在JetsonNano上编译OpenCV源码与OpenCV C++ YOLOv5程序演示

编译OpenCV最新4.5.x版本

Jetson Nano自带的OpenCV版本比较低，Jetpack4.6对应的OpenCV版本为4.1的，有图为证：

而OpenCV当前最新版本已经到了4.5跟4.6了，4.5.x中OpenCV DNN支持了很多新的模型推理跟新的特性都无法在OpenCV4.1上演示，所以我决定从源码编译OpenCV升级版本到 4.5.4，然后我发一个非常好的网站，提供了完整的脚本，于是我直接运行了该脚本就完成了安装，整个安装过程需要等待几个小时，耐心点。这个完整的脚本下载地址如下：

https://github.com/Qengineering/Install-OpenCV-Jetson-Nano

https://qengineering.eu/install-opencv-4.5-on-jetson-nano.html

#!/bin/bash
set-e

echo"InstallingOpenCV4.5.4onyourJetsonNano"
echo"Itwilltake2.5hours!"

#revealtheCUDAlocation
cd~
sudosh-c"echo'/usr/local/cuda/lib64'>>/etc/ld.so.conf.d/nvidia-tegra.conf"
sudoldconfig

#installthedependencies
sudoapt-getinstall-ybuild-essentialcmakegitunzippkg-configzlib1g-dev
sudoapt-getinstall-ylibjpeg-devlibjpeg8-devlibjpeg-turbo8-devlibpng-devlibtiff-dev
sudoapt-getinstall-ylibavcodec-devlibavformat-devlibswscale-devlibglew-dev
sudoapt-getinstall-ylibgtk2.0-devlibgtk-3-devlibcanberra-gtk*
sudoapt-getinstall-ypython-devpython-numpypython-pip
sudoapt-getinstall-ypython3-devpython3-numpypython3-pip
sudoapt-getinstall-ylibxvidcore-devlibx264-devlibgtk-3-dev
sudoapt-getinstall-ylibtbb2libtbb-devlibdc1394-22-devlibxine2-dev
sudoapt-getinstall-ygstreamer1.0-toolslibv4l-devv4l-utilsqv4l2
sudoapt-getinstall-ylibgstreamer-plugins-base1.0-devlibgstreamer-plugins-good1.0-dev
sudoapt-getinstall-ylibavresample-devlibvorbis-devlibxine2-devlibtesseract-dev
sudoapt-getinstall-ylibfaac-devlibmp3lame-devlibtheora-devlibpostproc-dev
sudoapt-getinstall-ylibopencore-amrnb-devlibopencore-amrwb-dev
sudoapt-getinstall-ylibopenblas-devlibatlas-base-devlibblas-dev
sudoapt-getinstall-yliblapack-devliblapacke-devlibeigen3-devgfortran
sudoapt-getinstall-ylibhdf5-devprotobuf-compiler
sudoapt-getinstall-ylibprotobuf-devlibgoogle-glog-devlibgflags-dev

#removeoldversionsorpreviousbuilds
cd~
sudorm-rfopencv*
#downloadthelatestversion
wget-Oopencv.ziphttps://github.com/opencv/opencv/archive/4.5.4.zip
wget-Oopencv_contrib.ziphttps://github.com/opencv/opencv_contrib/archive/4.5.4.zip
#unpack
unzipopencv.zip
unzipopencv_contrib.zip
#someadministrationtomakeliveeasierlateron
mvopencv-4.5.4opencv
mvopencv_contrib-4.5.4opencv_contrib
#cleanupthezipfiles
rmopencv.zip
rmopencv_contrib.zip

#setinstalldir
cd~/opencv
mkdirbuild
cdbuild

#runcmake
cmake-DCMAKE_BUILD_TYPE=RELEASE
-DCMAKE_INSTALL_PREFIX=/usr
-DOPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules
-DEIGEN_INCLUDE_PATH=/usr/include/eigen3
-DWITH_OPENCL=OFF
-DWITH_CUDA=ON
-DCUDA_ARCH_BIN=5.3
-DCUDA_ARCH_PTX=""
-DWITH_CUDNN=ON
-DWITH_CUBLAS=ON
-DENABLE_FAST_MATH=ON
-DCUDA_FAST_MATH=ON
-DOPENCV_DNN_CUDA=ON
-DENABLE_NEON=ON
-DWITH_QT=OFF
-DWITH_OPENMP=ON
-DBUILD_TIFF=ON
-DWITH_FFMPEG=ON
-DWITH_GSTREAMER=ON
-DWITH_TBB=ON
-DBUILD_TBB=ON
-DBUILD_TESTS=OFF
-DWITH_EIGEN=ON
-DWITH_V4L=ON
-DWITH_LIBV4L=ON
-DOPENCV_ENABLE_NONFREE=ON
-DINSTALL_C_EXAMPLES=OFF
-DINSTALL_PYTHON_EXAMPLES=OFF
-DBUILD_opencv_python3=TRUE
-DOPENCV_GENERATE_PKGCONFIG=ON
-DBUILD_EXAMPLES=OFF..

#runmake
FREE_MEM="$(free-m|awk'/^Swap/{print$2}')"
#Use"-j4"onlyswapspaceislargerthan5.5GB
if[["FREE_MEM"-gt"5500"]];then
NO_JOB=4
else
echo"Duetolimitedswap,makeonlyuses1core"
NO_JOB=1
fi
make-j${NO_JOB}

sudorm-r/usr/include/opencv4/opencv2
sudomakeinstall
sudoldconfig

#cleaning(frees300MB)
makeclean
sudoapt-getupdate

echo"Congratulations!"
echo"You'vesuccessfullyinstalledOpenCV4.5.4onyourJetsonNano"

直接在终端命令行中执行下载下来得脚本文件就可以完成安装了。我安装完整之后得显示如下：

验证与导入安装好之后的OpenCV4.5.4版本

OpenCV C++程序编译与演示

OpenCV YOLOv5跟人脸检测的演示C++程序是我以前写好的，直接拿过来，然后构建了一个项目目录如下：

拷贝到jetson的home目录下，cmake编译

然后make生成执行文件：

运行target

显示运行界面如下：

OpenCV DNN人脸检测演示：

CMakeLists.txt文件里面得内容如下：

cmake_minimum_required(VERSION2.8)

#声明一个cmake工程
project(yolov5_opencv_demo)

#设置编译模式
#set(CMAKE_BUILD_TYPE"Debug")

#添加OPENCV库
#指定OpenCV版本，代码如下
#find_package(OpenCV4.5.4REQUIRED)
#如果不需要指定OpenCV版本，代码如下
find_package(OpenCVREQUIRED)

include_directories(
./src/)


#添加OpenCV头文件
include_directories(${OpenCV_INCLUDE_DIRS})

#显示OpenCV_INCLUDE_DIRS的值
message(${OpenCV_INCLUDE_DIRS})

FILE(GLOB_RECURSETEST_SRC
src/*.cpp
)

#添加一个可执行程序
#语法：add_executable(程序名源代码文件）
add_executable(targetyolov5_opencv.cpp${TEST_SRC})

#将库文件链接到可执行程序上
target_link_libraries(target${OpenCV_LIBS})

OpenCV+ YOLOv5，CUDA加速支持的源码

#include
#include
#include

std::stringlabel_map="classes.txt";
intmain(intargc,char**argv){
std::vectorclassNames;
std::ifstreamfp(label_map);
std::stringname;
while(!fp.eof()){
getline(fp,name);
if(name.length()){
classNames.push_back(name);
}
}
fp.close();
std::vectorcolors;
colors.push_back(cv::Scalar(0,255,0));
colors.push_back(cv::Scalar(0,255,255));
colors.push_back(cv::Scalar(255,255,0));
colors.push_back(cv::Scalar(255,0,0));
colors.push_back(cv::Scalar(0,0,255));

std::stringonnxpath="yolov5s.onnx";
autonet=cv::readNetFromONNX(onnxpath);
net.setPreferableBackend(cv::DNN_BACKEND_CUDA);
net.setPreferableTarget(cv::DNN_TARGET_CUDA);
cv::VideoCapturecapture("example_dsh.mp4");
cv::Matframe;
while(true){
boolret=capture.read(frame);
if(frame.empty()){
break;
}
int64start=cv::getTickCount();
//图象预处理-格式化操作
intw=frame.cols;
inth=frame.rows;
int_max=std::max(h,w);
cv::Matimage=cv::Size(_max,_max),CV_8UC3);
cv::Rectroi(0,0,w,h);
frame.copyTo(image(roi));

floatx_factor=image.cols/640.0f;
floaty_factor=image.rows/640.0f;

//推理
cv::Matblob=cv::blobFromImage(image,1/255.0,cv::Size(640,640),cv::Scalar(0,0,0),true,false);
net.setInput(blob);
cv::Matpreds=net.forward();

//后处理,1x25200x85
cv::Matdet_output(preds.size[1],preds.size[2],CV_32F,preds.ptr());
floatconfidence_threshold=0.5;
std::vectorboxes;
std::vectorclassIds;
std::vectorconfidences;
for(inti=0;i< det_output.rows; i++) {
            float confidence = det_output.at(i,4);
if(confidence< 0.25) {
                continue;
            }
            cv::Mat classes_scores = det_output.row(i).colRange(5, preds.size[2]);
            cv::Point classIdPoint;
            double score;
            minMaxLoc(classes_scores, 0, &score, 0, &classIdPoint);

            // 置信度 0～1之间
            if (score >0.25)
{
floatcx=det_output.at(i,0);
floatcy=det_output.at(i,1);
floatow=det_output.at(i,2);
floatoh=det_output.at(i,3);
intx=static_cast((cx-0.5*ow)*x_factor);
inty=static_cast((cy-0.5*oh)*y_factor);
intwidth=static_cast(ow*x_factor);
intheight=static_cast(oh*y_factor);
cv::Rectbox;
box.x=x;
box.y=y;
box.width=width;
box.height=height;

boxes.push_back(box);
classIds.push_back(classIdPoint.x);
confidences.push_back(score);
}
}

//NMS
std::vectorindexes;
cv::NMSBoxes(boxes,confidences,0.25,0.50,indexes);
for(size_ti=0;i< indexes.size(); i++) {
            int index = indexes[i];
            int idx = classIds[index];
            cv::rectangle(frame, boxes[index], colors[idx%5], 2, 8);
            cv::rectangle(frame, cv::Point(boxes[index].tl().x, boxes[index].tl().y - 20),
                cv::Point(boxes[index].br().x, boxes[index].tl().y), cv::Scalar(255, 255, 255), -1);
            cv::putText(frame, classNames[idx], cv::Point(boxes[index].tl().x, boxes[index].tl().y - 10), cv::FONT_HERSHEY_SIMPLEX, .5, cv::Scalar(0, 0, 0));
        }

        float t = (cv::getTickCount() - start) / static_cast(cv::getTickFrequency());
putText(frame,cv::format("FPS:%.2f",1.0/t),cv::Point(20,40),cv::FONT_HERSHEY_PLAIN,2.0,cv::Scalar(255,0,0),2,8);

charc=cv::waitKey(1);
if(c==27){
break;
}
cv::imshow("OpenCV4.5.4CUDA+YOLOv5",frame);
}
cv::waitKey(0);
cv::destroyAllWindows();
return0;
}

审核编辑：汤梓红