492 lines
18 KiB
Python
492 lines
18 KiB
Python
#!/usr/bin/env python
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# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
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# Barcelona (UAB).
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#
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# This work is licensed under the terms of the MIT license.
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# For a copy, see <https://opensource.org/licenses/MIT>.
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"""
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Check raycast sensors determinism for CARLA
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This script spawn all the raycast sensors in a simple scenario and check if their
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output are deterministic.
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"""
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import glob
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import os
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import sys
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import argparse
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import time
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import filecmp
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import shutil
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from queue import Queue
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from queue import Empty
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import numpy as np
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try:
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sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
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sys.version_info.major,
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sys.version_info.minor,
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'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
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except IndexError:
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pass
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import carla
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class Scenario():
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def __init__(self, client, world, save_snapshots_mode=False):
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self.world = world
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self.client = client
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self.actor_list = []
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self.init_timestamp = []
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self.active = False
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self.prefix = ""
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self.save_snapshots_mode = save_snapshots_mode
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self.snapshots = []
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self.sensor_list = []
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self.sensor_queue = Queue()
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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self.prefix = prefix
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self.actor_list = []
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self.active = True
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self.snapshots = []
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self.sensor_list = []
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self.sensor_queue = Queue()
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self.reload_world(settings, spectator_tr)
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# Init timestamp
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snapshot = self.world.get_snapshot()
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self.init_timestamp = {'frame0' : snapshot.frame, 'time0' : snapshot.timestamp.elapsed_seconds}
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def add_actor(self, actor, actor_name="Actor"):
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actor_idx = len(self.actor_list)
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name = str(actor_idx) + "_" + actor_name
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self.actor_list.append((name, actor))
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if self.save_snapshots_mode:
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self.snapshots.append(np.empty((0,11), float))
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def wait(self, frames=100):
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for _i in range(0, frames):
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self.world.tick()
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if self.active:
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for _s in self.sensor_list:
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self.sensor_queue.get(True, 1.0)
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def clear_scene(self):
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for sensor in self.sensor_list:
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sensor[1].destroy()
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for actor in self.actor_list:
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actor[1].destroy()
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self.active = False
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def reload_world(self, settings = None, spectator_tr = None):
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self.client.reload_world()
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if settings is not None:
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self.world.apply_settings(settings)
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if spectator_tr is not None:
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self.reset_spectator(spectator_tr)
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def reset_spectator(self, spectator_tr):
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spectator = self.world.get_spectator()
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spectator.set_transform(spectator_tr)
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def save_snapshot(self, actor):
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snapshot = self.world.get_snapshot()
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actor_snapshot = np.array([
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float(snapshot.frame - self.init_timestamp['frame0']), \
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snapshot.timestamp.elapsed_seconds - self.init_timestamp['time0'], \
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actor.get_location().x, actor.get_location().y, actor.get_location().z, \
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actor.get_velocity().x, actor.get_velocity().y, actor.get_velocity().z, \
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actor.get_angular_velocity().x, actor.get_angular_velocity().y, actor.get_angular_velocity().z])
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return actor_snapshot
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def save_snapshots(self):
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if not self.save_snapshots_mode:
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return
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for i in range (0, len(self.actor_list)):
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self.snapshots[i] = np.vstack((self.snapshots[i], self.save_snapshot(self.actor_list[i][1])))
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def save_snapshots_to_disk(self):
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if not self.save_snapshots_mode:
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return
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for i, actor in enumerate(self.actor_list):
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np.savetxt(self.get_filename(actor[0]), self.snapshots[i])
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def get_filename_with_prefix(self, prefix, actor_id=None, frame=None):
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add_id = "" if actor_id is None else "_" + actor_id
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add_frame = "" if frame is None else ("_%04d") % frame
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return prefix + add_id + add_frame + ".out"
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def get_filename(self, actor_id=None, frame=None):
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return self.get_filename_with_prefix(self.prefix, actor_id, frame)
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def run_simulation(self, prefix, run_settings, spectator_tr, tics = 200):
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original_settings = self.world.get_settings()
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self.init_scene(prefix, run_settings, spectator_tr)
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t_start = time.perf_counter()
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for _i in range(0, tics):
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self.world.tick()
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self.sensor_syncronization()
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self.save_snapshots()
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t_end = time.perf_counter()
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self.world.apply_settings(original_settings)
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self.save_snapshots_to_disk()
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self.clear_scene()
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return t_end - t_start
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def add_sensor(self, sensor, sensor_type):
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sen_idx = len(self.sensor_list)
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if sensor_type == "LiDAR":
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name = str(sen_idx) + "_LiDAR"
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sensor.listen(lambda data : self.add_lidar_snapshot(data, name))
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elif sensor_type == "SemLiDAR":
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name = str(sen_idx) + "_SemLiDAR"
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sensor.listen(lambda data : self.add_semlidar_snapshot(data, name))
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elif sensor_type == "Radar":
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name = str(sen_idx) + "_Radar"
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sensor.listen(lambda data : self.add_radar_snapshot(data, name))
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self.sensor_list.append((name, sensor))
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def add_lidar_snapshot(self, lidar_data, name="LiDAR"):
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if not self.active:
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return
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points = np.frombuffer(lidar_data.raw_data, dtype=np.dtype('f4'))
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points = np.reshape(points, (int(points.shape[0] / 4), 4))
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frame = lidar_data.frame - self.init_timestamp['frame0']
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np.savetxt(self.get_filename(name, frame), points)
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self.sensor_queue.put((lidar_data.frame, name))
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def add_semlidar_snapshot(self, lidar_data, name="SemLiDAR"):
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if not self.active:
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return
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data = np.frombuffer(lidar_data.raw_data, dtype=np.dtype([
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('x', np.float32), ('y', np.float32), ('z', np.float32),
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('CosAngle', np.float32), ('ObjIdx', np.uint32), ('ObjTag', np.uint32)]))
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points = np.array([data['x'], data['y'], data['z'], data['CosAngle'], data['ObjTag']]).T
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frame = lidar_data.frame - self.init_timestamp['frame0']
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np.savetxt(self.get_filename(name, frame), points)
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self.sensor_queue.put((lidar_data.frame, name))
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def add_radar_snapshot(self, radar_data, name="Radar"):
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if not self.active:
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return
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points = np.frombuffer(radar_data.raw_data, dtype=np.dtype('f4'))
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points = np.reshape(points, (int(points.shape[0] / 4), 4))
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frame = radar_data.frame - self.init_timestamp['frame0']
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np.savetxt(self.get_filename(name, frame), points)
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self.sensor_queue.put((radar_data.frame, name))
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def sensor_syncronization(self):
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# Sensor Syncronization
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w_frame = self.world.get_snapshot().frame
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for sensor in self.sensor_list:
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s_frame = self.sensor_queue.get(True, 1.0)[0]
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if w_frame != s_frame:
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print("Error!!! frames are not equal for %s: %d %d" % (sensor[0], w_frame, s_frame))
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class SpawnLidarNoDropff(Scenario):
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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super().init_scene(prefix, settings, spectator_tr)
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blueprint_library = self.world.get_blueprint_library()
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vehicle00_tr = carla.Transform(carla.Location(140, -205, 0.1), carla.Rotation(yaw=181.5))
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vehicle00 = self.world.spawn_actor(blueprint_library.filter("tt")[0], vehicle00_tr)
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vehicle00.set_target_velocity(carla.Vector3D(-25, 0, 0))
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lidar_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast')
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lidar_bp.set_attribute('dropoff_general_rate', '0.0')
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lidar_bp.set_attribute('noise_seed', '43233')
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lidar_tr = carla.Transform(carla.Location(z=2))
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lidar = self.world.spawn_actor(lidar_bp, lidar_tr, attach_to=vehicle00)
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self.add_sensor(lidar, "LiDAR")
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self.add_actor(vehicle00, "Car")
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self.wait(1)
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class SpawnSemanticLidar(Scenario):
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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super().init_scene(prefix, settings, spectator_tr)
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blueprint_library = self.world.get_blueprint_library()
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vehicle00_tr = carla.Transform(carla.Location(140, -205, 0.1), carla.Rotation(yaw=181.5))
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vehicle00 = self.world.spawn_actor(blueprint_library.filter("tt")[0], vehicle00_tr)
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vehicle00.set_target_velocity(carla.Vector3D(-25, 0, 0))
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lidar_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast_semantic')
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lidar_tr = carla.Transform(carla.Location(z=2))
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lidar = self.world.spawn_actor(lidar_bp, lidar_tr, attach_to=vehicle00)
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self.add_sensor(lidar, "SemLiDAR")
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self.add_actor(vehicle00, "Car")
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self.wait(1)
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class SpawnRadar(Scenario):
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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super().init_scene(prefix, settings, spectator_tr)
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blueprint_library = self.world.get_blueprint_library()
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vehicle00_tr = carla.Transform(carla.Location(140, -205, 0.1), carla.Rotation(yaw=181.5))
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vehicle00 = self.world.spawn_actor(blueprint_library.filter("tt")[0], vehicle00_tr)
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vehicle00.set_target_velocity(carla.Vector3D(-25, 0, 0))
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radar_bp = self.world.get_blueprint_library().find('sensor.other.radar')
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radar_bp.set_attribute('noise_seed', '54283')
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radar_tr = carla.Transform(carla.Location(z=2))
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radar = self.world.spawn_actor(radar_bp, radar_tr, attach_to=vehicle00)
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self.add_sensor(radar, "Radar")
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self.add_actor(vehicle00, "Car")
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self.wait(1)
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class SpawnLidarWithDropff(Scenario):
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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super().init_scene(prefix, settings, spectator_tr)
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blueprint_library = self.world.get_blueprint_library()
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vehicle00_tr = carla.Transform(carla.Location(140, -205, 0.1), carla.Rotation(yaw=181.5))
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vehicle00 = self.world.spawn_actor(blueprint_library.filter("tt")[0], vehicle00_tr)
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vehicle00.set_target_velocity(carla.Vector3D(-25, 0, 0))
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lidar_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast')
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lidar_bp.set_attribute('channels', '64')
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lidar_bp.set_attribute('noise_seed', '249013')
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lidar_tr = carla.Transform(carla.Location(z=2))
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lidar = self.world.spawn_actor(lidar_bp, lidar_tr, attach_to=vehicle00)
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self.add_sensor(lidar, "LiDAR")
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self.add_actor(vehicle00, "Car")
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self.wait(1)
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class SpawnAllRaycastSensors(Scenario):
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def init_scene(self, prefix, settings = None, spectator_tr = None):
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super().init_scene(prefix, settings, spectator_tr)
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blueprint_library = self.world.get_blueprint_library()
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vehicle00_tr = carla.Transform(carla.Location(140, -205, 0.1), carla.Rotation(yaw=181.5))
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vehicle00 = self.world.spawn_actor(blueprint_library.filter("tt")[0], vehicle00_tr)
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vehicle00.set_target_velocity(carla.Vector3D(-25, 0, 0))
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vehicle01_tr = carla.Transform(carla.Location(50, -200, 0.1), carla.Rotation(yaw=1.5))
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vehicle01 = self.world.spawn_actor(blueprint_library.filter("lincoln")[0], vehicle01_tr)
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vehicle01.set_target_velocity(carla.Vector3D(25, 0, 0))
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radar_bp = self.world.get_blueprint_library().find('sensor.other.radar')
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radar_bp.set_attribute('noise_seed', '54283')
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radar_tr = carla.Transform(carla.Location(z=2))
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radar = self.world.spawn_actor(radar_bp, radar_tr, attach_to=vehicle00)
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lidar01_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast')
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lidar01_bp.set_attribute('noise_seed', '12134')
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lidar01_tr = carla.Transform(carla.Location(x=1, z=2))
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lidar01 = self.world.spawn_actor(lidar01_bp, lidar01_tr, attach_to=vehicle00)
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lidar02_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast_semantic')
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lidar02_tr = carla.Transform(carla.Location(x=1, z=2))
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lidar02 = self.world.spawn_actor(lidar02_bp, lidar02_tr, attach_to=vehicle01)
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lidar03_bp = self.world.get_blueprint_library().find('sensor.lidar.ray_cast')
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lidar03_bp.set_attribute('noise_seed', '23135')
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lidar03_tr = carla.Transform(carla.Location(z=2))
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lidar03 = self.world.spawn_actor(lidar03_bp, lidar03_tr, attach_to=vehicle01)
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self.add_sensor(radar, "Radar")
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self.add_sensor(lidar01, "LiDAR")
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self.add_sensor(lidar02, "SemLiDAR")
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self.add_sensor(lidar03, "LiDAR")
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self.add_actor(vehicle00, "Car")
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self.add_actor(vehicle01, "Car")
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self.wait(1)
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class SensorScenarioTester():
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def __init__(self, scene, output_path):
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self.scene = scene
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self.world = self.scene.world
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self.client = self.scene.client
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self.scenario_name = self.scene.__class__.__name__
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self.output_path = output_path
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def compare_files(self, file_i, file_j):
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# First, we check if the files are exactly equal,
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# if they are the simulations are equivalent
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check_ij = filecmp.cmp(file_i, file_j)
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if check_ij:
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return True
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# If not, if we have different number of points
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# the simulations are not equivalent
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data_i = np.loadtxt(file_i)
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data_j = np.loadtxt(file_j)
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if data_i.shape != data_j.shape:
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return False
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# If they have the same number of points but there is
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# a small diference, the simulations could be equivalent
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# differing only in floaring-point arithmetic errors
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max_error = np.amax(np.abs(data_i-data_j))
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return max_error < 0.01
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def check_simulations(self, rep_prefixes, sim_tics):
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repetitions = len(rep_prefixes)
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mat_check = np.zeros((repetitions, repetitions), int)
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for i in range(0, repetitions):
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mat_check[i][i] = 1
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for j in range(0, i):
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sim_check = True
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for f_idx in range(1, sim_tics):
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for sensor in self.scene.sensor_list:
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file_i = self.scene.get_filename_with_prefix(rep_prefixes[i], sensor[0], f_idx)
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file_j = self.scene.get_filename_with_prefix(rep_prefixes[j], sensor[0], f_idx)
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check_ij = self.compare_files(file_i, file_j)
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sim_check = sim_check and check_ij
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mat_check[i][j] = int(sim_check)
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mat_check[j][i] = int(sim_check)
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determinism = np.sum(mat_check,axis=1)
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determinism_set = list(set(determinism))
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determinism_set.sort(reverse=True)
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return determinism_set
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def test_scenario(self, repetitions = 1, sim_tics = 100):
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output_str = "Testing Determinism in %s -> " % (self.scenario_name)
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prefix = self.output_path + self.scenario_name
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config_settings = self.world.get_settings()
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config_settings.synchronous_mode = True
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config_settings.fixed_delta_seconds = 0.05
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spectator_tr = carla.Transform(carla.Location(160, -205, 10), carla.Rotation(yaw=180))
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sim_prefixes = []
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t_comp = 0
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for i in range(0, repetitions):
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prefix_rep = prefix + "_rep_" + ("%03d" % i)
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t_comp += self.scene.run_simulation(prefix_rep, config_settings, spectator_tr, tics=sim_tics)
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sim_prefixes.append(prefix_rep)
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determ_repet = self.check_simulations(sim_prefixes, sim_tics)
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output_str += "Deterministic Repetitions: %r / %2d" % (determ_repet, repetitions)
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output_str += " -> Comp. FPS: %.0f" % ((repetitions*sim_tics)/t_comp)
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if determ_repet[0] != repetitions:
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print("Error!!! Scenario %s is not deterministic: %d / %d" % (self.scenario_name, determ_repet[0], repetitions))
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return output_str
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def main(arg):
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"""Main function of the script"""
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client = carla.Client(arg.host, arg.port)
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client.set_timeout(30.0)
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world = client.get_world()
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pre_settings = world.get_settings()
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world = client.load_world("Town03")
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try:
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# Setting output temporal folder
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output_path = os.path.dirname(os.path.realpath(__file__))
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output_path = os.path.join(output_path, "_sensors") + os.path.sep
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if not os.path.exists(output_path):
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os.mkdir(output_path)
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test_list = [
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SensorScenarioTester(SpawnAllRaycastSensors(client, world), output_path),
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SensorScenarioTester(SpawnLidarNoDropff(client, world), output_path),
|
|
SensorScenarioTester(SpawnLidarWithDropff(client, world), output_path),
|
|
SensorScenarioTester(SpawnSemanticLidar(client, world), output_path),
|
|
SensorScenarioTester(SpawnRadar(client, world), output_path)
|
|
]
|
|
|
|
repetitions = 10
|
|
for item in test_list:
|
|
print("--------------------------------------------------------------")
|
|
out = item.test_scenario(repetitions)
|
|
print(out)
|
|
|
|
print("--------------------------------------------------------------")
|
|
|
|
# Remove all the output files
|
|
#shutil.rmtree(path)
|
|
|
|
finally:
|
|
world.apply_settings(pre_settings)
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
argparser = argparse.ArgumentParser(
|
|
description=__doc__)
|
|
argparser.add_argument(
|
|
'--host',
|
|
metavar='H',
|
|
default='localhost',
|
|
help='IP of the host CARLA Simulator (default: localhost)')
|
|
argparser.add_argument(
|
|
'-p', '--port',
|
|
metavar='P',
|
|
default=2000,
|
|
type=int,
|
|
help='TCP port of CARLA Simulator (default: 2000)')
|
|
argparser.add_argument(
|
|
'--filter',
|
|
metavar='PATTERN',
|
|
default='model3',
|
|
help='actor filter (default: "vehicle.*")')
|
|
args = argparser.parse_args()
|
|
|
|
try:
|
|
main(args)
|
|
except KeyboardInterrupt:
|
|
print(' - Exited by user.')
|
|
|