ubutu下ros2实现小车仿真建模与目标检测 |
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ubutu下ros2实现小车仿真建模与目标检测
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1.安装ros2 这里使用小鱼的一键安装，根据自己的喜好安装，博主用的是ros2的foxy版本 wget http://fishros.com/install -O fishros && . fishros2.下载代码（这里使用的是古月居的代码） https://book.guyuehome.com/ 可以结合古月居的B站视频来自己一步一步操作，里面有讲解基础理论与一些环境的配置 https://www.bilibili.com/video/BV16B4y1Q7jQ?p=1&vd_source=7ab152ebd2f75f63466b8dc7d78d3cf2 3.下载yolov5的代码 https://github.com/fishros/yolov5_ros2/tree/main 将古月居下载的代码与yolov5的代码一起放入一个文件夹下
4.打开终端安装依赖 sudo apt update sudo apt install python3-pip ros--vision-msgs # 替换为humble，foxy或galactic等ros2发行版 pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple yolov5注：如果pip3的安装命令出现报错如下：
这里就代表下载torch时网速慢，需要换源下载，这里我用的是去下载了torch的离线包自己进行安装https://download.pytorch.org/whl/cu116，下载安装包之前自己要先安装cuda
进入torch后使用Ctrl+f进行搜索，cuda对应的torch版本和你的python版本,一键安装ros2的时候会下载一个python2与python3，在终端输入python3就会得到python的版本 从网上自己搜索torch对应版本的torchvision版本
在这里右键打开终端进行安装，torchvision同理，要学会常用tab建去补全命令
然后重新下载依赖 pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple yolov55.修改下载的yolov5_ros2下的yolo_detect_2d.py代码 from math import frexp from traceback import print_tb from torch import imag from yolov5 import YOLOv5 import rclpy from rclpy.node import Node from ament_index_python.packages import get_package_share_directory from rcl_interfaces.msg import ParameterDescriptor from vision_msgs.msg import Detection2DArray, ObjectHypothesisWithPose, Detection2D from sensor_msgs.msg import Image, CameraInfo from cv_bridge import CvBridge import cv2 import yaml from yolov5_ros2.cv_tool import px2xy package_share_directory = get_package_share_directory('yolov5_ros2') # package_share_directory = "/home/mouse/code/github/yolov # # 5_test/src/yolov5_ros2" class YoloV5Ros2(Node): def __init__(self): super().__init__('yolov5_ros2') self.declare_parameter("device", "cuda", ParameterDescriptor( name="device", description="calculate_device default:cpu optional:cuda:0")) self.declare_parameter("model", "yolov5s", ParameterDescriptor( name="model", description="default: yolov5s.pt")) self.declare_parameter("image_topic", "/image_raw", ParameterDescriptor( name="image_topic", description=f"default: /image_raw")) # /camera/image_raw self.declare_parameter("camera_info_topic", "/camera/camera_info", ParameterDescriptor( name="camera_info_topic", description=f"default: /camera/camera_info")) # 默认从camera_info中读取参数,如果可以从话题接收到参数则覆盖文件中的参数 self.declare_parameter("camera_info_file", f"{package_share_directory}/config/camera_info.yaml", ParameterDescriptor( name="camera_info", description=f"{package_share_directory}/config/camera_info.yaml")) # 默认显示识别结果 self.declare_parameter("show_result", True, ParameterDescriptor( name="show_result", description=f"default: True")) # 1.load model model_path = package_share_directory + "/config/" + self.get_parameter('model').value + ".pt" device = self.get_parameter('device').value self.yolov5 = YOLOv5(model_path=model_path, device=device) # 2.create publisher self.yolo_result_pub = self.create_publisher( Detection2DArray, "yolo_result", 10) self.result_msg = Detection2DArray() # 3.create sub image (if 3d, sub depth, if 2d load camera info) image_topic = self.get_parameter('image_topic').value self.image_sub = self.create_subscription( Image, image_topic, self.image_callback, 10) camera_info_topic = self.get_parameter('camera_info_topic').value self.camera_info_sub = self.create_subscription( CameraInfo, camera_info_topic, self.camera_info_callback, 1) # get camera info with open(self.get_parameter('camera_info_file').value) as f: self.camera_info = yaml.full_load(f.read()) print(self.camera_info['k'], self.camera_info['d']) # 4.convert cv2 (cvbridge) self.bridge = CvBridge() self.show_result = self.get_parameter('show_result').value def camera_info_callback(self, msg: CameraInfo): """ 通过回调函数获取到相机的参数信息 """ self.camera_info['k'] = msg.k self.camera_info['p'] = msg.p self.camera_info['d'] = msg.d self.camera_info['r'] = msg.r self.camera_info['roi'] = msg.roi self.camera_info_sub.destroy() def image_callback(self, msg: Image): # 5.detect pub result image = self.bridge.imgmsg_to_cv2(msg) detect_result = self.yolov5.predict(image) self.get_logger().info(str(detect_result)) self.result_msg.detections.clear() self.result_msg.header.frame_id = "camera" self.result_msg.header.stamp = self.get_clock().now().to_msg() # parse results predictions = detect_result.pred[0] boxes = predictions[:, :4] # x1, y1, x2, y2 scores = predictions[:, 4] categories = predictions[:, 5] for index in range(len(categories)): name = detect_result.names[int(categories[index])] detection2d = Detection2D() detection2d.tracking_id = name # detection2d.bbox x1, y1, x2, y2 = boxes[index] x1 = int(x1) y1 = int(y1) x2 = int(x2) y2 = int(y2) center_x = (x1+x2)/2.0 center_y = (y1+y2)/2.0 # detection2d.bbox.center.position.x = center_x # detection2d.bbox.center.position.y = center_y # galactic使用如下center坐标，否则会报错：Pose2D object has no attribute position # 其它版本未验证 # 参考http://docs.ros.org/en/api/vision_msgs/html/msg/BoundingBox2D.html 及 http://docs.ros.org/en/api/geometry_msgs/html/msg/Pose2D.html detection2d.bbox.center.x = center_x detection2d.bbox.center.y = center_y detection2d.bbox.size_x = float(x2-x1) detection2d.bbox.size_y = float(y2-y1) obj_pose = ObjectHypothesisWithPose() obj_pose.id = name obj_pose.score = float(scores[index]) # px2xy world_x, world_y = px2xy( [center_x, center_y], self.camera_info["k"], self.camera_info["d"], 1) obj_pose.pose.pose.position.x = world_x obj_pose.pose.pose.position.y = world_y # obj_pose.pose.pose.position.z = 1.0 #2D相机则显示,归一化后的结果,用户用时自行乘上深度z获取正确xy detection2d.results.append(obj_pose) self.result_msg.detections.append(detection2d) # draw if self.show_result: cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.putText(image, name, (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) cv2.imshow('result', image) cv2.waitKey(1) # if view or pub if self.show_result: cv2.imshow('result', image) cv2.waitKey(1) print("before publish out if") if len(categories) > 0: self.yolo_result_pub.publish(self.result_msg) def main(): rclpy.init() rclpy.spin(YoloV5Ros2()) rclpy.shutdown() if __name__ == "__main__": main()6.编译工作空间
在src外打开终端进行编译 colcon build使用colcon build进行编译src文件夹下的代码
搜寻本地编译 source install/local_setup.bash如果想随时随地使用，打开主目录，使用ctrl+h打开隐藏文件找到.bashrc文件打开，插入一行
7.运行仿真建模 ros2 launch learning_gazebo load_mbot_camera_into_gazebo.launch.py
开启另一个终端 ros2 run yolov5_ros2 yolo_detect_2d --ros-args -p device:=cpu -p image_topic:=/camera/image_raw注：image_topic:=后是相机发布的话题名，将话题名改为相机所发布的话题名一致就能实现目标检测了，效果如下
可以通过开启另一个终端下输入以下命令实现小车的移动 ros2 run teleop_twist_keyboard teleop_twist_keyboard
在上面终端中按i是前进，j是向左旋转视角，l是向右旋转,k是停止移动，,（逗号）为后退。 博客会不定期修改与更新，建议收藏！！！ |
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