, image in sensor_data.items():
if name == camera_name:
observation = image
cur = measurements.player_measurements
if self.config["enable_planner"]:
next_command = COMMANDS_ENUM[self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
])]
else:
next_command = "LANE_FOLLOW"
if next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
elif self.config["enable_planner"]:
distance_to_goal = self.planner.get_shortest_path_distance([
cur.transform.location.x, cur.transform.location.y, GROUND_Z
], [
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
], [self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
]) / 100
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(
np.linalg.norm([
cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y
]) / 100)
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": cur.forward_speed,
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"intersection_offroad": cur.intersection_offroad,
"intersection_otherlane": cur.intersection_otherlane,
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command": next_command,
}
if CARLA_OUT_PATH and self.config["log_images"]:
for name, image in sensor_data.items():
out_dir = os.path.join(CARLA_OUT_PATH, name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(
out_dir, "{}_{:>04}.jpg".format(self.episode_id,
self.num_steps))
scipy.misc.imsave(out_file, image.data)
assert observation is not None, sensor_data
return observation, py_measurements
class DrivingBenchmark(object):
"""
The Benchmark class, controls the execution of the benchmark interfacing
an Agent class with a set Suite.
The benchmark class must be inherited with a class that defines the
all the experiments to be run by the agent
"""
def __init__(
self,
city_name='Town01',
name_to_save='Test',
continue_experiment=False,
save_images=False,
distance_for_success=2.0
):
self.__metaclass__ = abc.ABCMeta
self._city_name = city_name
self._base_name = name_to_save
# The minimum distance for arriving into the goal point in
# order to consider ir a success
self._distance_for_success = distance_for_success
# The object used to record the benchmark and to able to continue after
self._recording = Recording(name_to_save=name_to_save,
continue_experiment=continue_experiment,
save_images=save_images
)
# We have a default planner instantiated that produces high level commands
self._planner = Planner(city_name)
self.carla_map = CarlaMap(city_name, 0.1653, 50)
def benchmark_agent(self, experiment_suite, agent, client):
"""
Function to benchmark the agent.
It first check the log file for this benchmark.
if it exist it continues from the experiment where it stopped.
Args:
experiment_suite
agent: an agent object with the run step class implemented.
client:
Return:
A dictionary with all the metrics computed from the
agent running the set of experiments.
"""
# Instantiate a metric object that will be used to compute the metrics for
# the benchmark afterwards.
metrics_object = Metrics(experiment_suite.metrics_parameters,
experiment_suite.dynamic_tasks)
# Function return the current pose and task for this benchmark.
start_pose, start_experiment = self._recording.get_pose_and_experiment(
experiment_suite.get_number_of_poses_task())
logging.info('START')
for experiment in experiment_suite.get_experiments()[int(start_experiment):]:
positions = client.load_settings(
experiment.conditions).player_start_spots
self._recording.log_start(experiment.task)
for pose in experiment.poses[start_pose:]:
for rep in range(experiment.repetitions):
start_index = pose[0]
end_index = pose[1]
client.start_episode(start_index)
# Print information on
logging.info('======== !!!! ==========')
logging.info(' Start Position %d End Position %d ',
start_index, end_index)
self._recording.log_poses(start_index, end_index,
experiment.Conditions.WeatherId)
# Calculate the initial distance for this episode
initial_distance = \
sldist(
[positions[start_index].location.x, positions[start_index].location.y],
[positions[end_index].location.x, positions[end_index].location.y])
time_out = experiment_suite.calculate_time_out(
self._get_shortest_path(positions[start_index], positions[end_index]))
# running the agent
(result, reward_vec, control_vec, final_time, remaining_distance) = \
self._run_navigation_episode(
agent, client, time_out, positions[end_index],
str(experiment.Conditions.WeatherId) + '_'
+ str(experiment.task) + '_' + str(start_index)
+ '.' + str(end_index))
# Write the general status of the just ran episode
self._recording.write_summary_results(
experiment, pose, rep, initial_distance,
remaining_distance, final_time, time_out, result)
# Write the details of this episode.
self._recording.write_measurements_results(experiment, rep, pose, reward_vec,
control_vec)
if result > 0:
logging.info('+++++ Target achieved in %f seconds! +++++',
final_time)
else:
logging.info('----- Timeout! -----')
start_pose = 0
self._recording.log_end()
return metrics_object.compute(self._recording.path)
def get_path(self):
"""
Returns the path were the log was saved.
"""
return self._recording.path
def _get_directions(self, current_point, end_point):
"""
Class that should return the directions to reach a certain goal
"""
directions = self._planner.get_next_command(
(current_point.location.x,
current_point.location.y, 0.22),
(current_point.orientation.x,
current_point.orientation.y,
current_point.orientation.z),
(end_point.location.x, end_point.location.y, 0.22),
(end_point.orientation.x, end_point.orientation.y, end_point.orientation.z))
return directions
def _get_shortest_path(self, start_point, end_point):
"""
Calculates the shortest path between two points considering the road netowrk
"""
return self._planner.get_shortest_path_distance(
[
start_point.location.x, start_point.location.y, 0.22], [
start_point.orientation.x, start_point.orientation.y, 0.22], [
end_point.location.x, end_point.location.y, end_point.location.z], [
end_point.orientation.x, end_point.orientation.y, end_point.orientation.z])
def load_empty_trajectory(self):
original_path = "./drive_interfaces/carla/carla_client/carla/planner/" + self._city_name + ".png"
self.trajectory_img = cv2.imread(original_path)
def update_trajectory(self, curr_x, curr_y, tar_x, tar_y, is_first, directions):
cur = self.carla_map.convert_to_pixel([curr_x, curr_y, .22])
cur = [int(cur[1]), int(cur[0])]
tar = self.carla_map.convert_to_pixel([tar_x, tar_y, .22])
tar = [int(tar[1]), int(tar[0])]
print("current location", cur, "final location", tar)
if is_first:
trj_sz = 10
else:
trj_sz = 3
directions_to_color={0.0: [255, 0, 0], 2.0: [255, 0, 0], 5.0: [255, 0, 0],
3.0: [0, 255, 0], 4.0: [0, 0, 255]}
color = directions_to_color[directions]
self.trajectory_img[cur[0] - trj_sz:cur[0] + trj_sz, cur[1] - trj_sz:cur[1] + trj_sz, 0] = color[0]
self.trajectory_img[cur[0] - trj_sz:cur[0] + trj_sz, cur[1] - trj_sz:cur[1] + trj_sz, 1] = color[1]
self.trajectory_img[cur[0] - trj_sz:cur[0] + trj_sz, cur[1] - trj_sz:cur[1] + trj_sz, 2] = color[2]
tar_sz = 10
self.trajectory_img[tar[0] - tar_sz:tar[0] + tar_sz, tar[1] - tar_sz:tar[1] + tar_sz, 0] = 0
self.trajectory_img[tar[0] - tar_sz:tar[0] + tar_sz, tar[1] - tar_sz:tar[1] + tar_sz, 1] = 0
self.trajectory_img[tar[0] - tar_sz:tar[0] + tar_sz, tar[1] - tar_sz:tar[1] + tar_sz, 2] = 0
def save_trajectory_image(self, episode_name):
out_name = os.path.join(self._recording._path, '_images/episode_{:s}_trajectory.png'.format(episode_name))
folder = os.path.dirname(out_name)
if not os.path.isdir(folder):
os.makedirs(folder)
cv2.imwrite(out_name, self.trajectory_img)
def measurements_to_pos_yaw(self, measurements):
pos = [measurements.player_measurements.transform.location.x,
measurements.player_measurements.transform.location.y ]
ori = [measurements.player_measurements.transform.orientation.x,
measurements.player_measurements.transform.orientation.y,
measurements.player_measurements.transform.orientation.z ]
city_name = {"Town01": "01",
"Town02": "02",
"RFS_MAP": "10",
"Exp_Town": "11",
"Exp_Town02": "11",
"Exp_Town01_02Shoulder": "13"}[self._city_name]
return {"town_id": city_name, "pos": pos, "ori": ori}
def _run_navigation_episode(
self,
agent,
client,
time_out,
target,
episode_name):
"""
Run one episode of the benchmark (Pose) for a certain agent.
Args:
agent: the agent object
client: an object of the carla client to communicate
with the CARLA simulator
time_out: the time limit to complete this episode
target: the target to reach
episode_name: The name for saving images of this episode
"""
# Send an initial command.
measurements, sensor_data = client.read_data()
client.send_control(VehicleControl())
initial_timestamp = measurements.game_timestamp
current_timestamp = initial_timestamp
# The vector containing all measurements produced on this episode
measurement_vec = []
# The vector containing all controls produced on this episode
control_vec = []
frame = 0
distance = 10000
success = False
self.load_empty_trajectory()
agent.debug_i = 0
agent.temp_image_path = self._recording._path
stuck_counter = 0
pre_x = 0.0
pre_y = 0.0
last_collision_ago = 1000000000
is_first = True
while (current_timestamp - initial_timestamp) < (time_out * 1000) and not success:
# Read data from server with the client
measurements, sensor_data = client.read_data()
# The directions to reach the goal are calculated.
directions = self._get_directions(measurements.player_measurements.transform, target)
# Agent process the data.
control = agent.run_step(measurements, sensor_data, directions, target, self.measurements_to_pos_yaw(measurements))
# Send the control commands to the vehicle
client.send_control(control)
# save images if the flag is activated
#sensor_data = self._recording.yang_annotate_images(sensor_data, directions)
#self._recording.save_images(sensor_data, episode_name, frame)
current_x = measurements.player_measurements.transform.location.x
current_y = measurements.player_measurements.transform.location.y
logging.info("Controller is Inputting:")
logging.info('Steer = %f Throttle = %f Brake = %f ',
control.steer, control.throttle, control.brake)
# game timestamp is in microsecond
current_timestamp = measurements.game_timestamp
# Get the distance travelled until now
distance = sldist([current_x, current_y],
[target.location.x, target.location.y])
self.update_trajectory(current_x, current_y, target.location.x, target.location.y, is_first, directions)
is_first = False
# Write status of the run on verbose mode
logging.info('Status:')
logging.info(
'[d=%f] c_x = %f, c_y = %f ---> t_x = %f, t_y = %f',
float(distance), current_x, current_y, target.location.x,
target.location.y)
# Check if reach the target
if distance < self._distance_for_success:
success = True
# Increment the vectors and append the measurements and controls.
frame += 1
measurement_vec.append(measurements.player_measurements)
control_vec.append(control)
pm = measurements.player_measurements
if pm.collision_vehicles > 0.0 \
or pm.collision_pedestrians > 0.0 \
or pm.collision_other > 0.0:
last_collision_ago = 0
else:
last_collision_ago += 1
if sldist([current_x, current_y], [pre_x, pre_y]) < 0.1:
stuck_counter += 1
else:
stuck_counter = 0
pre_x = current_x
pre_y = current_y
if stuck_counter > 400:
print("breaking because of stucking too long")
break
if stuck_counter > 30 and last_collision_ago < 50:
#print("breaking because of collision and stuck")
#break
# disable the breaking
pass
if math.fabs(directions) < 0.1:
# The goal state is reached.
success = True
# convert the images saved by the underlying to a video
if self._recording._save_images:
# convert the saved images to a video, and store it in the right place
# we have to save the image within the carla_machine, since only that will know what's the cutting plane
# assume that the images lies around at ./temp/%05d.png
print("converting images to a video...")
out_name = os.path.join(self._recording._path, '_images/episode_{:s}.mp4'.format(episode_name))
folder = os.path.dirname(out_name)
if not os.path.isdir(folder):
os.makedirs(folder)
cmd = ["ffmpeg", "-y", "-i", agent.temp_image_path+"/%09d.png", "-c:v", "libx264", out_name]
call(" ".join(cmd), shell=True)
cmd = ["find", agent.temp_image_path, "-name", "00*png", "-print | xargs rm"]
call(" ".join(cmd), shell=True)
self.save_trajectory_image(episode_name)
if success:
return 1, measurement_vec, control_vec, float(
current_timestamp - initial_timestamp) / 1000.0, distance
return 0, measurement_vec, control_vec, time_out, distance
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG):
self.config = config
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
if config["discrete_actions"]:
self.action_space = Discrete(len(DISCRETE_ACTIONS))
else:
self.action_space = Box(-1.0, 1.0, shape=(2, ), dtype=np.float32)
if config["use_depth_camera"]:
image_space = Box(-1.0,
1.0,
shape=(config["y_res"], config["x_res"],
1 * config["framestack"]),
dtype=np.float32)
else:
image_space = Box(0,
255,
shape=(config["y_res"], config["x_res"],
3 * config["framestack"]),
dtype=np.uint8)
self.observation_space = Tuple( # forward_speed, dist to goal
[
image_space,
Discrete(len(COMMANDS_ENUM)), # next_command
Box(-128.0, 128.0, shape=(2, ), dtype=np.float32)
])
# TODO(ekl) this isn't really a proper gym spec
self._spec = lambda: None
self._spec.id = "Carla-v0"
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(10000, 60000)
self.server_process = subprocess.Popen([
SERVER_BINARY, self.config["server_map"], "-windowed", "-ResX=800",
"-ResY=600", "-carla-server", "-benchmark -fps=10",
"-carla-world-port={}".format(self.server_port)
],
preexec_fn=os.setsid,
stdout=open(os.devnull, "w"))
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(self):
self.clear_server_state()
def reset(self):
error = None
for _ in range(RETRIES_ON_ERROR):
try:
if not self.server_process:
self.init_server()
return self._reset()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
raise error
def _reset(self):
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(SynchronousMode=True,
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather)
settings.randomize_seeds()
if self.config["use_depth_camera"]:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera1.set_position(0.1, 0, 1.7)
settings.add_sensor(camera1)
camera2 = Camera("CameraRGB")
camera2.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera2.set_position(0.1, 0, 1.7)
settings.add_sensor(camera2)
# Setup start and end positions
scene = self.client.load_settings(settings)
positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x // 100, self.start_pos.location.y // 100
]
self.end_coord = [
self.end_pos.location.x // 100, self.end_pos.location.y // 100
]
print("Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], self.start_coord,
self.scenario["end_pos_id"], self.end_coord))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
image, py_measurements = self._read_observation()
self.prev_measurement = py_measurements
return self.encode_obs(self.preprocess_image(image), py_measurements)
def encode_obs(self, image, py_measurements):
assert self.config["framestack"] in [1, 2]
prev_image = self.prev_image
self.prev_image = image
if prev_image is None:
prev_image = image
if self.config["framestack"] == 2:
image = np.concatenate([prev_image, image], axis=2)
obs = (image, COMMAND_ORDINAL[py_measurements["next_command"]], [
py_measurements["forward_speed"],
py_measurements["distance_to_goal"]
])
self.last_obs = obs
return obs
def step(self, action):
try:
obs = self._step(action)
return obs
except Exception:
print("Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_obs, 0.0, True, {})
def _step(self, action):
if self.config["discrete_actions"]:
action = DISCRETE_ACTIONS[int(action)]
assert len(action) == 2, "Invalid action {}".format(action)
if self.config["squash_action_logits"]:
forward = 2 * float(sigmoid(action[0]) - 0.5)
throttle = float(np.clip(forward, 0, 1))
brake = float(np.abs(np.clip(forward, -1, 0)))
steer = 2 * float(sigmoid(action[1]) - 0.5)
else:
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
reverse = False
hand_brake = False
if self.config["verbose"]:
print("steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
self.client.send_control(steer=steer,
throttle=throttle,
brake=brake,
hand_brake=hand_brake,
reverse=reverse)
# Process observations
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
reward = compute_reward(self, self.prev_measurement, py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
done = (self.num_steps > self.scenario["max_steps"]
or py_measurements["next_command"] == "REACH_GOAL"
or (self.config["early_terminate_on_collision"]
and collided_done(py_measurements)))
py_measurements["done"] = done
self.prev_measurement = py_measurements
# Write out measurements to file
if CARLA_OUT_PATH:
if not self.measurements_file:
self.measurements_file = open(
os.path.join(
CARLA_OUT_PATH,
"measurements_{}.json".format(self.episode_id)), "w")
self.measurements_file.write(json.dumps(py_measurements))
self.measurements_file.write("\n")
if done:
self.measurements_file.close()
self.measurements_file = None
if self.config["convert_images_to_video"]:
self.images_to_video()
self.num_steps += 1
image = self.preprocess_image(image)
return (self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def images_to_video(self):
videos_dir = os.path.join(CARLA_OUT_PATH, "Videos")
if not os.path.exists(videos_dir):
os.makedirs(videos_dir)
ffmpeg_cmd = (
"ffmpeg -loglevel -8 -r 60 -f image2 -s {x_res}x{y_res} "
"-start_number 0 -i "
"{img}_%04d.jpg -vcodec libx264 {vid}.mp4 && rm -f {img}_*.jpg "
).format(x_res=self.config["render_x_res"],
y_res=self.config["render_y_res"],
vid=os.path.join(videos_dir, self.episode_id),
img=os.path.join(CARLA_OUT_PATH, "CameraRGB",
self.episode_id))
print("Executing ffmpeg command", ffmpeg_cmd)
subprocess.call(ffmpeg_cmd, shell=True)
def preprocess_image(self, image):
if self.config["use_depth_camera"]:
assert self.config["use_depth_camera"]
data = (image.data - 0.5) * 2
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 1)
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.expand_dims(data, 2)
else:
data = image.data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = (data.astype(np.float32) - 128) / 128
return data
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
observation = None
if self.config["use_depth_camera"]:
camera_name = "CameraDepth"
else:
camera_name = "CameraRGB"
for name, image in sensor_data.items():
if name == camera_name:
observation = image
cur = measurements.player_measurements
if self.config["enable_planner"]:
next_command = COMMANDS_ENUM[self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
])]
else:
next_command = "LANE_FOLLOW"
if next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
elif self.config["enable_planner"]:
distance_to_goal = self.planner.get_shortest_path_distance([
cur.transform.location.x, cur.transform.location.y, GROUND_Z
], [
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
], [self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
]) / 100
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(
np.linalg.norm([
cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y
]) / 100)
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": cur.forward_speed,
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"intersection_offroad": cur.intersection_offroad,
"intersection_otherlane": cur.intersection_otherlane,
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command":
next_command, # TODO can we figure some way using this command
}
if CARLA_OUT_PATH and self.config["log_images"]:
for name, image in sensor_data.items():
out_dir = os.path.join(CARLA_OUT_PATH, name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(
out_dir, "{}_{:>04}.jpg".format(self.episode_id,
self.num_steps))
scipy.misc.imsave(out_file, image.data)
assert observation is not None, sensor_data
return observation, py_measurements
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG):
self.config = config
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
# The Action Space
if config["discrete_actions"]:
self.action_space = Discrete(
len(DISCRETE_ACTIONS
)) # It will be transformed to continuous 2D action.
else:
self.action_space = Box(-1.0, 1.0, shape=(2, ),
dtype=np.float32) # 2D action.
if config["use_depth_camera"]:
image_space = Box(-1.0,
1.0,
shape=(config["y_res"], config["x_res"],
1 * config["framestack"]),
dtype=np.float32)
else:
image_space = Box(0,
255,
shape=(config["y_res"], config["x_res"],
3 * config["framestack"]),
dtype=np.uint8)
# encode_measure ---> rgb + depth + pre_rgb + measurement_encode
# 3 + 1 + 3 + 1 = 8
if config["encode_measurement"]:
image_space = Box(0,
255,
shape=(config["y_res"], config["x_res"], 8),
dtype=np.float32)
# The Observation Space
self.observation_space = Tuple([
image_space,
Discrete(len(COMMANDS_ENUM)), # next_command
Box(-128.0, 128.0, shape=(2, ),
dtype=np.float32) # forward_speed, dist to goal
])
# TODO(ekl) this isn't really a proper gym spec
self._spec = lambda: None
self._spec.id = "Carla-v0"
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(1000, 60000)
self.server_process = subprocess.Popen(
[
SERVER_BINARY,
self.config["server_map"],
"-windowed",
"-ResX=800",
"-ResY=600",
"-carla-server",
"-benchmark -fps=10", #: to run the simulation at a fixed time-step of 0.1 seconds
"-carla-world-port={}".format(self.server_port)
],
preexec_fn=os.setsid,
stdout=open(os.devnull, "w"))
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(
self
): # the __del__ method will be called when the instance of the class is deleted.(memory is freed.)
self.clear_server_state()
def reset(self):
error = None
for _ in range(RETRIES_ON_ERROR):
try:
if not self.server_process:
self.init_server()
return self._reset()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
raise error
def _reset(self):
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(
SynchronousMode=True,
# ServerTimeOut=10000, # CarlaSettings: no key named 'ServerTimeOut'
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather)
settings.randomize_seeds()
if self.config["use_depth_camera"]:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
# camera1.set_position(30, 0, 170)
camera1.set_position(0.5, 0.0, 1.6)
# camera1.set_rotation(0.0, 0.0, 0.0)
settings.add_sensor(camera1)
if self.config["encode_measurement"]:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
# camera1.set_position(30, 0, 170)
camera1.set_position(0.5, 0.0, 1.6)
camera1.set(FOV=120)
settings.add_sensor(camera1)
camera2 = Camera("CameraRGB")
camera2.set_image_size(self.config["render_x_res"],
self.config["render_y_res"])
camera2.set(FOV=120)
camera2.set_position(0.5, 0.0, 1.6)
settings.add_sensor(camera2)
# Setup start and end positions
scene = self.client.load_settings(settings)
positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x, self.start_pos.location.y
]
self.end_coord = [self.end_pos.location.x, self.end_pos.location.y]
print("Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], [int(x) for x in self.start_coord],
self.scenario["end_pos_id"], [int(x) for x in self.end_coord]))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
# Process observations: self._read_observation() returns image and py_measurements.
image, py_measurements = self._read_observation()
self.prev_measurement = py_measurements
return self.encode_obs(self.preprocess_image(image), py_measurements)
# rgb depth forward_speed next_comment
def encode_obs(self, image, py_measurements):
assert self.config["framestack"] in [1, 2]
prev_image = self.prev_image
self.prev_image = image
if prev_image is None:
prev_image = image
if self.config["framestack"] == 2:
image = np.concatenate([prev_image, image], axis=2)
if self.config["encode_measurement"]:
feature_map = np.zeros([4, 4])
feature_map[1, :] = (py_measurements["forward_speed"] - 15) / 15
feature_map[2, :] = (
COMMAND_ORDINAL[py_measurements["next_command"]] - 2) / 2
feature_map[0, 0] = py_measurements["x_orient"]
feature_map[0, 1] = py_measurements["y_orient"]
feature_map[0,
2] = (py_measurements["distance_to_goal"] - 170) / 170
feature_map[0,
1] = (py_measurements["distance_to_goal_euclidean"] -
170) / 170
feature_map[3, 0] = (py_measurements["x"] - 50) / 150
feature_map[3, 1] = (py_measurements["y"] - 50) / 150
feature_map[3, 2] = (py_measurements["end_coord"][0] - 150) / 150
feature_map[3, 3] = (py_measurements["end_coord"][1] - 150) / 150
feature_map = np.tile(feature_map, (24, 24))
image = np.concatenate(
[prev_image[:, :, 0:3], image, feature_map[:, :, np.newaxis]],
axis=2)
# print("image shape after encoding", image.shape)
obs = (image, COMMAND_ORDINAL[py_measurements["next_command"]], [
py_measurements["forward_speed"],
py_measurements["distance_to_goal"]
])
self.last_obs = obs
# print('distance to goal', py_measurements["distance_to_goal"])
# print("speed", py_measurements["forward_speed"])
return obs
# TODO:example of py_measurement
# {'episode_id': '2019-02-22_11-26-36_990083',
# 'step': 0,
# 'x': 71.53003692626953,
# 'y': 302.57000732421875,
# 'x_orient': -1.0,
# 'y_orient': -6.4373016357421875e-06,
# 'forward_speed': -3.7578740032934155e-13,
# 'distance_to_goal': 0.6572,
# 'distance_to_goal_euclidean': 0.6200001239776611,
# 'collision_vehicles': 0.0,
# 'collision_pedestrians': 0.0,
# 'collision_other': 0.0,
# 'intersection_offroad': 0.0,
# 'intersection_otherlane': 0.0,
# 'weather': 0,
# 'map': '/Game/Maps/Town02',
# 'start_coord': [0.0, 3.0],
# 'end_coord': [0.0, 3.0],
# 'current_scenario': {'city': 'Town02',
# 'num_vehicles': 0,
# 'num_pedestrians': 20,
# 'weather_distribution': [0],
# 'start_pos_id': 36,
# 'end_pos_id': 40,
# 'max_steps': 600},
# 'x_res': 10,
# 'y_res': 10,
# 'num_vehicles': 0,
# 'num_pedestrians': 20,
# 'max_steps': 600,
# 'next_command': 'GO_STRAIGHT',
# 'action': (0, 1),
# 'control': {'steer': 1.0,
# 'throttle': 0.0,
# 'brake': 0.0,
# 'reverse': False,
# 'hand_brake': False},
# 'reward': 0.0,
# 'total_reward': 0.0,
# 'done': False}
def step(self, action):
try:
obs = self._step(action)
return obs
except Exception:
print("Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_obs, 0.0, True, {})
def _step(self, action):
if self.config["discrete_actions"]:
action = DISCRETE_ACTIONS[int(action)] # Carla action is 2D.
assert len(action) == 2, "Invalid action {}".format(action)
if self.config["squash_action_logits"]:
forward = 2 * float(sigmoid(action[0]) - 0.5)
throttle = float(np.clip(forward, 0, 1))
brake = float(np.abs(np.clip(forward, -1, 0)))
steer = 2 * float(sigmoid(action[1]) - 0.5)
else:
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
# reverse and hand_brake are disabled.
reverse = False
hand_brake = False
if self.config["verbose"]:
print("steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
self.client.send_control(steer=steer,
throttle=throttle,
brake=brake,
hand_brake=hand_brake,
reverse=reverse)
# Process observations: self._read_observation() returns image and py_measurements.
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
# compute reward
reward = compute_reward(self, self.prev_measurement, py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
# done or not
done = (self.num_steps > self.scenario["max_steps"]
or py_measurements["next_command"] == "REACH_GOAL"
or (self.config["early_terminate_on_collision"]
and collided_done(py_measurements)))
py_measurements["done"] = done
self.prev_measurement = py_measurements
# Write out measurements to file
if self.config["verbose"] and CARLA_OUT_PATH:
if not self.measurements_file:
self.measurements_file = open(
os.path.join(
CARLA_OUT_PATH,
"measurements_{}.json".format(self.episode_id)), "w")
self.measurements_file.write(json.dumps(py_measurements))
self.measurements_file.write("\n")
if done:
self.measurements_file.close()
self.measurements_file = None
if self.config["convert_images_to_video"]:
self.images_to_video()
self.num_steps += 1
image = self.preprocess_image(image)
return (self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def images_to_video(self):
videos_dir = os.path.join(CARLA_OUT_PATH, "Videos")
if not os.path.exists(videos_dir):
os.makedirs(videos_dir)
ffmpeg_cmd = (
"ffmpeg -loglevel -8 -r 60 -f image2 -s {x_res}x{y_res} "
"-start_number 0 -i "
"{img}_%04d.jpg -vcodec libx264 {vid}.mp4 && rm -f {img}_*.jpg "
).format(x_res=self.config["render_x_res"],
y_res=self.config["render_y_res"],
vid=os.path.join(videos_dir, self.episode_id),
img=os.path.join(CARLA_OUT_PATH, "CameraRGB",
self.episode_id))
print("Executing ffmpeg command", ffmpeg_cmd)
subprocess.call(ffmpeg_cmd, shell=True)
def preprocess_image(self, image):
if self.config["use_depth_camera"]:
assert self.config["use_depth_camera"]
data = (image.data - 0.5) * 2
data = data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 1)
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.expand_dims(data, 2)
elif self.config["encode_measurement"]:
# data = (image - 0.5) * 2
data = image.reshape(self.config["render_y_res"],
self.config["render_x_res"], -1)
#data[:, :, 4] = (data[:, :, 0] - 0.5) * 2
#data[:, :, 0:3] = (data[:, :, 0:2].astype(np.float32) - 128) / 128
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
else:
data = image.data.reshape(self.config["render_y_res"],
self.config["render_x_res"], 3)
data = cv2.resize(data,
(self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = (data.astype(np.float32) - 128) / 128
return data
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
observation = None
if self.config["encode_measurement"]:
# print(sensor_data["CameraRGB"].data.shape, sensor_data["CameraDepth"].data.shape)
observation = np.concatenate(
((sensor_data["CameraRGB"].data.astype(np.float32) - 128) /
128,
(sensor_data["CameraDepth"].data[:, :, np.newaxis] - 0.5) *
0.5),
axis=2)
# print("observation_shape", observation.shape)
else:
if self.config["use_depth_camera"]:
camera_name = "CameraDepth"
else:
camera_name = "CameraRGB"
for name, image in sensor_data.items():
if name == camera_name:
observation = image
cur = measurements.player_measurements
if self.config["enable_planner"]:
next_command = COMMANDS_ENUM[self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
])]
else:
next_command = "LANE_FOLLOW"
if next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
elif self.config["enable_planner"]:
distance_to_goal = self.planner.get_shortest_path_distance(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[
cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z
],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z], [
self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z
])
# now the metrix should be meter carla8.0
# TODO run experience to verify the scale of distance in order to determine reward function
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(
np.linalg.norm([
cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y
]))
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": cur.forward_speed,
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"intersection_offroad": cur.intersection_offroad,
"intersection_otherlane": cur.intersection_otherlane,
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command": next_command,
}
if CARLA_OUT_PATH and self.config["log_images"]:
for name, image in sensor_data.items():
out_dir = os.path.join(CARLA_OUT_PATH, name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
out_file = os.path.join(
out_dir, "{}_{:>04}.jpg".format(self.episode_id,
self.num_steps))
scipy.misc.imsave(out_file, image.data)
assert observation is not None, sensor_data
return observation, py_measurements
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG):
"""
Carla Gym Environment class implementation. Creates an OpenAI Gym compatible driving environment based on
Carla driving simulator.
:param config: A dictionary with environment configuration keys and values
"""
self.config = config
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
if config["discrete_actions"]:
self.action_space = Discrete(len(DISCRETE_ACTIONS))
else:
self.action_space = Box(-1.0, 1.0, shape=(2,), dtype=np.uint8)
if config["use_depth_camera"]:
image_space = Box(
-1.0, 1.0, shape=(
config["y_res"], config["x_res"],
1 * config["framestack"]), dtype=np.float32)
else:
image_space = Box(
0.0, 255.0, shape=(
config["y_res"], config["x_res"],
3 * config["framestack"]), dtype=np.float32)
if self.config["use_image_only_observations"]:
self.observation_space = image_space
else:
self.observation_space = Tuple(
[image_space,
Discrete(len(COMMANDS_ENUM)), # next_command
Box(-128.0, 128.0, shape=(2,), dtype=np.float32)]) # forward_speed, dist to goal
self._spec = lambda: None
self._spec.id = "Carla-v0"
self._seed = ENV_CONFIG["seed"]
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
self.viewer = None
def render(self, mode=None):
filename = f'images/id_{self._spec.id}_ep_{self.episode_id}_step_{self.num_steps}'
self.frame_image.save_to_disk(filename)
# from gym.envs.classic_control import rendering
# if self.viewer is None:
# self.viewer = rq
# endering.SimpleImageViewer()
# self.viewer.imshow(self.frame_image)
# return self.viewer.isopen
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(10000, 60000)
if self.config["render"]:
self.server_process = subprocess.Popen(
[SERVER_BINARY, self.config["server_map"],
"-windowed", "-ResX=400", "-ResY=300",
"-carla-server",
"-carla-world-port={}".format(self.server_port)],
preexec_fn=os.setsid, stdout=open(os.devnull, "w"))
else:
self.server_process = subprocess.Popen(
("SDL_VIDEODRIVER=offscreen SDL_HINT_CUDA_DEVICE={} {} " +
self.config["server_map"] + " -windowed -ResX=400 -ResY=300"
" -carla-server -carla-world-port={}").format(0, SERVER_BINARY, self.server_port),
shell=True, preexec_fn=os.setsid, stdout=open(os.devnull, "w"))
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(self):
self.clear_server_state()
def reset(self):
error = None
for _ in range(RETRIES_ON_ERROR):
try:
if not self.server_process:
self.init_server()
return self.reset_env()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
raise error
def reset_env(self):
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
# If config["scenarios"] is a single scenario, then use it if it's an array of scenarios, randomly choose one and init
if isinstance(self.config["scenarios"],dict):
self.scenario = self.config["scenarios"]
else: #isinstance array of dict
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=True,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather)
settings.randomize_seeds()
if self.config["use_depth_camera"]:
camera1 = Camera("CameraDepth", PostProcessing="Depth")
camera1.set_image_size(
self.config["render_x_res"], self.config["render_y_res"])
camera1.set_position(0.30, 0, 1.30)
settings.add_sensor(camera1)
camera2 = Camera("CameraRGB")
camera2.set_image_size(
self.config["render_x_res"], self.config["render_y_res"])
camera2.set_position(0.30, 0, 1.30)
settings.add_sensor(camera2)
# Setup start and end positions
scene = self.client.load_settings(settings)
positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x // 100, self.start_pos.location.y // 100]
self.end_coord = [
self.end_pos.location.x // 100, self.end_pos.location.y // 100]
print(
"Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], self.start_coord,
self.scenario["end_pos_id"], self.end_coord))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
image, py_measurements = self._read_observation()
self.prev_measurement = py_measurements
return self.encode_obs(self.preprocess_image(image), py_measurements)
def encode_obs(self, image, py_measurements):
assert self.config["framestack"] in [1, 2]
prev_image = self.prev_image
self.prev_image = image
if prev_image is None:
prev_image = image
if self.config["framestack"] == 2:
image = np.concatenate([prev_image, image], axis=2)
if self.config["use_image_only_observations"]:
obs = image
else:
obs = (
image,
COMMAND_ORDINAL[py_measurements["next_command"]],
[py_measurements["forward_speed"],
py_measurements["distance_to_goal"]])
self.last_obs = obs
return obs
def step(self, action):
try:
obs = self.step_env(action)
return obs
except Exception:
print(
"Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_obs, 0.0, True, {})
def step_env(self, action):
if self.config["discrete_actions"]:
action = DISCRETE_ACTIONS[int(action)]
assert len(action) == 2, "Invalid action {}".format(action)
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
reverse = False
hand_brake = False
if self.config["verbose"]:
print(
"steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
self.client.send_control(
steer=steer, throttle=throttle, brake=brake, hand_brake=hand_brake,
reverse=reverse)
# Process observations
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
reward = self.calculate_reward(py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
done = (self.num_steps > self.scenario["max_steps"] or
py_measurements["next_command"] == "REACH_GOAL" or
(self.config["early_terminate_on_collision"] and
check_collision(py_measurements)))
py_measurements["done"] = done
self.prev_measurement = py_measurements
self.num_steps += 1
image = self.preprocess_image(image)
return (
self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def preprocess_image(self, image):
if self.config["use_depth_camera"]:
assert self.config["use_depth_camera"]
data = (image.data - 0.5) * 2
data = data.reshape(
self.config["render_y_res"], self.config["render_x_res"], 1)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = np.expand_dims(data, 2)
else:
data = image.data.reshape(
self.config["render_y_res"], self.config["render_x_res"], 3)
data = cv2.resize(
data, (self.config["x_res"], self.config["y_res"]),
interpolation=cv2.INTER_AREA)
data = (data.astype(np.float32) - 128) / 128
return data
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
observation = None
if self.config["use_depth_camera"]:
camera_name = "CameraDepth"
else:
camera_name = "CameraRGB"
for name, image in sensor_data.items():
if name == camera_name:
observation = image
self.frame_image = observation
cur = measurements.player_measurements
if self.config["enable_planner"]:
next_command = COMMANDS_ENUM[
self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y,
GROUND_Z],
[cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z],
[self.end_pos.location.x, self.end_pos.location.y,
GROUND_Z],
[self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z])
]
else:
next_command = "LANE_FOLLOW"
if next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
elif self.config["enable_planner"]:
distance_to_goal = self.planner.get_shortest_path_distance(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z],
[self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z]) / 100
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(np.linalg.norm(
[cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y]) / 100)
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": cur.forward_speed,
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"intersection_offroad": cur.intersection_offroad,
"intersection_otherlane": cur.intersection_otherlane,
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command": next_command,
}
assert observation is not None, sensor_data
return observation, py_measurements
def calculate_reward(self, current_measurement):
"""
Calculate the reward based on the effect of the action taken using the previous and the current measurements
:param current_measurement: The measurement obtained from the Carla engine after executing the current action
:return: The scalar reward
"""
reward = 0.0
cur_dist = current_measurement["distance_to_goal"]
prev_dist = self.prev_measurement["distance_to_goal"]
if self.config["verbose"]:
print("Cur dist {}, prev dist {}".format(cur_dist, prev_dist))
# Distance travelled toward the goal in m
reward += np.clip(prev_dist - cur_dist, -10.0, 10.0)
# Change in speed (km/hr)
reward += 0.05 * (current_measurement["forward_speed"] - self.prev_measurement["forward_speed"])
# New collision damage
reward -= .00002 * (
current_measurement["collision_vehicles"] + current_measurement["collision_pedestrians"] +
current_measurement["collision_other"] - self.prev_measurement["collision_vehicles"] -
self.prev_measurement["collision_pedestrians"] - self.prev_measurement["collision_other"])
# New sidewalk intersection
reward -= 2 * (
current_measurement["intersection_offroad"] - self.prev_measurement["intersection_offroad"])
# New opposite lane intersection
reward -= 2 * (
current_measurement["intersection_otherlane"] - self.prev_measurement["intersection_otherlane"])
return reward
class CarlaEnv(gym.Env):
def __init__(self, config=ENV_CONFIG):
self.config = config
self.city = self.config["server_map"].split("/")[-1]
if self.config["enable_planner"]:
self.planner = Planner(self.city)
self.action_space = Discrete(len(DISCRETE_ACTIONS))
# RGB Camera
self.frame_shape = (config["y_res"], config["x_res"])
image_space = Box(
0, 1, shape=(
config["y_res"], config["x_res"],
FRAME_DEPTH * config["framestack"]), dtype=np.float32)
self.observation_space = image_space
# TODO(ekl) this isn't really a proper gym spec
self._spec = lambda: None
self._spec.id = "Carla-v0"
self.server_port = None
self.server_process = None
self.client = None
self.num_steps = 0
self.total_reward = 0
self.prev_measurement = None
self.prev_image = None
self.episode_id = None
self.measurements_file = None
self.weather = None
self.scenario = None
self.start_pos = None
self.end_pos = None
self.start_coord = None
self.end_coord = None
self.last_obs = None
self.last_full_obs = None
self.framestack = [None] * config["framestack"]
self.framestack_index = 0
self.running_restarts = 0
self.on_step = None
self.on_next = None
self.photo_index = 1
self.episode_index = 0
def init_server(self):
print("Initializing new Carla server...")
# Create a new server process and start the client.
self.server_port = random.randint(10000, 60000)
self.server_process = subprocess.Popen(
[self.config["server_binary"], self.config["server_map"],
"-windowed",
"-ResX={}".format(self.config["render_x_res"]),
"-ResY={}".format(self.config["render_y_res"]),
"-fps={}".format(self.config["fps"]),
"-carla-server",
"-carla-world-port={}".format(self.server_port)],
preexec_fn=os.setsid, stdout=open(os.devnull, "w"))
live_carla_processes.add(os.getpgid(self.server_process.pid))
for i in range(RETRIES_ON_ERROR):
try:
self.client = CarlaClient("localhost", self.server_port)
return self.client.connect()
except Exception as e:
print("Error connecting: {}, attempt {}".format(e, i))
time.sleep(2)
def clear_server_state(self):
print("Clearing Carla server state")
try:
if self.client:
self.client.disconnect()
self.client = None
except Exception as e:
print("Error disconnecting client: {}".format(e))
pass
if self.server_process:
pgid = os.getpgid(self.server_process.pid)
os.killpg(pgid, signal.SIGKILL)
live_carla_processes.remove(pgid)
self.server_port = None
self.server_process = None
def __del__(self):
self.clear_server_state()
def reset(self):
if self.on_next is not None:
self.on_next()
error = None
self.running_restarts += 1
for _ in range(RETRIES_ON_ERROR):
try:
# Force a full reset of Carla after some number of restarts
if self.running_restarts > self.config["server_restart_interval"]:
print("Shutting down carla server...")
self.running_restarts = 0
self.clear_server_state()
# If server down, initialize
if not self.server_process:
self.init_server()
# Run episode reset
return self._reset()
except Exception as e:
print("Error during reset: {}".format(traceback.format_exc()))
self.clear_server_state()
error = e
time.sleep(5)
raise error
def _build_camera(self, name, post):
camera = Camera(name, PostProcessing=post)
camera.set_image_size(
self.config["render_x_res"], self.config["render_y_res"])
camera.set_position(x=2.4, y=0, z=0.8)
camera.set_rotation(pitch=-40, roll=0, yaw=0)
# camera.FOV = 100
return camera
def _reset(self):
self.num_steps = 0
self.total_reward = 0
self.episode_index += 1
self.photo_index = 1
self.prev_measurement = None
self.prev_image = None
self.episode_id = datetime.today().strftime("%Y-%m-%d_%H-%M-%S_%f")
self.measurements_file = None
# Create a CarlaSettings object. This object is a wrapper around
# the CarlaSettings.ini file. Here we set the configuration we
# want for the new episode.
settings = CarlaSettings()
self.scenario = random.choice(self.config["scenarios"])
assert self.scenario["city"] == self.city, (self.scenario, self.city)
self.weather = random.choice(self.scenario["weather_distribution"])
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=False,
NumberOfVehicles=self.scenario["num_vehicles"],
NumberOfPedestrians=self.scenario["num_pedestrians"],
WeatherId=self.weather,
QualityLevel=self.config["quality"])
settings.randomize_seeds()
# Add the cameras
if self.config["save_images_rgb"]:
settings.add_sensor(self._build_camera(name="CameraRGB", post="SceneFinal"))
settings.add_sensor(self._build_camera(name="CameraDepth", post="Depth"))
settings.add_sensor(self._build_camera(name="CameraClass", post="SemanticSegmentation"))
# Setup start and end positions
scene = self.client.load_settings(settings)
positions = scene.player_start_spots
self.start_pos = positions[self.scenario["start_pos_id"]]
self.end_pos = positions[self.scenario["end_pos_id"]]
self.start_coord = [
self.start_pos.location.x // 100, self.start_pos.location.y // 100]
self.end_coord = [
self.end_pos.location.x // 100, self.end_pos.location.y // 100]
print(
"Start pos {} ({}), end {} ({})".format(
self.scenario["start_pos_id"], self.start_coord,
self.scenario["end_pos_id"], self.end_coord))
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print("Starting new episode...")
self.client.start_episode(self.scenario["start_pos_id"])
image, py_measurements = self._read_observation()
py_measurements["control"] = {
"throttle_brake": 0,
"steer": 0,
"throttle": 0,
"brake": 0,
"reverse": False,
"hand_brake": False,
}
self.prev_measurement = py_measurements
return self.encode_obs(self.preprocess_image(image), py_measurements)
def encode_obs(self, obs, py_measurements):
new_obs = obs
num_frames = int(self.config["framestack"])
if num_frames > 1:
# Spread out to number of frames
frame_array = [obs] * num_frames
for frame_index in range(1, num_frames):
index = (self.framestack_index - frame_index) % num_frames
if self.framestack[index] is not None:
frame_array[frame_index] = self.framestack[index]
# Concatenate into a single np array
new_obs = np.concatenate(frame_array, axis=2)
# Store frame
self.framestack[self.framestack_index] = obs
self.framestack_index = (self.framestack_index + 1) % num_frames
self.last_obs = obs
# Return
return new_obs
def step(self, action):
try:
obs = self._step(action)
self.last_full_obs = obs
return obs
except Exception:
print(
"Error during step, terminating episode early",
traceback.format_exc())
self.clear_server_state()
return (self.last_full_obs, 0.0, True, {})
def _step(self, action):
action = DISCRETE_ACTIONS[int(action)]
assert len(action) == 2, "Invalid action {}".format(action)
if self.config["squash_action_logits"]:
forward = 2 * float(sigmoid(action[0]) - 0.5)
throttle = float(np.clip(forward, 0, 1))
brake = float(np.abs(np.clip(forward, -1, 0)))
steer = 2 * float(sigmoid(action[1]) - 0.5)
else:
throttle = float(np.clip(action[0], 0, 1))
brake = float(np.abs(np.clip(action[0], -1, 0)))
steer = float(np.clip(action[1], -1, 1))
reverse = False
hand_brake = False
if self.config["verbose"]:
print(
"steer", steer, "throttle", throttle, "brake", brake,
"reverse", reverse)
self.client.send_control(
steer=steer, throttle=throttle, brake=brake, hand_brake=hand_brake,
reverse=reverse)
# Process observations
image, py_measurements = self._read_observation()
if self.config["verbose"]:
print("Next command", py_measurements["next_command"])
if type(action) is np.ndarray:
py_measurements["action"] = [float(a) for a in action]
else:
py_measurements["action"] = action
py_measurements["control"] = {
"throttle_brake": action[0],
"steer": steer,
"throttle": throttle,
"brake": brake,
"reverse": reverse,
"hand_brake": hand_brake,
}
# Done?
finished = self.num_steps > self.scenario["max_steps"]
# py_measurements["next_command"] == "REACH_GOAL"
failed = TERM.compute_termination(self, py_measurements, self.prev_measurement)
done = finished or failed
py_measurements["finished"] = finished
py_measurements["failed"] = failed
py_measurements["done"] = done
# Determine Reward
reward = compute_reward(self, self.prev_measurement, py_measurements)
self.total_reward += reward
py_measurements["reward"] = reward
py_measurements["total_reward"] = self.total_reward
self.prev_measurement = py_measurements
# Callback
if self.on_step is not None:
self.on_step(py_measurements)
# Write out measurements to file
if self.config["carla_out_path"]:
# Ensure measurements dir exists
measurements_dir = os.path.join(self.config["carla_out_path"], self.config["measurements_subdir"])
if not os.path.exists(measurements_dir):
os.mkdir(measurements_dir)
if not self.measurements_file:
self.measurements_file = open(
os.path.join(
measurements_dir,
"m_{}.json".format(self.episode_id)),
"w")
self.measurements_file.write(json.dumps(py_measurements))
self.measurements_file.write("\n")
if done:
self.measurements_file.close()
self.measurements_file = None
if self.config["convert_images_to_video"]:
self.images_to_video(camera_name="RGB")
self.images_to_video(camera_name="Depth")
self.images_to_video(camera_name="Class")
self.num_steps += 1
image = self.preprocess_image(image)
return (
self.encode_obs(image, py_measurements), reward, done,
py_measurements)
def preprocess_image(self, image):
return image
def _fuse_observations(self, depth, clazz, speed):
base_shape = (self.config["render_y_res"], self.config["render_x_res"])
new_shape = (self.config["y_res"], self.config["x_res"])
# Reduce class
clazz = reduce_classifications(clazz)
# Do we need to resize?
if base_shape[0] is not new_shape[0] and base_shape[1] is not new_shape[1]:
depth_reshape = depth.reshape(*depth.shape)
depth = cv2.resize(depth_reshape, (new_shape[1], new_shape[0]))
clazz = resize_classifications(clazz, new_shape)
# Fuse with depth!
obs = fuse_with_depth(clazz, depth, extra_layers=1)
# Fuse with speed!
obs[:, :, KEEP_CLASSIFICATIONS] = speed
return obs, clazz
def _read_observation(self):
# Read the data produced by the server this frame.
measurements, sensor_data = self.client.read_data()
cur = measurements.player_measurements
# Print some of the measurements.
if self.config["verbose"]:
print_measurements(measurements)
# Fuse the observation data to create a single observation
observation, clazzes = self._fuse_observations(
sensor_data['CameraDepth'].data,
sensor_data['CameraClass'].data,
cur.forward_speed)
if self.config["enable_planner"]:
next_command = COMMANDS_ENUM[
self.planner.get_next_command(
[cur.transform.location.x, cur.transform.location.y,
GROUND_Z],
[cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z],
[self.end_pos.location.x, self.end_pos.location.y,
GROUND_Z],
[self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z])
]
else:
next_command = "LANE_FOLLOW"
if next_command == "REACH_GOAL":
distance_to_goal = 0.0 # avoids crash in planner
elif self.config["enable_planner"]:
distance_to_goal = self.planner.get_shortest_path_distance(
[cur.transform.location.x, cur.transform.location.y, GROUND_Z],
[cur.transform.orientation.x, cur.transform.orientation.y,
GROUND_Z],
[self.end_pos.location.x, self.end_pos.location.y, GROUND_Z],
[self.end_pos.orientation.x, self.end_pos.orientation.y,
GROUND_Z]) / 100
else:
distance_to_goal = -1
distance_to_goal_euclidean = float(np.linalg.norm(
[cur.transform.location.x - self.end_pos.location.x,
cur.transform.location.y - self.end_pos.location.y]) / 100)
py_measurements = {
"episode_id": self.episode_id,
"step": self.num_steps,
"x": cur.transform.location.x,
"y": cur.transform.location.y,
"x_orient": cur.transform.orientation.x,
"y_orient": cur.transform.orientation.y,
"forward_speed": cur.forward_speed,
"distance_to_goal": distance_to_goal,
"distance_to_goal_euclidean": distance_to_goal_euclidean,
"collision_vehicles": cur.collision_vehicles,
"collision_pedestrians": cur.collision_pedestrians,
"collision_other": cur.collision_other,
"intersection_offroad": cur.intersection_offroad,
"intersection_otherlane": cur.intersection_otherlane,
"weather": self.weather,
"map": self.config["server_map"],
"start_coord": self.start_coord,
"end_coord": self.end_coord,
"current_scenario": self.scenario,
"x_res": self.config["x_res"],
"y_res": self.config["y_res"],
"num_vehicles": self.scenario["num_vehicles"],
"num_pedestrians": self.scenario["num_pedestrians"],
"max_steps": self.scenario["max_steps"],
"next_command": next_command,
"applied_penalty": False,
"total_reward": self.total_reward,
}
if self.config["carla_out_path"] \
and self.num_steps % self.config["save_image_frequency"] == 0\
and self.num_steps > 15:
self.take_photo(
sensor_data=sensor_data,
class_data=clazzes,
fused_data=observation
)
assert observation is not None, sensor_data
return observation, py_measurements
def images_dir(self):
dir = os.path.join(self.config["carla_out_path"], "images")
if not os.path.exists(dir):
os.mkdir(dir)
return dir
def episode_dir(self):
episode_dir = os.path.join(self.images_dir(), "episode_" + str(self.episode_index))
if not os.path.exists(episode_dir):
os.mkdir(episode_dir)
return episode_dir
def take_photo(self, sensor_data, class_data, fused_data):
# Get the proper locations\
episode_dir = self.episode_dir()
photo_index = self.photo_index
name_prefix = "episode_{:>04}_step_{:>04}".format(self.episode_index, photo_index)
self.photo_index += 1
# Save the image
if self.config["save_images_rgb"]:
rgb_image = sensor_data["CameraRGB"]
image_path = os.path.join(episode_dir, name_prefix + "_rgb.png")
scipy.misc.imsave(image_path, rgb_image.data)
# Save the classes
if self.config["save_images_class"]:
classes_path = os.path.join(episode_dir, name_prefix + "_class.png")
scipy.misc.imsave(classes_path, class_data.data)
# Save the class images
if self.config["save_images_fusion"]:
fused_images = fused_to_rgb(fused_data)
fused_path = os.path.join(episode_dir, name_prefix + "_fused.png")
scipy.misc.imsave(fused_path, fused_images.data)
def images_to_video(self, camera_name):
# Video directory
videos_dir = os.path.join(self.config["carla_out_path"], "Videos" + camera_name)
if not os.path.exists(videos_dir):
os.makedirs(videos_dir)
# Build command
video_fps = self.config["fps"] / self.config["saveimage_frequency"]
ffmpeg_cmd = (
"ffmpeg -loglevel -8 -r 10 -f image2 -s {x_res}x{y_res} "
"-start_number 0 -i "
"{img}_%04d.jpg -vcodec libx264 {vid}.mp4 && rm -f {img}_*.jpg "
).format(
x_res=self.config["render_x_res"],
y_res=self.config["render_y_res"],
vid=os.path.join(videos_dir, self.episode_id),
img=os.path.join(self.config["carla_out_path"], "Camera" + camera_name, self.episode_id))
# Execute command
print("Executing ffmpeg command: ", ffmpeg_cmd)
try:
subprocess.call(ffmpeg_cmd, shell=True, timeout=60)
except Exception as ex:
print("FFMPEG EXPIRED")
print(ex)