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基础版本收集数据：
来自：zhang-kin
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部分代码有过修改，不然运行起来，车不动。
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这里没有保存imu_yaw, gnss的数据
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由于在收集过程中进行了可视化窗口展示，cv2.waitKey(100)，因此需要按一下键盘任意键，才可以停止阻塞。
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collect_data.py
#!/usr/bin/env python
"""
此代码主要用于收集数据示意（简易版）
对应博文的详细教程在 https://www.cnblogs.com/kin-zhang/p/16057173.html
0. 此示意引用参考全部总结与博客之中 再次不做赘述
1. 记得修改一下保存数据的绝对路径
2. 检查CARLA PORT是否和设置的是一致的 免得连接不上
3. 相关注释基本都没咋写 具体可以看看博客
"""
import glob
import os
import sys
import time
try:
sys.path.append(glob.glob('../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
import carla
import random
import numpy as np
import cv2
from queue import Queue, Empty
import copy
import random
random.seed(0)
# args
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--host', metavar='H', default='127.0.0.1', help='IP of the host server (default: 127.0.0.1)')
parser.add_argument('--port', '-p', default=2000, type=int, help='TCP port to listen to (default: 2000)')
parser.add_argument('--tm_port', default=8000, type=int, help='Traffic Manager Port (default: 8000)')
parser.add_argument('--ego-spawn', type=list, default=None, help='[x,y] in world coordinate')
parser.add_argument('--top-view', default=True, help='Setting spectator to top view on ego car')
parser.add_argument('--map', default='Town04', help='Town Map')
parser.add_argument('--sync', default=True, help='Synchronous mode execution')
parser.add_argument('--sensor-h', default=2.4, help='Sensor Height')
# 给绝对路径 记得改位置哦！
parser.add_argument('--save-path', default='/home/aikedaer/Desktop/carla_data/', help='Synchronous mode execution')
args = parser.parse_args()
# 图片大小可自行修改
IM_WIDTH = 256
IM_HEIGHT = 256
actor_list, sensor_list = [], []
sensor_type = ['rgb','lidar','imu','gnss']
def main(args):
try:
# We start creating the client
client = carla.Client(args.host, args.port)
client.set_timeout(5.0)
# world = client.get_world()
world = client.load_world(args.map)
blueprint_library = world.get_blueprint_library()
# Set weather for your world
weather = carla.WeatherParameters(cloudiness=10.0,
precipitation=10.0,
fog_density=10.0)
world.set_weather(weather)
original_settings = world.get_settings()
settings = world.get_settings()
# We set CARLA syncronous mode
settings.fixed_delta_seconds = 0.05
settings.synchronous_mode = True
world.apply_settings(settings)
spectator = world.get_spectator()
# 设置traffic manager
tm = client.get_trafficmanager(args.tm_port)
tm.set_synchronous_mode(True)
# 是否忽略红绿灯
# tm.ignore_lights_percentage(ego_vehicle, 100)
# 如果限速30km/h -> 30*(1+10%)= 33km/h
tm.global_percentage_speed_difference(-10.0)
# 手动规定
# transform_vehicle = carla.Transform(carla.Location(0, 10, 0), carla.Rotation(0, 0, 0))
# 自动选择
transform_vehicle = random.choice(world.get_map().get_spawn_points())
ego_vehicle = world.spawn_actor(random.choice(blueprint_library.filter("model3")), transform_vehicle)
ego_vehicle.set_autopilot(True, tm.get_port())
actor_list.append(ego_vehicle)
#-------------------------- 进入传感器部分 --------------------------#
sensor_queue = Queue()
cam_bp = blueprint_library.find('sensor.camera.rgb')
lidar_bp = blueprint_library.find('sensor.lidar.ray_cast')
imu_bp = blueprint_library.find('sensor.other.imu')
gnss_bp = blueprint_library.find('sensor.other.gnss')
# 可以设置一些参数 set the attribute of camera
cam_bp.set_attribute("image_size_x", "{}".format(IM_WIDTH))
cam_bp.set_attribute("image_size_y", "{}".format(IM_HEIGHT))
cam_bp.set_attribute("fov", "60")
# cam_bp.set_attribute('sensor_tick', '0.1')
# 这里的Rotation不是简单的直角坐标系上的刻度，而是弧度坐标系
cam00 = world.spawn_actor(cam_bp, carla.Transform(carla.Location(z=args.sensor_h),carla.Rotation(yaw=60)), attach_to=ego_vehicle)
cam00.listen(lambda data: sensor_callback(data, sensor_queue, "rgb_left"))
sensor_list.append(cam00)
cam01 = world.spawn_actor(cam_bp, carla.Transform(carla.Location(z=args.sensor_h),carla.Rotation(yaw=0)), attach_to=ego_vehicle)
cam01.listen(lambda data: sensor_callback(data, sensor_queue, "rgb_front"))
sensor_list.append(cam01)
cam02 = world.spawn_actor(cam_bp, carla.Transform(carla.Location(z=args.sensor_h),carla.Rotation(yaw=-60)), attach_to=ego_vehicle)
cam02.listen(lambda data: sensor_callback(data, sensor_queue, "rgb_right"))
sensor_list.append(cam02)
lidar_bp.set_attribute('channels', '64')
lidar_bp.set_attribute('points_per_second', '200000')
lidar_bp.set_attribute('range', '32')
lidar_bp.set_attribute('rotation_frequency', str(int(1/settings.fixed_delta_seconds))) #
lidar01 = world.spawn_actor(lidar_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)
lidar01.listen(lambda data: sensor_callback(data, sensor_queue, "lidar"))
sensor_list.append(lidar01)
imu01 = world.spawn_actor(imu_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)
imu01.listen(lambda data: sensor_callback(data, sensor_queue, "imu"))
sensor_list.append(imu01)
gnss01 = world.spawn_actor(gnss_bp, carla.Transform(carla.Location(z=args.sensor_h)), attach_to=ego_vehicle)
gnss01.listen(lambda data: sensor_callback(data, sensor_queue, "gnss"))
sensor_list.append(gnss01)
#-------------------------- 传感器设置完毕 --------------------------#
while True:
# Tick the server
world.tick()
# 将CARLA界面摄像头跟随车动
loc = ego_vehicle.get_transform().location
spectator.set_transform(carla.Transform(carla.Location(x=loc.x,y=loc.y,z=35),carla.Rotation(yaw=0,pitch=-90,roll=0)))
w_frame = world.get_snapshot().frame
print("\nWorld's frame: %d" % w_frame)
try:
rgbs = []
for i in range (0, len(sensor_list)):
s_frame, s_name, s_data = sensor_queue.get(True, 1.0)
print(" Frame: %d Sensor: %s" % (s_frame, s_name))
sensor_type = s_name.split('_')[0]
if sensor_type == 'rgb':
rgbs.append(_parse_image_cb(s_data))
elif sensor_type == 'lidar':
lidar = _parse_lidar_cb(s_data)
elif sensor_type == 'imu':
imu_yaw = s_data.compass
elif sensor_type == 'gnss':
gnss = s_data
# 仅用来可视化 可注释
rgb=np.concatenate(rgbs, axis=1)[...,:3]
cv2.imshow('vizs', visualize_data(rgb, lidar, imu_yaw, gnss))
cv2.waitKey(100)
if rgb is None or args.save_path is not None:
# 检查是否有各自传感器的文件夹
mkdir_folder(args.save_path)
filename = args.save_path +'rgb/'+str(w_frame)+'.png'
cv2.imwrite(filename, np.array(rgb[...,::-1]))
filename = args.save_path +'lidar/'+str(w_frame)+'.npy'
np.save(filename, lidar)
except Empty:
print(" Some of the sensor information is missed")
finally:
world.apply_settings(original_settings)
tm.set_synchronous_mode(False)
for sensor in sensor_list:
sensor.destroy()
for actor in actor_list:
actor.destroy()
print("All cleaned up!")
def mkdir_folder(path):
for s_type in sensor_type:
if not os.path.isdir(os.path.join(path, s_type)):
os.makedirs(os.path.join(path, s_type))
return True
def sensor_callback(sensor_data, sensor_queue, sensor_name):
# Do stuff with the sensor_data data like save it to disk
# Then you just need to add to the queue
sensor_queue.put((sensor_data.frame, sensor_name, sensor_data))
# modify from world on rail code
def visualize_data(rgb, lidar, imu_yaw, gnss, text_args=(cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255,255,255), 1)):
canvas = np.array(rgb[...,::-1])
if lidar is not None:
lidar_viz = lidar_to_bev(lidar).astype(np.uint8)
lidar_viz = cv2.cvtColor(lidar_viz,cv2.COLOR_GRAY2RGB)
canvas = np.concatenate([canvas, cv2.resize(lidar_viz.astype(np.uint8), (canvas.shape[0], canvas.shape[0]))], axis=1)
# cv2.putText(canvas, f'yaw angle: {imu_yaw:.3f}', (4, 10), *text_args)
# cv2.putText(canvas, f'log: {gnss[0]:.3f} alt: {gnss[1]:.3f} brake: {gnss[2]:.3f}', (4, 20), *text_args)
return canvas
# modify from world on rail code
def lidar_to_bev(lidar, min_x=-24,max_x=24,min_y=-16,max_y=16, pixels_per_meter=4, hist_max_per_pixel=10):
xbins = np.linspace(
min_x, max_x+1,
(max_x - min_x) * pixels_per_meter + 1,
)
ybins = np.linspace(
min_y, max_y+1,
(max_y - min_y) * pixels_per_meter + 1,
)
# Compute histogram of x and y coordinates of points.
hist = np.histogramdd(lidar[..., :2], bins=(xbins, ybins))[0]
# Clip histogram
hist[hist > hist_max_per_pixel] = hist_max_per_pixel
# Normalize histogram by the maximum number of points in a bin we care about.
overhead_splat = hist / hist_max_per_pixel * 255.
# Return splat in X x Y orientation, with X parallel to car axis, Y perp, both parallel to ground.
return overhead_splat[::-1,:]
# modify from manual control
def _parse_image_cb(image):
array = np.frombuffer(image.raw_data, dtype=np.dtype("uint8"))
array = np.reshape(array, (image.height, image.width, 4))
array = array[:, :, :3]
array = array[:, :, ::-1]
return array
# modify from leaderboard
def _parse_lidar_cb(lidar_data):
points = np.frombuffer(lidar_data.raw_data, dtype=np.dtype('f4'))
points = copy.deepcopy(points)
points = np.reshape(points, (int(points.shape[0] / 4), 4))
return points
if __name__ == "__main__":
try:
main(args)
except KeyboardInterrupt:
print(' - Exited by user.')来自LAV的数据收集
由于作者的代码库依赖没有写清楚，垃圾复杂的依赖，并没有运行起来 ray 库，各种依赖报错，不是找不到这个就是找不到那个，算了。
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