ubuntu20.04环境下将yolov3部署到ros中，并实现cuda加速

1,先查看是否安装有cuda,我这里是cuda10.1，若没有，直接sudo apt-get下载就行

nvcc --version

2，官网下载对应版本的cudnn，需要注册cuDNN Archive | NVIDIA Developer

3，进入src目录下，下载darknet_ros包，链接如下GitHub - leggedrobotics/darknet_ros: YOLO ROS: Real-Time Object Detection for ROS

git clone https://github.com/leggedrobotics/darknet_ros.git

4，克隆下来可能darknet文件夹是空的，参考解决方法如下：

darknet_ros编译错误CMake Error at /usr/share/cmake-3.5/Modules/FindCUDA.cmake:1649问题解决办法_Timothy_Liuu的博客-优快云博客

也可以直接删除darknet文件，cd到功能包下重新下载

ubuntu20 ros darknet 安装记录_啵啵大的博客-优快云博客

git clone https://github.com/pjreddie/darknet
cd darknet
make

5，开始编译！

catkin_make -DCMAKE_BUILD_TYPE=Release

6，报错gcc版本应该小于8，解决方法如下：先安装旧版本的，再修改试用版本

CUDA编译 unsupported GNU version! gcc versions later than 8 are not supported_gcc later than 8_进程中的博客-优快云博客

sudo apt-get install g++-8
sudo apt-get install gcc-8

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-8 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-8 20

7，报错‘IplImage’ does not name a type； did you mean ‘image’

解决方法参考如下：处理：‘IplImage’ does not name a type； did you mean ‘image’?_许野平的博客-优快云博客

替换darknet/src/imageopencv.cpp的文件如下：

#ifdef OPENCV

#include "stdio.h"
#include "stdlib.h"
#include "opencv2/opencv.hpp"
#include "image.h"

using namespace cv;

extern "C" {

/*IplImage *image_to_ipl(image im)
{
    int x,y,c;
    IplImage *disp = cvCreateImage(cvSize(im.w,im.h), IPL_DEPTH_8U, im.c);
    int step = disp->widthStep;
    for(y = 0; y < im.h; ++y){
        for(x = 0; x < im.w; ++x){
            for(c= 0; c < im.c; ++c){
                float val = im.data[c*im.h*im.w + y*im.w + x];
                disp->imageData[y*step + x*im.c + c] = (unsigned char)(val*255);
            }
        }
    }
    return disp;
}

image ipl_to_image(IplImage* src)
{
    int h = src->height;
    int w = src->width;
    int c = src->nChannels;
    image im = make_image(w, h, c);
    unsigned char *data = (unsigned char *)src->imageData;
    int step = src->widthStep;
    int i, j, k;

    for(i = 0; i < h; ++i){
        for(k= 0; k < c; ++k){
            for(j = 0; j < w; ++j){
                im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
            }
        }
    }
    return im;
}*/

/*Mat image_to_mat(image im)
{
    image copy = copy_image(im);
    constrain_image(copy);
    if(im.c == 3) rgbgr_image(copy);

    IplImage *ipl = image_to_ipl(copy);
    Mat m = cvarrToMat(ipl, true);
    cvReleaseImage(&ipl);
    free_image(copy);
    return m;
}

image mat_to_image(Mat m)
{
    IplImage ipl = m;
    image im = ipl_to_image(&ipl);
    rgbgr_image(im);
    return im;
}*/

Mat image_to_mat(image im)
{
    image copy = copy_image(im);
    constrain_image(copy);
    if(im.c == 3) rgbgr_image(copy);

    Mat m(cv::Size(im.w,im.h), CV_8UC(im.c));
    int x,y,c;

    int step = m.step;
    for(y = 0; y < im.h; ++y){
        for(x = 0; x < im.w; ++x){
            for(c= 0; c < im.c; ++c){
                float val = im.data[c*im.h*im.w + y*im.w + x];
                m.data[y*step + x*im.c + c] = (unsigned char)(val*255);
            }
        }
    }

    free_image(copy);
    return m;

// free_image(copy);
// return m;
//     IplImage *ipl = image_to_ipl(copy);
//     Mat m = cvarrToMat(ipl, true);
//     cvReleaseImage(&ipl);
//     free_image(copy);
//     return m;
}

image mat_to_image(Mat m)
{
    int h = m.rows;
    int w = m.cols;
    int c = m.channels();
    image im = make_image(w, h, c);
    unsigned char *data = (unsigned char *)m.data;
    int step = m.step;
    int i, j, k;

    for(i = 0; i < h; ++i){
        for(k= 0; k < c; ++k){
            for(j = 0; j < w; ++j){
                im.data[k*w*h + i*w + j] = data[i*step + j*c + k]/255.;
            }
        }
    }
    rgbgr_image(im);
    return im;
    // IplImage ipl = m;
    // image im = ipl_to_image(&ipl);
    // rgbgr_image(im);
    // return im;
}

void *open_video_stream(const char *f, int c, int w, int h, int fps)
{
    VideoCapture *cap;
    if(f) cap = new VideoCapture(f);
    else cap = new VideoCapture(c);
    if(!cap->isOpened()) return 0;
    //if(w) cap->set(CV_CAP_PROP_FRAME_WIDTH, w);
    //if(h) cap->set(CV_CAP_PROP_FRAME_HEIGHT, w);
    //if(fps) cap->set(CV_CAP_PROP_FPS, w);
    if(w) cap->set(CAP_PROP_FRAME_WIDTH, w);
    if(h) cap->set(CAP_PROP_FRAME_HEIGHT, w);
    if(fps) cap->set(CAP_PROP_FPS, w);
    return (void *) cap;
}

image get_image_from_stream(void *p)
{
    VideoCapture *cap = (VideoCapture *)p;
    Mat m;
    *cap >> m;
    if(m.empty()) return make_empty_image(0,0,0);
    return mat_to_image(m);
}

image load_image_cv(char *filename, int channels)
{
    int flag = -1;
    if (channels == 0) flag = -1;
    else if (channels == 1) flag = 0;
    else if (channels == 3) flag = 1;
    else {
        fprintf(stderr, "OpenCV can't force load with %d channels\n", channels);
    }
    Mat m;
    m = imread(filename, flag);
    if(!m.data){
        fprintf(stderr, "Cannot load image \"%s\"\n", filename);
        char buff[256];
        sprintf(buff, "echo %s >> bad.list", filename);
        system(buff);
        return make_image(10,10,3);
        //exit(0);
    }
    image im = mat_to_image(m);
    return im;
}

int show_image_cv(image im, const char* name, int ms)
{
    Mat m = image_to_mat(im);
    imshow(name, m);
    int c = waitKey(ms);
    if (c != -1) c = c%256;
    return c;
}

void make_window(char *name, int w, int h, int fullscreen)
{
    namedWindow(name, WINDOW_NORMAL); 
    if (fullscreen) {
        //setWindowProperty(name, CV_WND_PROP_FULLSCREEN, CV_WINDOW_FULLSCREEN);
        setWindowProperty(name, WND_PROP_FULLSCREEN, WINDOW_FULLSCREEN);
    } else {
        resizeWindow(name, w, h);
        if(strcmp(name, "Demo") == 0) moveWindow(name, 0, 0);
    }
}

}

#endif

8，至此功能包的编译已经基本完成了，接下来调用电脑摄像头测试一下

        首先下载摄像头驱动

sudo apt-get install ros-noetic-usb-cam

        然后启动摄像头节点

roslaunch usb_cam usb_cam-test.launch

        在darknet_ros.launch修改话题名，与摄像头节点发布的一致

9，最后一步，开始检测！

roslaunch darknet_ros darknet_ros.launch

 成功！

以下是记事本：

更换cuda版本：

Linux下安装cuda和对应版本的cudnn_linux 安装cuda和cudnn_水哥很水的博客-优快云博客

配置opencv，yolo，cuda等

https://www.cnblogs.com/chua-n/p/13208414.html

Ubuntu20.04+cuda11.1+yolo3 目标检测 深度学习系统 真正从0搭建 包含各种可能遇到的错误_bitmap中有标记为已使用的未用簇_Sliverp的博客-优快云博客

本文仅用于记录学习过程，后续可能用到的和已经参考到的其它文章如下：

ROS下实现darknet_ros(YOLO V3)检测_yolov3怎么定位坐标_pd很不专业的博客-优快云博客

在ROS中实现darknet_ros(YOLO V3)检测以及训练自己的数据集_矩池云训练yolo_马文茂的博客-优快云博客

darknet_ros部署及测试_哪来那么多热情^^的博客-优快云博客

darknet_ros加速--使用GPU和CUDA_ros如何使用cuda_鲁米内特的博客-优快云博客

darknet_ros +yolov3+Basler camera 实时目标检测环境配置（Ubuntu16.04+ROS kinetic）_禁用nouveau驱动无法进入系统_是锋不是峰啊的博客-优快云博客