从carla的语义相机中，获取车辆的yolo格式标注
在同一个位置设置语义相机和rgb相机
#!/usr/bin/env python
# Copyright (c) 2020 Computer Vision Center (CVC) at the Universitat Autonoma de
# Barcelona (UAB).
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Script to integrate CARLA and SUMO simulations
"""
# ==================================================================================================
# -- imports ---------------------------------------------------------------------------------------
# ==================================================================================================
import argparse
import logging
import time
# ==================================================================================================
# -- find carla module -----------------------------------------------------------------------------
# ==================================================================================================
import glob
import os
import sys
import carla
import traci
try:
sys.path.append(
glob.glob('../../PythonAPI/carla/dist/carla-*%d.%d-%s.egg' %
(sys.version_info.major, sys.version_info.minor,
'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
pass
# ==================================================================================================
# -- find traci module -----------------------------------------------------------------------------
# ==================================================================================================
if 'SUMO_HOME' in os.environ:
sys.path.append(os.path.join(os.environ['SUMO_HOME'], 'tools'))
else:
sys.exit("please declare environment variable 'SUMO_HOME'")
# ==================================================================================================
# -- sumo integration imports ----------------------------------------------------------------------
# ==================================================================================================
from sumo_integration.bridge_helper import BridgeHelper # pylint: disable=wrong-import-position
from sumo_integration.carla_simulation import CarlaSimulation # pylint: disable=wrong-import-position
from sumo_integration.constants import INVALID_ACTOR_ID # pylint: disable=wrong-import-position
from sumo_integration.sumo_simulation import SumoSimulation # pylint: disable=wrong-import-position
# ==================================================================================================
# -- synchronization_loop --------------------------------------------------------------------------
# ==================================================================================================
class SimulationSynchronization(object):
"""
SimulationSynchronization class is responsible for the synchronization of sumo and carla
simulations.
"""
def __init__(self,
sumo_simulation,
carla_simulation,
tls_manager='none',
sync_vehicle_color=False,
sync_vehicle_lights=False):
self.sumo = sumo_simulation
self.carla = carla_simulation
self.tls_manager = tls_manager
self.sync_vehicle_color = sync_vehicle_color
self.sync_vehicle_lights = sync_vehicle_lights
if tls_manager == 'carla':
self.sumo.switch_off_traffic_lights()
elif tls_manager == 'sumo':
self.carla.switch_off_traffic_lights()
# Mapped actor ids.
self.sumo2carla_ids = {} # Contains only actors controlled by sumo.
self.carla2sumo_ids = {} # Contains only actors controlled by carla.
BridgeHelper.blueprint_library = self.carla.world.get_blueprint_library()
BridgeHelper.offset = self.sumo.get_net_offset()
# Configuring carla simulation in sync mode.
settings = self.carla.world.get_settings()
settings.synchronous_mode = True
settings.max_substep_delta_time = 0.1
settings.fixed_delta_seconds = self.carla.step_length
self.carla.world.apply_settings(settings)
traffic_manager = self.carla.client.get_trafficmanager()
traffic_manager.set_synchronous_mode(True)
self.image_width = 640
self.image_height = 640
self.fov = 90
camera_blueprint = BridgeHelper.blueprint_library.find('sensor.camera.semantic_segmentation')
camera_blueprint.set_attribute('image_size_x', str(self.image_width))
camera_blueprint.set_attribute('image_size_y', str(self.image_height))
camera_blueprint.set_attribute('fov', str(self.fov))
camera_blueprint_rgb = BridgeHelper.blueprint_library.find('sensor.camera.rgb')
camera_blueprint_rgb.set_attribute('image_size_x', str(self.image_width))
camera_blueprint_rgb.set_attribute('image_size_y', str(self.image_height))
camera_blueprint_rgb.set_attribute('fov', str(self.fov))
self.camera_n = self.carla.world.spawn_actor(camera_blueprint, carla.Transform(carla.Location(x=-3.63, y=-13.55, z=18.0), carla.Rotation(pitch=-40, yaw=-90.0, roll=0.0)))
self.camera_s = self.carla.world.spawn_actor(camera_blueprint, carla.Transform(carla.Location(x=3.63, y=13.55, z=18.0), carla.Rotation(pitch=-40, yaw=90.0, roll=0.0))) # s
self.camera_w = self.carla.world.spawn_actor(camera_blueprint, carla.Transform(carla.Location(x=-13.55, y=3.63, z=18.0), carla.Rotation(pitch=-40, yaw=180.0, roll=0.0))) # w
self.camera_e = self.carla.world.spawn_actor(camera_blueprint,carla.Transform(carla.Location(x=13.55, y=-3.63, z=18.0), carla.Rotation(pitch=-40, yaw=0.0, roll=0.0))) # e
# self.camera_n.listen(lambda data: self.process_img(image=data, name="camera_n"))
# self.camera_s.listen(lambda data: self.process_img(image=data, name="camera_s"))
# self.camera_e.listen(lambda data: self.process_img(image=data, name="camera_e"))
# self.camera_w.listen(lambda data: self.process_img(image=data, name="camera_w"))
self.camera_n.listen(lambda image: image.save_to_disk('carla_images_segment_2/n_out/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette))
self.camera_s.listen(lambda image: image.save_to_disk('carla_images_segment_2/s_out/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette))
self.camera_e.listen(lambda image: image.save_to_disk('carla_images_segment_2/e_out/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette))
self.camera_w.listen(lambda image: image.save_to_disk('carla_images_segment_2/w_out/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette))
self.camera_n_rgb = self.carla.world.spawn_actor(camera_blueprint_rgb, carla.Transform(carla.Location(x=-3.63, y=-13.55, z=18.0), carla.Rotation(pitch=-40, yaw=-90.0, roll=0.0)))
self.camera_s_rgb = self.carla.world.spawn_actor(camera_blueprint_rgb, carla.Transform(carla.Location(x=3.63, y=13.55, z=18.0), carla.Rotation(pitch=-40, yaw=90.0, roll=0.0))) # s
self.camera_w_rgb = self.carla.world.spawn_actor(camera_blueprint_rgb, carla.Transform(carla.Location(x=-13.55, y=3.63, z=18.0), carla.Rotation(pitch=-40, yaw=180.0, roll=0.0))) # w
self.camera_e_rgb = self.carla.world.spawn_actor(camera_blueprint_rgb,carla.Transform(carla.Location(x=13.55, y=-3.63, z=18.0), carla.Rotation(pitch=-40, yaw=0.0, roll=0.0))) # e
# self.camera_n_rgb.listen(lambda data: self.process_img_rgb(image=data, name="camera_n"))
# self.camera_s_rgb.listen(lambda data: self.process_img_rgb(image=data, name="camera_s"))
# self.camera_w_rgb.listen(lambda data: self.process_img_rgb(image=data, name="camera_e"))
# self.camera_e_rgb.listen(lambda data: self.process_img_rgb(image=data, name="camera_w"))
self.camera_n_rgb.listen(lambda image: image.save_to_disk('carla_images_segment_2/n_out_segment/%06d.png' % image.frame))
self.camera_s_rgb.listen(lambda image: image.save_to_disk('carla_images_segment_2/s_out_segment/%06d.png' % image.frame))
self.camera_w_rgb.listen(lambda image: image.save_to_disk('carla_images_segment_2/w_out_segment/%06d.png' % image.frame))
self.camera_e_rgb.listen(lambda image: image.save_to_disk('carla_images_segment_2/e_out_segment/%06d.png' % image.frame))
self.sensor_list = []
self.sensor_list.append(self.camera_n)
self.sensor_list.append(self.camera_s)
self.sensor_list.append(self.camera_e)
self.sensor_list.append(self.camera_w)
self.sensor_list.append(self.camera_n_rgb)
self.sensor_list.append(self.camera_s_rgb)
self.sensor_list.append(self.camera_w_rgb)
self.sensor_list.append(self.camera_e_rgb)
def process_img(self, image, name):
frame = image.frame
# print('frame: ', frame)
if frame % 5 == 0:
if name == 'camera_n':
image.save_to_disk('carla_images_segment_2/n_out_segment/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette)
# image.save_to_disk('carla_images_segment_1/n_out/%06d.png' % image.frame)
if name == 'camera_s':
image.save_to_disk('carla_images_segment_2/s_out_segment/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette)
# image.save_to_disk('carla_images_segment_1/s_out/%06d.png' % image.frame)
if name == 'camera_w':
image.save_to_disk('carla_images_segment_2/w_out_segment/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette)
# image.save_to_disk('carla_images_segment_1/w_out/%06d.png' % image.frame)
if name == 'camera_e':
image.save_to_disk('carla_images_segment_2/e_out_segment/%06d.png' % image.frame, carla.ColorConverter.CityScapesPalette)
# image.save_to_disk('carla_images_segment_1/e_out/%06d.png' % image.frame)
def process_img_rgb(self, image, name):
frame = image.frame
# print('frame: ', frame)
if frame % 5 == 0:
if name == 'camera_n':
image.save_to_disk('carla_images_segment_2/n_out/%06d.png' % image.frame)
if name == 'camera_s':
image.save_to_disk('carla_images_segment_2/s_out/%06d.png' % image.frame)
if name == 'camera_w':
image.save_to_disk('carla_images_segment_2/w_out/%06d.png' % image.frame)
if name == 'camera_e':
image.save_to_disk('carla_images_segment_2/e_out/%06d.png' % image.frame)
def tick(self):
"""
Tick to simulation synchronization
"""
# -----------------
# sumo-->carla sync
# -----------------
self.sumo.tick()
# Spawning new sumo actors in carla (i.e, not controlled by carla).
sumo_spawned_actors = self.sumo.spawned_actors - set(self.carla2sumo_ids.values())
for sumo_actor_id in sumo_spawned_actors:
self.sumo.subscribe(sumo_actor_id)
sumo_actor = self.sumo.get_actor(sumo_actor_id)
carla_blueprint = BridgeHelper.get_carla_blueprint(sumo_actor, self.sync_vehicle_color)
if carla_blueprint is not None:
carla_transform = BridgeHelper.get_carla_transform(sumo_actor.transform,
sumo_actor.extent)
carla_actor_id = self.carla.spawn_actor(carla_blueprint, carla_transform)
if carla_actor_id != INVALID_ACTOR_ID:
self.sumo2carla_ids[sumo_actor_id] = carla_actor_id
else:
self.sumo.unsubscribe(sumo_actor_id)
# Destroying sumo arrived actors in carla.
for sumo_actor_id in self.sumo.destroyed_actors:
if sumo_actor_id in self.sumo2carla_ids:
self.carla.destroy_actor(self.sumo2carla_ids.pop(sumo_actor_id))
# Updating sumo actors in carla.
for sumo_actor_id in self.sumo2carla_ids:
carla_actor_id = self.sumo2carla_ids[sumo_actor_id]
sumo_actor = self.sumo.get_actor(sumo_actor_id)
carla_actor = self.carla.get_actor(carla_actor_id)
carla_transform = BridgeHelper.get_carla_transform(sumo_actor.transform,
sumo_actor.extent)
if self.sync_vehicle_lights:
carla_lights = BridgeHelper.get_carla_lights_state(carla_actor.get_light_state(),
sumo_actor.signals)
else:
carla_lights = None
self.carla.synchronize_vehicle(carla_actor_id, carla_transform, carla_lights)
# Updates traffic lights in carla based on sumo information.
if self.tls_manager == 'sumo':
common_landmarks = self.sumo.traffic_light_ids & self.carla.traffic_light_ids
for landmark_id in common_landmarks:
sumo_tl_state = self.sumo.get_traffic_light_state(landmark_id)
carla_tl_state = BridgeHelper.get_carla_traffic_light_state(sumo_tl_state)
self.carla.synchronize_traffic_light(landmark_id, carla_tl_state)
# -----------------
# carla-->sumo sync
# -----------------
self.carla.tick()
# Spawning new carla actors (not controlled by sumo)
carla_spawned_actors = self.carla.spawned_actors - set(self.sumo2carla_ids.values())
for carla_actor_id in carla_spawned_actors:
carla_actor = self.carla.get_actor(carla_actor_id)
type_id = BridgeHelper.get_sumo_vtype(carla_actor)
color = carla_actor.attributes.get('color', None) if self.sync_vehicle_color else None
if type_id is not None:
sumo_actor_id = self.sumo.spawn_actor(type_id, color)
if sumo_actor_id != INVALID_ACTOR_ID:
self.carla2sumo_ids[carla_actor_id] = sumo_actor_id
self.sumo.subscribe(sumo_actor_id)
# Destroying required carla actors in sumo.
for carla_actor_id in self.carla.destroyed_actors:
if carla_actor_id in self.carla2sumo_ids:
self.sumo.destroy_actor(self.carla2sumo_ids.pop(carla_actor_id))
# Updating carla actors in sumo.
for carla_actor_id in self.carla2sumo_ids:
sumo_actor_id = self.carla2sumo_ids[carla_actor_id]
carla_actor = self.carla.get_actor(carla_actor_id)
sumo_actor = self.sumo.get_actor(sumo_actor_id)
sumo_transform = BridgeHelper.get_sumo_transform(carla_actor.get_transform(),
carla_actor.bounding_box.extent)
if self.sync_vehicle_lights:
carla_lights = self.carla.get_actor_light_state(carla_actor_id)
if carla_lights is not None:
sumo_lights = BridgeHelper.get_sumo_lights_state(sumo_actor.signals,
carla_lights)
else:
sumo_lights = None
else:
sumo_lights = None
self.sumo.synchronize_vehicle(sumo_actor_id, sumo_transform, sumo_lights)
# Updates traffic lights in sumo based on carla information.
if self.tls_manager == 'carla':
common_landmarks = self.sumo.traffic_light_ids & self.carla.traffic_light_ids
for landmark_id in common_landmarks:
carla_tl_state = self.carla.get_traffic_light_state(landmark_id)
sumo_tl_state = BridgeHelper.get_sumo_traffic_light_state(carla_tl_state)
# Updates all the sumo links related to this landmark.
self.sumo.synchronize_traffic_light(landmark_id, sumo_tl_state)
def close(self):
"""
Cleans synchronization.
"""
# Configuring carla simulation in async mode.
settings = self.carla.world.get_settings()
settings.synchronous_mode = False
settings.fixed_delta_seconds = None
self.carla.world.apply_settings(settings)
# Destroying synchronized actors.
for carla_actor_id in self.sumo2carla_ids.values():
self.carla.destroy_actor(carla_actor_id)
for sumo_actor_id in self.carla2sumo_ids.values():
self.sumo.destroy_actor(sumo_actor_id)
# Closing sumo and carla client.
self.carla.close()
self.sumo.close()
for x in self.sensor_list:
# if x.is_listening():
# x.stop()
self.carla.destroy_actor(x)
def synchronization_loop(args):
"""
Entry point for sumo-carla co-simulation.
"""
sumo_simulation = SumoSimulation(args.sumo_cfg_file, args.step_length, args.sumo_host,
args.sumo_port, args.sumo_gui, args.client_order)
# carla_simulation = CarlaSimulation(args.carla_host, args.carla_port, args.step_length)
carla_simulation = CarlaSimulation(args.carla_host, args.carla_port, args.step_length, args.xodr_path, args.carla_seed)
synchronization = SimulationSynchronization(sumo_simulation, carla_simulation, args.tls_manager,
args.sync_vehicle_color, args.sync_vehicle_lights)
try:
while True:
start = time.time()
synchronization.tick()
end = time.time()
elapsed = end - start
if elapsed < args.step_length:
time.sleep(args.step_length - elapsed)
except KeyboardInterrupt:
logging.info('Cancelled by user.')
finally:
logging.info('Cleaning synchronization')
synchronization.close()
# traci.close()
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description=__doc__)
argparser.add_argument('--sumo_cfg_file', type=str, help='sumo configuration file', default='sumo_integration/examples/gong_ten_cross_one.sumocfg')
argparser.add_argument('--carla-host',
metavar='H',
default='127.0.0.1',
help='IP of the carla host server (default: 127.0.0.1)')
argparser.add_argument('--carla-port',
metavar='P',
default=2000,
type=int,
help='TCP port to listen to (default: 2000)')
argparser.add_argument('--sumo-host',
metavar='H',
default=None,
help='IP of the sumo host server (default: 127.0.0.1)')
argparser.add_argument('--sumo-port',
metavar='P',
default=None,
type=int,
help='TCP port to listen to (default: 8813)')
argparser.add_argument('--sumo-gui', default=True, help='run the gui version of sumo')
argparser.add_argument('--step-length',
default=1,
type=float,
help='set fixed delta seconds (default: 0.05s)')
argparser.add_argument('--client-order',
metavar='TRACI_CLIENT_ORDER',
default=1,
type=int,
help='client order number for the co-simulation TraCI connection (default: 1)')
argparser.add_argument('--sync-vehicle-lights',
action='store_true',
help='synchronize vehicle lights state (default: False)')
argparser.add_argument('--sync-vehicle-color',
action='store_true',
help='synchronize vehicle color (default: False)')
argparser.add_argument('--sync-vehicle-all',
action='store_true',
help='synchronize all vehicle properties (default: False)')
argparser.add_argument('--tls-manager',
type=str,
choices=['none', 'sumo', 'carla'],
help="select traffic light manager (default: none)",
default='none')
argparser.add_argument('--debug', action='store_true', help='enable debug messages')
argparser.add_argument('--carla-seed', type=int, default=21, help='sumo seed')
argparser.add_argument('--xodr-path', type=str, default='sumo_integration/examples/opendrive/gong_ten_cross.xodr', help='xodr path')
arguments = argparser.parse_args()
if arguments.sync_vehicle_all is True:
arguments.sync_vehicle_lights = True
arguments.sync_vehicle_color = True
if arguments.debug:
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.DEBUG)
else:
logging.basicConfig(format='%(levelname)s: %(message)s', level=logging.INFO)
synchronization_loop(arguments)
筛选作为数据集的图片，删掉没有语义图片而有rgb图片
import os
# 定义两个文件夹的路径
folder_a = 'D:\Work\Pycharm\FinallyDesign\carla_images_segment_2\\e_out'
folder_b = 'D:\Work\Pycharm\FinallyDesign\carla_images_segment_2\\e_out_segment'
# 获取两个文件夹中的文件名
files_in_a = set(os.listdir(folder_a))
files_in_b = set(os.listdir(folder_b))
# 查找只在文件夹a中存在的文件
files_only_in_a = files_in_a - files_in_b
# 删除这些文件
for file in files_only_in_a:
os.remove(os.path.join(folder_a, file))
跟据carla语义相机中车辆的颜色，获取其yolo标注，并存储起来
import os
import cv2
import numpy as np
# 循环处理文件夹中的所有图片
def process_directory(directory_path, rgb_path, directory_path_txt):
for filename in os.listdir(directory_path):
if filename.endswith(".jpg") or filename.endswith(".png"):
image_path = os.path.join(directory_path, filename)
image_rgb_path = os.path.join(rgb_path, filename)
target_path = os.path.join(directory_path_txt, filename)
process_image(image_path, image_rgb_path, target_path)
# 处理单张图片
def process_image(image_path, rgb_path, target_path):
# 读取图像
img = cv2.imread(image_path)
img_rgb = cv2.imread(rgb_path)
# 找到像素值为(0, 0, 142)的所有像素
mask = np.all(img == (142, 0, 0), axis=-1)
# 应用连通组件算法
num_labels, labels = cv2.connectedComponents(mask.astype(np.uint8))
# 准备一个列表来存储结果
result = []
# 对每个连通组件
for label in range(1, num_labels):
# 找到这个组件的像素
component = np.where(labels == label)
# 计算外接矩形
y_min, y_max = np.min(component[0]), np.max(component[0])
x_min, x_max = np.min(component[1]), np.max(component[1])
# 转换为YOLO格式
x_center = (x_min + x_max) / 2 / img.shape[1]
y_center = (y_min + y_max) / 2 / img.shape[0]
width = (x_max - x_min) / img.shape[1]
height = (y_max - y_min) / img.shape[0]
# 添加到结果
result.append((x_center, y_center, width, height))
cv2.rectangle(img_rgb, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2) # 用绿色画框
# 将标注的图像保存
annotated_image_path = os.path.splitext(target_path)[0] + '_annotated.jpg'
cv2.imwrite(annotated_image_path, img_rgb)
# 将结果写入txt文件
output_path = target_path.rsplit('.', 1)[0] + '.txt'
with open(output_path, 'w') as f:
for item in result:
f.write(f'0 {item[0]} {item[1]} {item[2]} {item[3]}\n')
# 使用方法：指定文件夹路径
directory_path = 'D:\Work\Pycharm\FinallyDesign\carla_images_segment_2\\e_out_segment'
directory_path_rgb = 'D:\Work\Pycharm\FinallyDesign\carla_images_segment_2\\e_out'
directory_path_txt = 'D:\Work\Pycharm\FinallyDesign\carla_images_segment_2\\e'
process_directory(directory_path, directory_path_rgb, directory_path_txt)