def run_test(test, args): log_test(SEP1, 'Instantiating %s', test.name) try: test.instantiate(args) except Exception as exception: logging.error('exception instantiating %r: %s', test.name, exception) return False log_test(RUN, test.name) while True: try: timer = StopWatch() result = test.run() timer.stop() break except TCPConnectionError as error: logging.error(error) time.sleep(1) except Exception as exception: timer.stop() logging.exception('exception: %s', exception) result = False break log_test(OK if result else FAILED, '%s (%d ms)', test.name, timer.milliseconds()) return result
class FPSWatch(object): def __init__(self): self.stop_watch = StopWatch() self.sum = 0.0 self.count = 0 self.max = 0.0 self.min = float("inf") def annotate(self): self.stop_watch.stop() fps = 1.0 / self.stop_watch.seconds() self.sum += fps self.count += 1 self.max = max(self.max, fps) self.min = min(self.min, fps) self.stop_watch.restart() def __str__(self): return TEXT.format(count=self.count, avg=self.sum / self.count, max=self.max, min=self.min)
class FPSWatch(object): def __init__(self): self.stop_watch = StopWatch() self.sum = 0.0 self.count = 0 self.max = 0.0 self.min = float("inf") def annotate(self): self.stop_watch.stop() fps = 1.0 / self.stop_watch.seconds() self.sum += fps self.count += 1 self.max = max(self.max, fps) self.min = min(self.min, fps) self.stop_watch.restart() def __str__(self): return TEXT.format( count=self.count, avg=self.sum/self.count, max=self.max, min=self.min)
def run_test(test, args): log_test(SEP1, 'Instantiating %s', test.name) try: test.instantiate(args) except Exception as exception: logging.error('exception instantiating %r: %s', test.name, exception) return False log_test(RUN, test.name) try: timer = StopWatch() result = test.run() timer.stop() except Exception as exception: timer.stop() logging.error('exception: %s', exception) result = False log_test(OK if result else FAILED, '%s (%d ms)', test.name, timer.milliseconds()) return result
def run_test(test, args): log_test(SEP1, 'Instantiating %s', test.name) try: test.instantiate(args) except Exception as exception: logging.error('exception instantiating %r: %s', test.name, exception) return False log_test(RUN, test.name) while True: try: timer = StopWatch() result = test.run() timer.stop() break except TCPConnectionError as error: logging.error(error) time.sleep(1) except Exception as exception: timer.stop() logging.exception('exception: %s', exception) result = False break log_test(OK if result else FAILED, '%s (%d ms)', test.name, timer.milliseconds()) return result
def run_carla_client(host, port, far): # Here we will run a single episode with 300 frames. number_of_frames = 30 frame_step = 10 # Save one image every 100 frames image_size = [800, 600] camera_local_pos = [0.3, 0.0, 1.3] # [X, Y, Z] camera_local_rotation = [0, 0, 0] # [pitch(Y), yaw(Z), roll(X)] fov = 70 autopilot = False control = VehicleControl() for start_i in range(150): output_folder = '/home2/mariap/Packages/CARLA_0.8.2/PythonClient/_out/pos_' + str( start_i) if not os.path.exists(output_folder): os.mkdir(output_folder) print("make " + str(output_folder)) # Connect with the server with make_carla_client(host, port) as client: print('CarlaClient connected') for start_i in range(150): output_folder = '/home2/mariap/Packages/CARLA_0.8.2/PythonClient/_out/pos_' + str( start_i) print(output_folder) # Here we load the settings. settings = CarlaSettings() settings.set(SynchronousMode=True, SendNonPlayerAgentsInfo=True, NumberOfVehicles=100, NumberOfPedestrians=500, WeatherId=random.choice([1, 3, 7, 8, 14])) settings.randomize_seeds() camera1 = Camera('CameraDepth', PostProcessing='Depth', FOV=fov) camera1.set_image_size(*image_size) camera1.set_position(*camera_local_pos) camera1.set_rotation(*camera_local_rotation) settings.add_sensor(camera1) camera2 = Camera('CameraRGB', PostProcessing='SceneFinal', FOV=fov) camera2.set_image_size(*image_size) camera2.set_position(*camera_local_pos) camera2.set_rotation(*camera_local_rotation) settings.add_sensor(camera2) camera3 = Camera('CameraSeg', PostProcessing='SemanticSegmentation') camera3.set_image_size(*image_size) camera3.set_position(*camera_local_pos) camera3.set_rotation(*camera_local_rotation) settings.add_sensor(camera3) client.load_settings(settings) # Start at location index id '0' client.start_episode(start_i) cameras_dict = {} pedestrians_dict = {} cars_dict = {} # Compute the camera transform matrix camera_to_car_transform = camera2.get_unreal_transform() # (Intrinsic) (3, 3) K Matrix K = np.identity(3) K[0, 2] = image_size[0] / 2.0 K[1, 2] = image_size[1] / 2.0 K[0, 0] = K[1, 1] = image_size[0] / (2.0 * np.tan(fov * np.pi / 360.0)) with open(output_folder + '/camera_intrinsics.p', 'w') as camfile: pickle.dump(K, camfile) # Iterate every frame in the episode except for the first one. for frame in range(1, number_of_frames): # Read the data produced by the server this frame. measurements, sensor_data = client.read_data() # Save one image every 'frame_step' frames if not frame % frame_step: for name, measurement in sensor_data.items(): filename = '{:s}/{:0>6d}'.format(name, frame) measurement.save_to_disk( os.path.join(output_folder, filename)) # Start transformations time mesure. timer = StopWatch() # RGB image [[[r,g,b],..[r,g,b]],..[[r,g,b],..[r,g,b]]] image_RGB = to_rgb_array(sensor_data['CameraRGB']) image_seg = np.tile( labels_to_array(sensor_data['CameraSeg']), (1, 1, 3)) # 2d to (camera) local 3d # We use the image_RGB to colorize each 3D point, this is optional. # "max_depth" is used to keep only the points that are near to the # camera, meaning 1.0 the farest points (sky) point_cloud = depth_to_local_point_cloud( sensor_data['CameraDepth'], image_RGB, max_depth=far) point_cloud_seg = depth_to_local_point_cloud( sensor_data['CameraDepth'], image_seg, max_depth=far) # (Camera) local 3d to world 3d. # Get the transform from the player protobuf transformation. world_transform = Transform( measurements.player_measurements.transform) # Compute the final transformation matrix. car_to_world_transform = world_transform * camera_to_car_transform # Car to World transformation given the 3D points and the # transformation matrix. point_cloud.apply_transform(car_to_world_transform) point_cloud_seg.apply_transform(car_to_world_transform) Rt = car_to_world_transform.matrix Rt_inv = car_to_world_transform.inverse( ).matrix # Rt_inv is the world to camera matrix !! #R_inv=world_transform.inverse().matrix cameras_dict[frame] = {} cameras_dict[frame]['inverse_rotation'] = Rt_inv[:] cameras_dict[frame]['rotation'] = Rt[:] cameras_dict[frame]['translation'] = Rt_inv[0:3, 3] cameras_dict[frame][ 'camera_to_car'] = camera_to_car_transform.matrix # Get non-player info vehicles = {} pedestrians = {} for agent in measurements.non_player_agents: # check if the agent is a vehicle. if agent.HasField('vehicle'): pos = agent.vehicle.transform.location pos_vector = np.array([[pos.x], [pos.y], [pos.z], [1.0]]) trnasformed_3d_pos = np.dot(Rt_inv, pos_vector) pos2d = np.dot(K, trnasformed_3d_pos[:3]) # Normalize the point norm_pos2d = np.array([ pos2d[0] / pos2d[2], pos2d[1] / pos2d[2], pos2d[2] ]) # Now, pos2d contains the [x, y, d] values of the image in pixels (where d is the depth) # You can use the depth to know the points that are in front of the camera (positive depth). x_2d = image_size[0] - norm_pos2d[0] y_2d = image_size[1] - norm_pos2d[1] vehicles[agent.id] = {} vehicles[agent.id]['transform'] = [ agent.vehicle.transform.location.x, agent.vehicle.transform.location.y, agent.vehicle.transform.location.z ] vehicles[agent.id][ 'bounding_box.transform'] = agent.vehicle.bounding_box.transform.location.z vehicles[agent.id][ 'yaw'] = agent.vehicle.transform.rotation.yaw vehicles[agent.id]['bounding_box'] = [ agent.vehicle.bounding_box.extent.x, agent.vehicle.bounding_box.extent.y, agent.vehicle.bounding_box.extent.z ] vehicle_transform = Transform( agent.vehicle.bounding_box.transform) pos = agent.vehicle.transform.location bbox3d = agent.vehicle.bounding_box.extent # Compute the 3D bounding boxes # f contains the 4 points that corresponds to the bottom f = np.array([[ pos.x + bbox3d.x, pos.y - bbox3d.y, pos.z - bbox3d.z + agent.vehicle.bounding_box.transform.location.z ], [ pos.x + bbox3d.x, pos.y + bbox3d.y, pos.z - bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x - bbox3d.x, pos.y + bbox3d.y, pos.z - bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x - bbox3d.x, pos.y - bbox3d.y, pos.z - bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x + bbox3d.x, pos.y - bbox3d.y, pos.z + bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x + bbox3d.x, pos.y + bbox3d.y, pos.z + bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x - bbox3d.x, pos.y + bbox3d.y, pos.z + bbox3d.z + agent.vehicle. bounding_box.transform.location.z ], [ pos.x - bbox3d.x, pos.y - bbox3d.y, pos.z + bbox3d.z + agent.vehicle. bounding_box.transform.location.z ]]) f_rotated = vehicle_transform.transform_points(f) f_2D_rotated = [] vehicles[ agent.id]['bounding_box_coord'] = f_rotated for i in range(f.shape[0]): point = np.array([[f_rotated[i, 0]], [f_rotated[i, 1]], [f_rotated[i, 2]], [1]]) transformed_2d_pos = np.dot( Rt_inv, point) # 3d Position in camera space pos2d = np.dot( K, transformed_2d_pos[:3] ) # Conversion to camera frustum space norm_pos2d = np.array([ pos2d[0] / pos2d[2], pos2d[1] / pos2d[2], pos2d[2] ]) #print([image_size[0] - (pos2d[0] / pos2d[2]), image_size[1] - (pos2d[1] / pos2d[2])]) f_2D_rotated.append( np.array([ image_size[0] - norm_pos2d[0], image_size[1] - norm_pos2d[1] ])) vehicles[ agent.id]['bounding_box_2D'] = f_2D_rotated elif agent.HasField('pedestrian'): pedestrians[agent.id] = {} pedestrians[agent.id]['transform'] = [ agent.pedestrian.transform.location.x, agent.pedestrian.transform.location.y, agent.pedestrian.transform.location.z ] pedestrians[agent.id][ 'yaw'] = agent.pedestrian.transform.rotation.yaw pedestrians[agent.id]['bounding_box'] = [ agent.pedestrian.bounding_box.extent.x, agent.pedestrian.bounding_box.extent.y, agent.pedestrian.bounding_box.extent.z ] vehicle_transform = Transform( agent.pedestrian.bounding_box.transform) pos = agent.pedestrian.transform.location bbox3d = agent.pedestrian.bounding_box.extent # Compute the 3D bounding boxes # f contains the 4 points that corresponds to the bottom f = np.array( [[pos.x + bbox3d.x, pos.y - bbox3d.y, pos.z], [pos.x + bbox3d.x, pos.y + bbox3d.y, pos.z], [pos.x - bbox3d.x, pos.y + bbox3d.y, pos.z], [pos.x - bbox3d.x, pos.y - bbox3d.y, pos.z], [ pos.x + bbox3d.x, pos.y - bbox3d.y, pos.z + bbox3d.z ], [ pos.x + bbox3d.x, pos.y + bbox3d.y, pos.z + bbox3d.z ], [ pos.x - bbox3d.x, pos.y + bbox3d.y, pos.z + bbox3d.z ], [ pos.x - bbox3d.x, pos.y - bbox3d.y, pos.z + bbox3d.z ]]) f_rotated = vehicle_transform.transform_points(f) pedestrians[ agent.id]['bounding_box_coord'] = f_rotated f_2D_rotated = [] for i in range(f.shape[0]): point = np.array([[f_rotated[i, 0]], [f_rotated[i, 1]], [f_rotated[i, 2]], [1]]) transformed_2d_pos = np.dot( Rt_inv, point) # See above for cars pos2d = np.dot(K, transformed_2d_pos[:3]) norm_pos2d = np.array([ pos2d[0] / pos2d[2], pos2d[1] / pos2d[2], pos2d[2] ]) f_2D_rotated.append([ image_size[0] - norm_pos2d[0], image_size[1] - norm_pos2d[1] ]) pedestrians[ agent.id]['bounding_box_2D'] = f_2D_rotated cars_dict[frame] = vehicles pedestrians_dict[frame] = pedestrians # End transformations time mesure. timer.stop() # Save PLY to disk # This generates the PLY string with the 3D points and the RGB colors # for each row of the file. point_cloud.save_to_disk( os.path.join(output_folder, '{:0>5}.ply'.format(frame))) point_cloud_seg.save_to_disk( os.path.join(output_folder, '{:0>5}_seg.ply'.format(frame))) print_message(timer.milliseconds(), len(point_cloud), frame) if autopilot: client.send_control( measurements.player_measurements.autopilot_control) else: control.hand_brake = True client.send_control(control) with open(output_folder + '/cameras.p', 'w') as camerafile: pickle.dump(cameras_dict, camerafile) print(output_folder + "cameras.p") with open(output_folder + '/people.p', 'w') as peoplefile: pickle.dump(pedestrians_dict, peoplefile) with open(output_folder + '/cars.p', 'w') as carfile: pickle.dump(cars_dict, carfile)
def __init__(self): self.stop_watch = StopWatch() self.sum = 0.0 self.count = 0 self.max = 0.0 self.min = float("inf")
def __init__(self): self.stop_watch = StopWatch() self.sum = 0.0 self.count = 0 self.max = 0.0 self.min = float("inf")
def run_carla_client(host, port, far): # Here we will run a single episode with 300 frames. number_of_frames = 3000 frame_step = 100 # Save one image every 100 frames output_folder = '_out' image_size = [800, 600] camera_local_pos = [0.3, 0.0, 1.3] # [X, Y, Z] camera_local_rotation = [0, 0, 0] # [pitch(Y), yaw(Z), roll(X)] fov = 70 # Connect with the server with make_carla_client(host, port) as client: print('CarlaClient connected') # Here we load the settings. settings = CarlaSettings() settings.set(SynchronousMode=True, SendNonPlayerAgentsInfo=False, NumberOfVehicles=20, NumberOfPedestrians=40, WeatherId=random.choice([1, 3, 7, 8, 14])) settings.randomize_seeds() camera1 = Camera('CameraDepth', PostProcessing='Depth', FOV=fov) camera1.set_image_size(*image_size) camera1.set_position(*camera_local_pos) camera1.set_rotation(*camera_local_rotation) settings.add_sensor(camera1) camera2 = Camera('CameraRGB', PostProcessing='SceneFinal', FOV=fov) camera2.set_image_size(*image_size) camera2.set_position(*camera_local_pos) camera2.set_rotation(*camera_local_rotation) settings.add_sensor(camera2) client.load_settings(settings) # Start at location index id '0' client.start_episode(0) # Compute the camera transform matrix camera_to_car_transform = camera2.get_unreal_transform() # Iterate every frame in the episode except for the first one. for frame in range(1, number_of_frames): # Read the data produced by the server this frame. measurements, sensor_data = client.read_data() # Save one image every 'frame_step' frames if not frame % frame_step: # Start transformations time mesure. timer = StopWatch() # RGB image [[[r,g,b],..[r,g,b]],..[[r,g,b],..[r,g,b]]] image_RGB = to_rgb_array(sensor_data['CameraRGB']) # 2d to (camera) local 3d # We use the image_RGB to colorize each 3D point, this is optional. # "max_depth" is used to keep only the points that are near to the # camera, meaning 1.0 the farest points (sky) point_cloud = depth_to_local_point_cloud( sensor_data['CameraDepth'], image_RGB, max_depth=far) # (Camera) local 3d to world 3d. # Get the transform from the player protobuf transformation. world_transform = Transform( measurements.player_measurements.transform) # Compute the final transformation matrix. car_to_world_transform = world_transform * camera_to_car_transform # Car to World transformation given the 3D points and the # transformation matrix. point_cloud.apply_transform(car_to_world_transform) # End transformations time mesure. timer.stop() # Save PLY to disk # This generates the PLY string with the 3D points and the RGB colors # for each row of the file. point_cloud.save_to_disk( os.path.join(output_folder, '{:0>5}.ply'.format(frame))) print_message(timer.milliseconds(), len(point_cloud), frame) client.send_control( measurements.player_measurements.autopilot_control)
def run_carla_client(host, port, far): # Here we will run a single episode with 300 frames. number_of_frames = 3000 frame_step = 100 # Save one image every 100 frames output_folder = '_out' image_size = [800, 600] camera_local_pos = [0.3, 0.0, 1.3] # [X, Y, Z] camera_local_rotation = [0, 0, 0] # [pitch(Y), yaw(Z), roll(X)] fov = 70 # Connect with the server with make_carla_client(host, port) as client: print('CarlaClient connected') # Here we load the settings. settings = CarlaSettings() settings.set( SynchronousMode=True, SendNonPlayerAgentsInfo=False, NumberOfVehicles=20, NumberOfPedestrians=40, WeatherId=random.choice([1, 3, 7, 8, 14])) settings.randomize_seeds() camera1 = Camera('CameraDepth', PostProcessing='Depth', FOV=fov) camera1.set_image_size(*image_size) camera1.set_position(*camera_local_pos) camera1.set_rotation(*camera_local_rotation) settings.add_sensor(camera1) camera2 = Camera('CameraRGB', PostProcessing='SceneFinal', FOV=fov) camera2.set_image_size(*image_size) camera2.set_position(*camera_local_pos) camera2.set_rotation(*camera_local_rotation) settings.add_sensor(camera2) client.load_settings(settings) # Start at location index id '0' client.start_episode(0) # Compute the camera transform matrix camera_to_car_transform = camera2.get_unreal_transform() # Iterate every frame in the episode except for the first one. for frame in range(1, number_of_frames): # Read the data produced by the server this frame. measurements, sensor_data = client.read_data() # Save one image every 'frame_step' frames if not frame % frame_step: # Start transformations time mesure. timer = StopWatch() # RGB image [[[r,g,b],..[r,g,b]],..[[r,g,b],..[r,g,b]]] image_RGB = to_rgb_array(sensor_data['CameraRGB']) # 2d to (camera) local 3d # We use the image_RGB to colorize each 3D point, this is optional. # "max_depth" is used to keep only the points that are near to the # camera, meaning 1.0 the farest points (sky) point_cloud = depth_to_local_point_cloud( sensor_data['CameraDepth'], image_RGB, max_depth=far ) # (Camera) local 3d to world 3d. # Get the transform from the player protobuf transformation. world_transform = Transform( measurements.player_measurements.transform ) # Compute the final transformation matrix. car_to_world_transform = world_transform * camera_to_car_transform # Car to World transformation given the 3D points and the # transformation matrix. point_cloud.apply_transform(car_to_world_transform) # End transformations time mesure. timer.stop() # Save PLY to disk # This generates the PLY string with the 3D points and the RGB colors # for each row of the file. point_cloud.save_to_disk(os.path.join( output_folder, '{:0>5}.ply'.format(frame)) ) print_message(timer.milliseconds(), len(point_cloud), frame) client.send_control( measurements.player_measurements.autopilot_control )