Carla实现强化学习（1）安装所需模块carla创建一个汽车并添加摄像头展示摄像头获取的画面图片的reshape报错

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我是靠谱客的博主背后草丛，这篇文章主要介绍Carla实现强化学习（1）安装所需模块carla创建一个汽车并添加摄像头展示摄像头获取的画面图片的reshape报错，现在分享给大家，希望可以做个参考。

Carla实现强化学习（1

安装所需模块
carla创建一个汽车并添加摄像头展示摄像头获取的画面
图片的reshape报错

安装所需模块

首先下载carla
链接: https://pan.baidu.com/s/1JTfm93EjYNXBgeUrN6Z8lQ 提取码: qxf4
注意：python的版本使用3.7 不然的话会报错
安装tensorflow

复制代码

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pip install tensorflow==2.0.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
pip install tensorflow-gpu==2.0.0 -i https://pypi.tuna.tsinghua.edu.cn/simple

carla创建一个汽车并添加摄像头展示摄像头获取的画面

复制代码

import glob
import os
import sys
import random
import time
import numpy as np
import cv2
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
IMG_WIDTH=600
IMG_HEIGHT=480
def process_img(image):
i=np.array(image.raw_data)
print(i.shape)
i2=i.reshape((IMG_HEIGHT,IMG_WIDTH,4))
i3=i2[:,:,:3] #所有的高度和rgb，不要alpha通道，快速获取RGB
#print(dir(image))
cv2.imshow('',i3)
cv2.waitKey(1)
return i3/255.0
actor_list=[]
try:
##生成一个车辆
client=carla.Client(host='127.0.0.1', port=2000) #连接主机
client.set_timeout(2.0)
world=client.get_world()
blueprint_library=world.get_blueprint_library()
bp=blueprint_library.filter('model3')[0]#获取车辆
print(bp)
spaw_point=random.choice(world.get_map().get_spawn_points())#在随机点生成车辆 ，随机点
vehicle=world.spawn_actor(bp,spaw_point)#随机点生成车辆
#vehicle.set_autopilot(True) #基于规则的行驶
vehicle.apply_control(carla.VehicleControl(throttle=1.0,steer=0.0))
actor_list.append(vehicle)
##在车辆上方安装传感器,以满足后面的碰撞检测
cam_bp=blueprint_library.find('sensor.camera.rgb')
cam_bp.set_attribute('image_size_x',f"{IMG_WIDTH}")
cam_bp.set_attribute('image_size_x', f"{IMG_HEIGHT}")
cam_bp.set_attribute('fov','110')
spawn_point=carla.Transform(carla.Location(x=2.5,z=0.7))#将摄像机的位置进行设置
sensor=world.spawn_actor(cam_bp,spawn_point,attach_to=vehicle)
actor_list.append(sensor)
sensor.listen(lambda data:process_img(data))
time.sleep(10)
finally:
for actor in actor_list:
actor.destroy()
actor.destroy()
print('All cleaned up!')

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import glob
import os
import sys
import random
import time
import numpy as np
import cv2
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
IMG_WIDTH=600
IMG_HEIGHT=480
def process_img(image):
i=np.array(image.raw_data)
print(i.shape)
i2=i.reshape((IMG_HEIGHT,IMG_WIDTH,4))
i3=i2[:,:,:3] #所有的高度和rgb，不要alpha通道，快速获取RGB
#print(dir(image))
cv2.imshow('',i3)
cv2.waitKey(1)
return i3/255.0
actor_list=[]
try:
##生成一个车辆
client=carla.Client(host='127.0.0.1', port=2000) #连接主机
client.set_timeout(2.0)
world=client.get_world()
blueprint_library=world.get_blueprint_library()
bp=blueprint_library.filter('model3')[0]#获取车辆
print(bp)
spaw_point=random.choice(world.get_map().get_spawn_points())#在随机点生成车辆 ，随机点
vehicle=world.spawn_actor(bp,spaw_point)#随机点生成车辆
#vehicle.set_autopilot(True) #基于规则的行驶
vehicle.apply_control(carla.VehicleControl(throttle=1.0,steer=0.0))
actor_list.append(vehicle)
##在车辆上方安装传感器,以满足后面的碰撞检测
cam_bp=blueprint_library.find('sensor.camera.rgb')
cam_bp.set_attribute('image_size_x',f"{IMG_WIDTH}")
cam_bp.set_attribute('image_size_x', f"{IMG_HEIGHT}")
cam_bp.set_attribute('fov','110')
spawn_point=carla.Transform(carla.Location(x=2.5,z=0.7))#将摄像机的位置进行设置
sensor=world.spawn_actor(cam_bp,spawn_point,attach_to=vehicle)
actor_list.append(sensor)
sensor.listen(lambda data:process_img(data))
time.sleep(10)
finally:
for actor in actor_list:
actor.destroy()
actor.destroy()
print('All cleaned up!')