深度学习目标检测算法发展历程目标检测算法部署

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mob64ca14101b2f 2023-10-18 23:06:16

文章标签 深度学习目标检测算法发展历程 #include 工作空间初始化 文章分类 深度学习人工智能

1 封装darknet框架为dll方式

2 封装TensorFlow或pytorch框架为dll方式

3 调用OpenCV中DNN模块方式

3.0 需求说明

3.1 环境搭建

3.2 创建catkin空间

3.3 创建package

3.4 package中源码

3.4.1 ①main.cpp

3.4.2 ②Detection.h

3.4.3 ③Detection.cpp

3.4.4 ④CMakeLists.txt

3.4.5 ⑤package.xml

3.5 修改ROS系统中cv_bridge模块的配置文件

3.6 重新编译catkin空间

3.7 测试

1 封装darknet框架为dll方式

暂略

2 封装TensorFlow或pytorch框架为dll方式

暂略

3 调用OpenCV中DNN模块方式

3.0 需求说明

在ROS系统下创建一个package目标检测程序，订阅realsense传感器的RGB流，进行行人检测，然后将检测结果信息发布出去。

包和节点名称定义：

该目标检测package名称为：detection_pkg
开展目标检测的节点名称为：yolo4
发布检测结果的节点名称为：yolo4_result

接口定义：

目标检测结果发布的格式为：（string类型）类别序号,置信度,目标框的xmin,目标框的ymin,目标框的width,目标框的height@类别序号,置信度,目标框的xmin,目标框的ymin,目标框的width,目标框的height@............

接口解释：

单帧图像中，可能有多个人，每个检测人框的结果以@分隔。
“人”的类别序号目前设定为0，后续开展其他目标检测时，新增类别序号后续再定。
置信度的取值范围：[0, 1]
重要备注：目前xy的坐标，人在图像边缘情况出现xy为负值情况，该BUG后续将会去解决。

3.1 环境搭建

关于OpenCV4.5.1编译安装、ROS melodic版安装等见我之前的《ubuntu + oepncv + PCL + realsenseSDK + ROS + OpenVino开发环境搭建》
yolov4算法的模型文件、cfg文件、coco.names文件可百度搜索下载，具体文件摆放位置见下文源码。

3.2 创建catkin空间

$ mkdir -p ~/catkin_ws/src　#直接创建了catkin_ws文件夹和src子文件夹。
$ cd ~/catkin_ws/
$ catkin_make #初始化工作空间，此命令必须在工作空间路径下执行。

备注：catkin_ws是工作空间名字，可自定义。
重要备注：创建工作空间后，还需要给工作空间进行注册，注册的目的是让各种命令能够找到这个工作空间（相当于win系统中设定path）。

注册方法1（注册一次爽一次）：每次新建一个终端时，都先执行如下命令告诉此终端我在哪：source ～/catkin_ws/devel/setup.bash #catkin_ws是改你空间名字。
注册方法2（一劳永逸）： (base) ym@ym-2021:~$ echo "source ~/catkin_test/catkin_2/devel/setup.bash" >> ~/.bashrc
(base) ym@ym-2021:~$ source ~/.bashrc

备注：上述catkin_test是我创建的普通文件夹，catkin_2是我创建的Catkin工作空间。

3.3 创建package

先cd到工作空间的src路径内，然后创建package，语法规则是：catkin_create_pkg packageYY dependsXX

其中packageYY是包名，dependsXX是依赖的包名，可以依赖多个软件包。

例如，新建一个package叫做detection_pkg,依赖roscpp、rospy、std_msgs(常用依赖)。

$ catkin_create_pkg detection_pkg roscpp rospy std_msgs

3.4 package中源码

程序结构如下：（在/home/ym/catkin_test/catkin_2/src/detection_pkg 路径下）

①main.cpp（自己的程序）
②Detection.h（自己的程序）
③Detection.cpp（自己的程序）
④CMakeLists.txt（创建package时自动生成的，等下需要改造内容）
⑤package.xml（创建package时自动生成的，等下需要改造内容）
include 文件夹（创建package时自动生成的空文件夹）
src 文件夹（创建package时自动生成的空文件夹）

3.4.1 ①main.cpp

#include "Detection.h"

#include <ros/ros.h>
#include <cv_bridge/cv_bridge.h> //cv_bridge是在ROS图像消息和OpenCV图像之间进行转换的一个功能包。如提供sensor_msgs

#include <opencv2/highgui/highgui.hpp>

#include <iostream>

ros::Publisher pub;

#include "std_msgs/Int8.h"
#include "std_msgs/String.h"

using namespace std;
using namespace cv;
using namespace dnn;

Detection detection = Detection();

void imageCallback(const sensor_msgs::ImageConstPtr &msg)
{
    cv::Mat img;
    cv_bridge::CvImageConstPtr cv_ptr;
    cv_ptr = cv_bridge::toCvShare(msg, "bgr8");
    img = cv_ptr->image;

    detection.Initialize(img.cols, img.rows);
    detection.Detecting(img);

    imshow("Show RgbImage", detection.GetFrame());
    char ch = waitKey(10); // 一定要加这个，不然不显示窗口

    string output = detection.GetResult();

    std_msgs::String out_msg;
    std::stringstream ss;
    ss << output;
    out_msg.data = ss.str();
    pub.publish(out_msg);
}

int main(int argc, char **argv)
{
    //创建node第一步需要用到的函数
    ros::init(argc, argv, "yolo4"); //第3个参数为本节点名，

    //ros::NodeHandle : 和topic、service、param等交互的公共接口
    //创建ros::NodeHandle对象，也就是节点的句柄，它可以用来创建Publisher、Subscriber以及做其他事情。
    //句柄(Handle)这个概念可以理解为一个“把手”，你握住了门把手，就可以很容易把整扇门拉开，而不必关心门是
    //什么样子。NodeHandle就是对节点资源的描述，有了它你就可以操作这个节点了，比如为程序提供服务、监听某个topic上的消息、访问和修改param等等。
    ros::NodeHandle nd; //实例化句柄，初始化node

    // Create a ROS subscriber for the input point cloud
    ros::Subscriber sub = nd.subscribe("/realsense_camera0/color_image", 1, imageCallback); //第二个参数：缓存队列大小，以防我们处理消息的速度不够快
    std::cout << "sub:" << sub << std::endl;

    // Create a ROS publisher for the output
    pub = nd.advertise<std_msgs::String>("yolo4_result", 1);

    ros::spin();

    return 0;
}

3.4.2 ②Detection.h

#pragma once
#ifndef __DETECTION_H__
#define __DETECTION_H__

#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <string.h>
#include <vector>
#include <fstream>

using namespace std;
using namespace cv;
using namespace dnn;

class Detection
{
public:
    //构造、析构函数
    Detection();
    ~Detection();
    //初始化函数
    void Initialize(int width, int height);
    //读取网络模型
    void ReadModel();
    //行人与车辆检测
    bool Detecting(Mat frame);
    //获取网络输出层名称
    vector<String> GetOutputsNames();
    //对输出进行处理，使用NMS选出最合适的框
    void PostProcess();
    //画检测结果
    void Drawer();
    //画出检测框和相关信息
    void DrawBoxes(int classId, float conf, int left, int top, int right, int bottom);
    //获取Mat对象
    Mat GetFrame();
    //获取检测结果字符串
    string GetResult();
    //获取图像宽度
    int GetResWidth();
    //获取图像高度
    int GetResHeight();

private:
    //图像属性
    int m_width;  //图像宽度
    int m_height; //图像高度
    //网络处理相关
    Net m_model;            //网络模型
    Mat m_frame;            //每一帧
    Mat m_blob;             //从每一帧创建一个4D的blob用于网络输入
    vector<Mat> m_outs;     //网络输出
    vector<float> m_confs;  //置信度
    vector<Rect> m_boxes;   //检测框左上角坐标、宽、高
    vector<int> m_classIds; //类别id
    vector<int> m_perfIndx; //非极大阈值处理后边界框的下标
    //检测超参数
    int m_inpWidth;           //网络输入图像宽度
    int m_inpHeight;          //网络输入图像高度
    float m_confThro;         //置信度阈值
    float m_NMSThro;          //NMS非极大抑制阈值
    vector<string> m_classes; //类别名称

private:
    //内存释放
    void Dump();
};

#endif

3.4.3 ③Detection.cpp

#include "Detection.h"

using namespace cv;
using namespace dnn;

//构造函数，成员变量初始化
Detection::Detection()
{
    //图像属性
    m_width = 0;
    m_height = 0;
    m_inpWidth = 416;
    m_inpHeight = 416;

    //其他成员变量
    m_confThro = 0.25;
    m_NMSThro = 0.4;

    //网络模型加载
    ReadModel();
}

//析构函数
Detection::~Detection()
{
    //Dump();
}

//内存释放
void Detection::Dump()
{
    //网络输出相关清零
    m_outs.clear();
    m_boxes.clear();
    m_confs.clear();
    m_classIds.clear();
    m_perfIndx.clear();
}

//初始化函数
void Detection::Initialize(int width, int height)
{
    //图像属性
    m_width = width;
    m_height = height;
}

//读取网络模型和类别
void Detection::ReadModel()
{
    string classesFile = "/data/coco.names";
    String modelConfig = "/data/yolov4.cfg";
    String modelWeights = "/data/yolov4.weights";
    //加载类别名
    ifstream ifs(classesFile.c_str());
    string line;
    while (getline(ifs, line))
        m_classes.push_back(line);
    //加载网络模型
    m_model = readNetFromDarknet(modelConfig, modelWeights);
    m_model.setPreferableBackend(DNN_BACKEND_OPENCV);
    m_model.setPreferableTarget(DNN_TARGET_OPENCL);
}

//检测
bool Detection::Detecting(Mat frame)
{
    Dump(); //各缓存变量的内存释放

    m_frame = frame.clone();

    //创建4D的blob用于网络输入
    blobFromImage(m_frame, m_blob, 1 / 255.0, Size(m_inpWidth, m_inpHeight), Scalar(0, 0, 0), true, false);

    //设置网络输入
    m_model.setInput(m_blob);

    //前向预测得到网络输出，forward需要知道输出层，这里用了一个函数找到输出层
    m_model.forward(m_outs, GetOutputsNames());

    //使用非极大抑制NMS删除置信度较低的边界框
    PostProcess();

    //画检测框
    Drawer();

    return true;
}

//获取网络输出层名称
vector<String> Detection::GetOutputsNames()
{
    static vector<String> names;
    if (names.empty())
    {
        //得到输出层索引号
        vector<int> outLayers = m_model.getUnconnectedOutLayers();

        //得到网络中所有层名称
        vector<String> layersNames = m_model.getLayerNames();

        //获取输出层名称
        names.resize(outLayers.size());
        for (int i = 0; i < outLayers.size(); ++i)
            names[i] = layersNames[outLayers[i] - 1];
    }
    return names;
}

//使用非极大抑制NMS去除置信度较低的边界框
void Detection::PostProcess()
{
    for (int num = 0; num < m_outs.size(); num++)
    {
        Point Position;
        double confidence;

        //得到每个输出的数据
        float *data = (float *)m_outs[num].data;
        for (int j = 0; j < m_outs[num].rows; j++, data += m_outs[num].cols)
        {
            //得到该输出的所有类别的
            Mat scores = m_outs[num].row(j).colRange(5, m_outs[num].cols);

            //获取最大置信度对应的值和位置
            minMaxLoc(scores, 0, &confidence, 0, &Position);

            //对置信度大于阈值的边界框进行相关计算和保存
            if (confidence > m_confThro)
            {
                //data[0],data[1],data[2],data[3]都是相对于原图像的比例
                int centerX = (int)(data[0] * m_width);
                int centerY = (int)(data[1] * m_height);
                int width = (int)(data[2] * m_width);
                int height = (int)(data[3] * m_height);
                int left = centerX - width / 2;
                int top = centerY - height / 2;
                //保存信息
                m_classIds.push_back(Position.x);
                m_confs.push_back((float)confidence);
                m_boxes.push_back(Rect(left, top, width, height));
            }
        }
    }
    //非极大值抑制，以消除具有较低置信度的冗余重叠框
    NMSBoxes(m_boxes, m_confs, m_confThro, m_NMSThro, m_perfIndx);
}

//画出检测结果
void Detection::Drawer()
{
    //获取所有最佳检测框信息
    for (int i = 0; i < m_perfIndx.size(); i++)
    {
        int idx = m_perfIndx[i];
        Rect box = m_boxes[idx];

        // std::cout << "m_classIds[idx]"<< m_classIds[idx] << std::endl;
        // std::cout << "m_confs[idx]"<< m_confs[idx] << std::endl;
        // std::cout << "box.x"<< box.x << std::endl;
        // std::cout << "box.y"<< box.y << std::endl;
        // std::cout << "box.width"<< box.width << std::endl;
        // std::cout << "box.height"<< box.height << std::endl;

        if (m_classIds[idx] == 0) //仅person类
        {
            DrawBoxes(m_classIds[idx], m_confs[idx], box.x, box.y,
                      box.x + box.width, box.y + box.height);
        }
        // DrawBoxes(m_classIds[idx], m_confs[idx], box.x, box.y,
        //           box.x + box.width, box.y + box.height);
    }
}

//画出检测框和相关信息
void Detection::DrawBoxes(int classId, float conf, int left, int top, int right, int bottom)
{
    //画检测框
    rectangle(m_frame, Point(left, top), Point(right, bottom), Scalar(255, 178, 50), 3);

    //该检测框对应的类别和置信度
    string label = format("%.2f", conf);
    if (!m_classes.empty())
    {
        CV_Assert(classId < (int)m_classes.size());
        label = m_classes[classId] + ":" + label;
    }

    //将标签显示在检测框顶部
    int baseLine;
    Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
    top = max(top, labelSize.height);
    rectangle(m_frame, Point(left, top - round(1.5 * labelSize.height)), Point(left + round(1.5 * labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
    putText(m_frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1);
}

//获取Mat对象
Mat Detection::GetFrame()
{
    return m_frame;
}

//返回检测结果
string Detection::GetResult()
{
    string result = "";

    //获取所有最佳检测框信息
    for (int i = 0; i < m_perfIndx.size(); i++)
    {
        int idx = m_perfIndx[i];
        Rect box = m_boxes[idx];

        // std::cout << "m_classIds[idx]"<< m_classIds[idx] << std::endl;
        // std::cout << "m_confs[idx]"<< m_confs[idx] << std::endl;
        // std::cout << "box.x"<< box.x << std::endl;
        // std::cout << "box.y"<< box.y << std::endl;
        // std::cout << "box.width"<< box.width << std::endl;
        // std::cout << "box.height"<< box.height << std::endl;

        if (m_classIds[idx] == 0) //仅person类
        {
            stringstream strStream;
            //int、float类型都可以塞到stringstream中
            strStream << m_classIds[idx] << "," << m_confs[idx] << "," << box.x << "," << box.y << "," << box.width << "," << box.height;
            result = strStream.str();
        }
    }

    return result;
}

//获取结果图像宽度
int Detection::GetResWidth()
{
    return m_width;
}

//获取结果图像高度
int Detection::GetResHeight()
{
    return m_height;
}

3.4.4 ④CMakeLists.txt

这部分主要新增如下几点：

开头几行find_package中新增“cv_bridge”
新增OpenCV部分

cmake_minimum_required(VERSION 3.0.2)
project(detection_pkg)

## Compile as C++11, supported in ROS Kinetic and newer
# add_compile_options(-std=c++11)

## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(catkin REQUIRED COMPONENTS
  roscpp
  rospy
  std_msgs
  cv_bridge
)

## System dependencies are found with CMake's conventions
# find_package(Boost REQUIRED COMPONENTS system)


## Uncomment this if the package has a setup.py. This macro ensures
## modules and global scripts declared therein get installed
## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
# catkin_python_setup()

################################################
## Declare ROS messages, services and actions ##
################################################

## To declare and build messages, services or actions from within this
## package, follow these steps:
## * Let MSG_DEP_SET be the set of packages whose message types you use in
##   your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
## * In the file package.xml:
##   * add a build_depend tag for "message_generation"
##   * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
##   * If MSG_DEP_SET isn't empty the following dependency has been pulled in
##     but can be declared for certainty nonetheless:
##     * add a exec_depend tag for "message_runtime"
## * In this file (CMakeLists.txt):
##   * add "message_generation" and every package in MSG_DEP_SET to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * add "message_runtime" and every package in MSG_DEP_SET to
##     catkin_package(CATKIN_DEPENDS ...)
##   * uncomment the add_*_files sections below as needed
##     and list every .msg/.srv/.action file to be processed
##   * uncomment the generate_messages entry below
##   * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)

## Generate messages in the 'msg' folder
# add_message_files(
#   FILES
#   Message1.msg
#   Message2.msg
# )

## Generate services in the 'srv' folder
# add_service_files(
#   FILES
#   Service1.srv
#   Service2.srv
# )

## Generate actions in the 'action' folder
# add_action_files(
#   FILES
#   Action1.action
#   Action2.action
# )

## Generate added messages and services with any dependencies listed here
# generate_messages(
#   DEPENDENCIES
#   std_msgs
# )

################################################
## Declare ROS dynamic reconfigure parameters ##
################################################

## To declare and build dynamic reconfigure parameters within this
## package, follow these steps:
## * In the file package.xml:
##   * add a build_depend and a exec_depend tag for "dynamic_reconfigure"
## * In this file (CMakeLists.txt):
##   * add "dynamic_reconfigure" to
##     find_package(catkin REQUIRED COMPONENTS ...)
##   * uncomment the "generate_dynamic_reconfigure_options" section below
##     and list every .cfg file to be processed

## Generate dynamic reconfigure parameters in the 'cfg' folder
# generate_dynamic_reconfigure_options(
#   cfg/DynReconf1.cfg
#   cfg/DynReconf2.cfg
# )

###################################
## catkin specific configuration ##
###################################
## The catkin_package macro generates cmake config files for your package
## Declare things to be passed to dependent projects
## INCLUDE_DIRS: uncomment this if your package contains header files
## LIBRARIES: libraries you create in this project that dependent projects also need
## CATKIN_DEPENDS: catkin_packages dependent projects also need
## DEPENDS: system dependencies of this project that dependent projects also need
catkin_package(
#  INCLUDE_DIRS include
#  LIBRARIES detection_pkg
#  CATKIN_DEPENDS roscpp rospy std_msgs
#  DEPENDS system_lib
)

###########
## Build ##
###########

## Specify additional locations of header files
## Your package locations should be listed before other locations
include_directories(
# include
  ${catkin_INCLUDE_DIRS}
)

## Declare a C++ library
# add_library(${PROJECT_NAME}
#   src/${PROJECT_NAME}/detection_pkg.cpp
# )

## Add cmake target dependencies of the library
## as an example, code may need to be generated before libraries
## either from message generation or dynamic reconfigure
# add_dependencies(${PROJECT_NAME} ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Declare a C++ executable
## With catkin_make all packages are built within a single CMake context
## The recommended prefix ensures that target names across packages don't collide
# add_executable(${PROJECT_NAME}_node src/detection_pkg_node.cpp)

## Rename C++ executable without prefix
## The above recommended prefix causes long target names, the following renames the
## target back to the shorter version for ease of user use
## e.g. "rosrun someones_pkg node" instead of "rosrun someones_pkg someones_pkg_node"
# set_target_properties(${PROJECT_NAME}_node PROPERTIES OUTPUT_NAME node PREFIX "")

## Add cmake target dependencies of the executable
## same as for the library above
# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})

## Specify libraries to link a library or executable target against
# target_link_libraries(${PROJECT_NAME}_node
#   ${catkin_LIBRARIES}
# )

#############
## Install ##
#############

# all install targets should use catkin DESTINATION variables
# See http://ros.org/doc/api/catkin/html/adv_user_guide/variables.html

## Mark executable scripts (Python etc.) for installation
## in contrast to setup.py, you can choose the destination
# catkin_install_python(PROGRAMS
#   scripts/my_python_script
#   DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark executables for installation
## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_executables.html
# install(TARGETS ${PROJECT_NAME}_node
#   RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
# )

## Mark libraries for installation
## See http://docs.ros.org/melodic/api/catkin/html/howto/format1/building_libraries.html
# install(TARGETS ${PROJECT_NAME}
#   ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
#   RUNTIME DESTINATION ${CATKIN_GLOBAL_BIN_DESTINATION}
# )

## Mark cpp header files for installation
# install(DIRECTORY include/${PROJECT_NAME}/
#   DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
#   FILES_MATCHING PATTERN "*.h"
#   PATTERN ".svn" EXCLUDE
# )

## Mark other files for installation (e.g. launch and bag files, etc.)
# install(FILES
#   # myfile1
#   # myfile2
#   DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
# )

#############
## Testing ##
#############

## Add gtest based cpp test target and link libraries
# catkin_add_gtest(${PROJECT_NAME}-test test/test_detection_pkg.cpp)
# if(TARGET ${PROJECT_NAME}-test)
#   target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
# endif()

## Add folders to be run by python nosetests
# catkin_add_nosetests(test)


#此句要在下面target_link_libraries语句之前！！！！！
set(DETECTION Detection.cpp)
add_executable(yolo4 main.cpp ${DETECTION})


# opencv opencv opencv opencv opencv opencv 
#寻找opencv库
find_package(OpenCV REQUIRED)
#添加头文件
include_directories(${OpenCV_INCLUDE_DIRS})
#链接Opencv库
target_link_libraries(yolo4 ${OpenCV_LIBS} )


target_link_libraries(yolo4 ${catkin_LIBRARIES})

3.4.5 ⑤package.xml

这里主要是新增一个“cv_bridge”字段

<?xml version="1.0"?>
<package format="2">
  <name>detection_pkg</name>
  <version>0.0.0</version>
  <description>The detection_pkg package</description>

  <!-- One maintainer tag required, multiple allowed, one person per tag -->
  <!-- Example:  -->
  <!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
  <maintainer email="ym@todo.todo">ym</maintainer>


  <!-- One license tag required, multiple allowed, one license per tag -->
  <!-- Commonly used license strings: -->
  <!--   BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
  <license>TODO</license>


  <!-- Url tags are optional, but multiple are allowed, one per tag -->
  <!-- Optional attribute type can be: website, bugtracker, or repository -->
  <!-- Example: -->
  <!-- <url type="website">http://wiki.ros.org/detection_pkg</url> -->


  <!-- Author tags are optional, multiple are allowed, one per tag -->
  <!-- Authors do not have to be maintainers, but could be -->
  <!-- Example: -->
  <!-- <author email="jane.doe@example.com">Jane Doe</author> -->


  <!-- The *depend tags are used to specify dependencies -->
  <!-- Dependencies can be catkin packages or system dependencies -->
  <!-- Examples: -->
  <!-- Use depend as a shortcut for packages that are both build and exec dependencies -->
  <!--   <depend>roscpp</depend> -->
  <!--   Note that this is equivalent to the following: -->
  <!--   <build_depend>roscpp</build_depend> -->
  <!--   <exec_depend>roscpp</exec_depend> -->
  <!-- Use build_depend for packages you need at compile time: -->
  <!--   <build_depend>message_generation</build_depend> -->
  <!-- Use build_export_depend for packages you need in order to build against this package: -->
  <!--   <build_export_depend>message_generation</build_export_depend> -->
  <!-- Use buildtool_depend for build tool packages: -->
  <!--   <buildtool_depend>catkin</buildtool_depend> -->
  <!-- Use exec_depend for packages you need at runtime: -->
  <!--   <exec_depend>message_runtime</exec_depend> -->
  <!-- Use test_depend for packages you need only for testing: -->
  <!--   <test_depend>gtest</test_depend> -->
  <!-- Use doc_depend for packages you need only for building documentation: -->
  <!--   <doc_depend>doxygen</doc_depend> -->
  <buildtool_depend>catkin</buildtool_depend>
  <build_depend>roscpp</build_depend>
  <build_depend>rospy</build_depend>
  <build_depend>std_msgs</build_depend>
  <build_depend>cv_bridge</build_depend>
  <build_export_depend>roscpp</build_export_depend>
  <build_export_depend>rospy</build_export_depend>
  <build_export_depend>std_msgs</build_export_depend>
  <build_export_depend>cv_bridge</build_export_depend>
  <exec_depend>roscpp</exec_depend>
  <exec_depend>rospy</exec_depend>
  <exec_depend>std_msgs</exec_depend>
  <exec_depend>cv_bridge</exec_depend>


  <!-- The export tag contains other, unspecified, tags -->
  <export>
    <!-- Other tools can request additional information be placed here -->

  </export>
</package>

3.5 修改ROS系统中cv_bridge模块的配置文件

因为ROS系统中默认安装一个低版本OpenCV，导致有些函数无法正常使用而报错，需要修改ROS系统中cv_bridge模块对OpenCV的引用，让它引用自己安装的4.5.1版本OpenCV。

参考：《Ubuntu18.04+ROS melodic+opencv4 修改cv_bridge配置文件，解决ROS使用不同版本opencv的冲突问题》

操作步骤：

首先找到cv_brige文件，使用gedit打开：

cd /opt/ros/melodic/share/cv_bridge/cmake/
sudo gedit cv_bridgeConfig.cmake

将此处指定的头文件目录改到自己安装的头文件目录下：
之后再将指定库文件也修改为自己安装的opencv库文件：

3.6 重新编译catkin空间

先cd到工作空间路径（注意不是package路径），比如我的是：/home/ym/catkin_test/catkin_2
然后终端中执行：catkin_make

按照流程走编译一般没问题，有其他小问题应该也好自己解决。

BUG备注：

make[2]: *** No rule to make target '/usr/local/lib/libopencv_hiqhgui.so.4.5.1', needed by '/home/moi/yinming/catkin_ym/devel/lib/detection_pkg/yolo4'
原因打错字：libopencv_hiqhgui.so.4.5.1应该写成libopencv_highgui.so.4.5.1

3.7 测试

测试流程简述：使用在ros系统中预先录制的realsense传感器的bag数据，通过rosbag play这个bag历史数据，然后我们这个目标检测package程序去订阅里面的RGB流，再将检测结果发布出来。

整个流程操作命令如下（顺序不能变）：

新建第1个终端，直接执行：roscore
新建第2个终端，cd到bag包的存放路径，执行：rosbag play 2021-04-27-15-59-19.bag
新建第3个终端，直接执行：rosrun detection_pkg yolo4
新建第4个终端，直接执行：rostopic echo yolo4_result

结果如下：

深度学习目标检测算法发展历程目标检测算法部署_#include

问题备注：

在真机上编译通过，运行节点时，如果报如下错：

what(): OpenCV(4.5.1) /home/moi/yinming/OpenCV/opencv-4.5.1/modules/highgui/src/window_gtk.cpp:624: error: (-2:Unspecified error) Can't initialize GTK backend in function 'cvInitSystem'
原因是：笔记本VScode远程接入机器的Ubuntu系统，没有可视化界面，需要注释main.py中可视化的两行代码。

imshow("Show RgbImage", detection.GetFrame());
char ch = waitKey(10);

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