/ros-yolov5

YOLO ROS: Real-Time Object Detection for ROS # ros2yolo yolo2ros ros yolov5

Primary LanguageMakefile

README

环境:ubuntu18.04 + ros-melodic + python3.8 + pytorch1.0+

编译依赖:catkin_simple_pkg 下载放入同一个rosws下catkin_make 编译

此外

ROS Service

实现yolo作为service的server,发送sensor::Imge 图片,得到yolo检测的结果(置信度,2dbbox左上右下点坐标,附加文本信息,分类结果), 使用步骤如下:

  1. 依赖安装

    1. 安装好pytorch
    2. catkin-simple放入本包同一工程下

  2. 填写config/config.demo中参数,其中

    1. weight为保存的pytorch模型.pt文件 ,
    2. img_size为图片大小,必须和训练时的设置一致
    3. action 为选择使用action模式还是service模式来使用ros2yolo功能

  3. demo_server.py开头#!/home/ou/software/anaconda3/envs/dl/bin/python换为自己装有pytorch的虚拟环境下的解释器

    1. 设置脚本为可执行权限
    2. 运行launch/service_demo.launch 启动service-server

  4. 参考src/service_client_demo.cpp

    #include <cv_bridge/cv_bridge.h>
#include <ros_yolo/yolo.h>

//instance of server-client
ros::ServiceClient client = n.serviceClient<ros_yolo::yolo>("yolo_service");
//request once
ros_yolo::yolo srv;
srv.request.image =  //sensor_msgs::Image  variable
//send request
 if (client.call(srv)) {
	ROS_INFO("request succeed");
     // analyze each result
	for (auto &result:srv.response.results){
        auto prob = result.prob;  // probability of this classification
        auto xyxy = result.bbox.xyxy;  // coordinates of the upper left corner and the lower right corner
        auto text = result.label;  // addtional infomation 
        auto cls = result.id;  // classification result 
        // ....
    }
}else {
	ROS_ERROR("request fail");
}

ROS Action

填写config/config.demo中参数 action=True

action-client 参考src/action_client_demo.cpp

action_client发出的goal只需包含iamge即goal.image = //sensor_msgs::Image

action_client接受到的结果result包含原始图像msg和检测结果即result->image 和 result->result