sudo apt-get install build-essential
# 必须的，gcc编译环境
?
sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev libavformat-dev libswscale-dev
# 必须的,包括cmake等工具
?
sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev
# 可选的，看个人需要，总共5M左右

1.2 下载opencv 3.4.0（源码）

官网地址：https://opencv.org/releases.html

1.3 编译（时间较长）

cd opencv-3.4.0
mkdir build
# 创建编译文件
cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local ..
# 生成后的Makefile在build文件夹中，而需要的CMakeLists.txt在 ".."中，即上一级文件夹中（opencv-3.4.0）

1.4 安装

# 4个线程编译,查看cpu核数量
# grep "model name" /proc/cpuinfo | wc -l?
make -j4
?
# 安装
sudo make install

1.5 配置opencv路径

#终端中输入

sudo vim /etc/ld.so.conf.d/opencv.conf

在打开的文件内添加：

/usr/local/lib

#终端中输入

sudo ldconfig

sudo vim /etc/bash.bashrc

在打开的文件夹末尾添加：

PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig
export PKG_CONFIG_PATH

#终端中输入

source /etc/bash.bashrc

1.6 测试opencv3.4.0

#上一个终端不用关闭，继续输入

cd ../samples/cpp/example_cmake
cmake .
make
./opencv_example

2. c++使用darknet链接库调用yolov3

2.1 准备工作

下图中，可以看到存在darknet、libdarknet.so两个文件（网上有很多darknet资源）
2.2 在终端创建一个新文件夹yolov3_test

?创建完成后，在文件夹内创建CMakeLists.txt文件，并将以下内容粘贴到文件里。

cmake_minimum_required(VERSION 2.8) # 最低版本需求
project(yolov3) #项目名
#opencv
add_definitions(-std=c++11) 
ADD_DEFINITIONS(-DOPENCV)
ADD_DEFINITIONS(-DGPU)
#########   opencv   #########
set(OpenCV_DIR "/home/zzp/opencv-3.4.0")
find_package( OpenCV REQUIRED )
include_directories( ${OpenCV_INCLUDE_DIRS} )
#########         darknet   #########
include_directories(/home/zzp/darknet-master/include)
find_library(darknet libdarknet.so /home/zzp/darknet-master)
add_executable(${PROJECT_NAME} "/home/zzp/yolov3/yolov3_test.cpp" )
target_link_libraries(${PROJECT_NAME} ${OpenCV_LIBS}  ${darknet})

接下来，在yolov3_test文件夹内新建cfg、img、data、include、weights文件夹，结果如下图所示。?

2.3 丰富yolov3_test

回到darknet-master文件夹，将coco.names、yolov-tiny.cfg放入cfg文件，将dog.jpg放入img，将yolo_v2_class.hpp放入Include，将yolov3-tiny.weights放入weights。

2.4 编写源码

#include <opencv2/opencv.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <vector>
#include <iostream>
#include <fstream>
#include "/home/zzp/yolov3/include/yolo_v2_class.hpp"//引用yolo动态链接库中的头文件

using namespace std;
using namespace cv;

#pragma comment(lib, "yolo_cpp_dll.lib")//引用yolo动态链接库

//画出检测框和相关信息
void DrawBoxes(Mat &frame, vector<string> classes, int classId, float conf, int left, int top, int right, int bottom)
{
	//画检测框
	rectangle(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 (!classes.empty())
	{
		CV_Assert(classId < (int)classes.size());
		label = 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(frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
	putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1);
}

//画出检测结果
void Drawer(Mat &frame, vector<bbox_t> outs, vector<string> classes)
{
	//获取所有最佳检测框信息
	for (int i = 0; i < outs.size(); i++)
	{
		DrawBoxes(frame, classes, outs[i].obj_id, outs[i].prob, outs[i].x, outs[i].y,
			outs[i].x + outs[i].w, outs[i].y + outs[i].h);
	}
}

int main(void)
{
	string classesFile = "/home/zzp/yolov3/cfg/coco.names";
	string modelConfig = "/home/zzp/yolov3/cfg/yolov3-tiny.cfg";
	string modelWeights = "/home/zzp/yolov3/weights/yolov3-tiny.weights";

	//加载类别名
	vector<string> classes;
	ifstream ifs(classesFile.c_str());
	string line;
	while (getline(ifs, line)) classes.push_back(line);
	//加载网络模型，0是指定第一块GPU
	Detector detector(modelConfig, modelWeights, 0);

	string mode = "video";
	//图像
	if (mode == "image")
	{
		Mat frame = imread("/home/zzp/yolov3/img/test.jpeg");
		std::vector<bbox_t> result_vec = detector.detect(frame);
		Drawer(frame, result_vec, classes);
		cv::namedWindow("CV_WINDOW", CV_WINDOW_NORMAL);
		cv::imshow("CV_IMAGE", frame);
		cv::imwrite("/home/zzp/yolov3/img/result.jpg",frame);
		cv::waitKey(0);
	}
	//视频
	else if (mode == "video")
	{
		VideoCapture cap(0);
		Size size(640, 480);
		VideoWriter writer("/home/zzp/yolov3/data/result.avi", cv::VideoWriter::fourcc('M', 'J', 'P', 'G'), 25, size);
		while (1)
		{
			Mat frame;
			cap >> frame;
			if (frame.empty()) 
				break;

			std::vector<bbox_t> result_vec = detector.detect(frame);
			//画图
			Drawer(frame, result_vec, classes);
			cv::imshow("YOLO V3 Camera", frame);
			for(int i = 0; i < result_vec.size(); i++)
			{    
                //检测到人就告知用户此人所在区域
				if(result_vec[i].obj_id == 0)
				{
					cout << "-----------------------------------------------------------------------" << endl;
					cout << "检测到一个人,他(她)位于视频中这个位置: " << endl;
					cout << "起始点(左上):(" << result_vec[i].x << "," << result_vec[i].y << ")"
						<< "\t宽与高:(" << result_vec[i].w << "," << result_vec[i].h << ")." << endl; 
					cout << "-----------------------------------------------------------------------" << endl;
				}
			}
            //ESC退出程序
			if(waitKey(3) == 27) break;
			writer << frame;
		}
		cv::destroyAllWindows();
		cap.release();
	}
    return 0;
}