linux C++ onnxruntime yolov8 视频检测Demo

目录

项目目录

效果

​编辑CMakeLists.txt

代码 

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---

项目目录

效果

./yolov8_demo --help

./yolov8_demo -c=2 -p=true

./yolov8_demo -c=1 -s=true

CMakeLists.txt

# cmake needs this line
cmake_minimum_required(VERSION 3.0)# Define project name
project(yolov8_demo)# Release模式下的编译指令
# SET(CMAKE_BUILD_TYPE "Release")
# set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_RELEASE} -s")
# set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -std=c++17 -pthread -Wall -Wl")# Debug模式下的编译指令
SET(CMAKE_BUILD_TYPE "Debug")
set(CMAKE_C_FLAGS_RELEASE "${CMAKE_C_FLAGS_DEBUG}")
set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -std=c++17 -pthread") set(OpenCV_LIBS opencv_videoio opencv_imgcodecs opencv_imgproc opencv_core opencv_dnn opencv_highgui)include_directories(/usr/local/include/opencv4${PROJECT_SOURCE_DIR}/include${PROJECT_SOURCE_DIR}/include/onnxruntime
)link_directories(${PROJECT_SOURCE_DIR}/lib/onnxruntime   # 第三方动态库文件/usr/local/lib/
)#递归指定源码的路径
file(GLOB_RECURSE SRCS ${PROJECT_SOURCE_DIR}/src/*.cpp)# Declare the executable target built from your sources
add_executable(yolov8_demo ${SRCS})# Link your application with OpenCV libraries
target_link_libraries(yolov8_demo 
-lonnxruntime
${OpenCV_LIBS}
)

代码 

main.cpp

#include <opencv2/core.hpp>

#include <opencv2/highgui.hpp>

#include <iostream>

#include <YoloV8.hpp>

#include <unistd.h>

#include <sys/syscall.h>

#include <thread>

int VideoDet(int index, bool showDet, bool useGPU, bool printPerStepInfo)

{

    size_t threadId = static_cast<size_t>(syscall(SYS_gettid));

    // std::cout << "index:" << index << " thread id: " << threadId << std::endl;

    cv::VideoCapture capture("./test/test_car_person_1080P.mp4");

    // 检查视频是否成功打开

    if (!capture.isOpened())

    {

        std::cout << "无法读取视频文件" << std::endl;

        return -1;

    }

    int frameCount = capture.get(cv::CAP_PROP_FRAME_COUNT); // 获取视频帧数

    double fps = capture.get(cv::CAP_PROP_FPS);             // 获取帧率

    int delay = int(1000 / fps);                            // 根据帧率计算帧间间隔时间

    // delay=1;

    std::string model_path = "./models/yolov8n.onnx";

    std::string lable_path = "./models/lable.txt";

    int GPUCount = 2;

    int device_id = 0;

    if (index >= GPUCount)

    {

        device_id = index % GPUCount;

    }

    else

    {

        device_id = index;

    }

    // device_id=0;

    YoloV8 yoloV8(model_path, lable_path, useGPU, device_id);

    yoloV8.index = index;

    yoloV8.threadId = threadId;

    yoloV8.videoFps = fps;

    yoloV8.frameCount = frameCount;

    // std::cout << "device_id:" << yoloV8.device_id << std::endl;

    // vector<DetectionResult> detectionResult;

    // Mat frame=cv::imread("../test/dog.jpg");

    // yoloV8.Detect(frame, detectionResult);

    // std::cout << "detectionResult size:" << detectionResult.size() << std::endl;

    string winname = "detectionResult-" + std::to_string(index);

    while (true)

    {

        double start = (double)cv::getTickCount();

        delay = int(1000 / fps);

        Mat frame;

        bool success = capture.read(frame); // 读取一帧数据

        // 检查是否成功读取帧

        if (!success)

        {

            std::cout << "index:" << index << ",读取完毕" << std::endl;

            yoloV8.PrintAvgCostTime();

            break;

        }

        vector<DetectionResult> detectionResult;

        yoloV8.Detect(frame, detectionResult);

        // std::cout <<"index:"<<index<< " thread id: " << threadId << " detectionResult size: " << detectionResult.size() << std::endl;

        yoloV8.detectionResultSize = detectionResult.size();

        if (printPerStepInfo)

        {

            yoloV8.PrintCostTime();

            yoloV8.PrintAvgCostTime();

        }

        if (showDet)

        {

            yoloV8.Draw(frame, detectionResult);

            imshow(winname, frame);

            double costTime = ((double)getTickCount() - start) / getTickFrequency();

            delay = delay - costTime;

            if (delay <= 0)

            {

                delay = 1;

            }

            if (waitKey(delay) == 27) // 通过按下ESC键退出循环

            {

                break;

            }

        }

    }

    capture.release(); // 释放视频对象

    if (showDet)

    {

        cv::destroyWindow(winname);

    }

    return 0;

}

int main(int argc, char *const argv[])

{

    int threadCount = 1;

    bool showDet = false;

    bool useGPU = false;

    bool printPerStepInfo = true;

    const char *keys ="{h help                || print this message}"

        "{c threadCount         | 1     | run thread number}"

        "{s showDet             | false | show detection result}"

        "{g useGPU              | false | use GPU}"

        "{p printPerStepInfo    | false | print per Step Info}";

    cv::CommandLineParser parser(argc, argv, keys);

    if(parser.has("help"))

    {

        parser.about("YoloV8 demo v1.0.0");

        parser.printMessage();

        return 0;

    }

    threadCount=parser.get<int>("threadCount");

    showDet=parser.get<bool>("showDet");

    useGPU=parser.get<bool>("useGPU");

    printPerStepInfo=parser.get<bool>("printPerStepInfo");

    std::cout << std::boolalpha;

    std::cout << "threadCount:" << threadCount << ",showDet:" << showDet<< ",useGPU:" << useGPU << ",printPerStepInfo:" << printPerStepInfo << std::endl;

    for (size_t i = 0; i < threadCount; i++)

    {

        std::thread thread(VideoDet, i, showDet, useGPU, printPerStepInfo);

        thread.detach();

    }

    while (true)

    {

        sleep(100);

    }

    return 0;

}

#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <YoloV8.hpp>
#include <unistd.h>
#include <sys/syscall.h>
#include <thread>int VideoDet(int index, bool showDet, bool useGPU, bool printPerStepInfo)
{size_t threadId = static_cast<size_t>(syscall(SYS_gettid));// std::cout << "index:" << index << " thread id: " << threadId << std::endl;cv::VideoCapture capture("./test/test_car_person_1080P.mp4");// 检查视频是否成功打开if (!capture.isOpened()){std::cout << "无法读取视频文件" << std::endl;return -1;}int frameCount = capture.get(cv::CAP_PROP_FRAME_COUNT); // 获取视频帧数double fps = capture.get(cv::CAP_PROP_FPS);             // 获取帧率int delay = int(1000 / fps);                            // 根据帧率计算帧间间隔时间// delay=1;std::string model_path = "./models/yolov8n.onnx";std::string lable_path = "./models/lable.txt";int GPUCount = 2;int device_id = 0;if (index >= GPUCount){device_id = index % GPUCount;}else{device_id = index;}// device_id=0;YoloV8 yoloV8(model_path, lable_path, useGPU, device_id);yoloV8.index = index;yoloV8.threadId = threadId;yoloV8.videoFps = fps;yoloV8.frameCount = frameCount;// std::cout << "device_id:" << yoloV8.device_id << std::endl;// vector<DetectionResult> detectionResult;// Mat frame=cv::imread("../test/dog.jpg");// yoloV8.Detect(frame, detectionResult);// std::cout << "detectionResult size:" << detectionResult.size() << std::endl;string winname = "detectionResult-" + std::to_string(index);while (true){double start = (double)cv::getTickCount();delay = int(1000 / fps);Mat frame;bool success = capture.read(frame); // 读取一帧数据// 检查是否成功读取帧if (!success){std::cout << "index:" << index << ",读取完毕" << std::endl;yoloV8.PrintAvgCostTime();break;}vector<DetectionResult> detectionResult;yoloV8.Detect(frame, detectionResult);// std::cout <<"index:"<<index<< " thread id: " << threadId << " detectionResult size: " << detectionResult.size() << std::endl;yoloV8.detectionResultSize = detectionResult.size();if (printPerStepInfo){yoloV8.PrintCostTime();yoloV8.PrintAvgCostTime();}if (showDet){yoloV8.Draw(frame, detectionResult);imshow(winname, frame);double costTime = ((double)getTickCount() - start) / getTickFrequency();delay = delay - costTime;if (delay <= 0){delay = 1;}if (waitKey(delay) == 27) // 通过按下ESC键退出循环{break;}}}capture.release(); // 释放视频对象if (showDet){cv::destroyWindow(winname);}return 0;
}int main(int argc, char *const argv[])
{int threadCount = 1;bool showDet = false;bool useGPU = false;bool printPerStepInfo = true;const char *keys ="{h help                || print this message}""{c threadCount         | 1     | run thread number}""{s showDet             | false | show detection result}""{g useGPU              | false | use GPU}""{p printPerStepInfo    | false | print per Step Info}";cv::CommandLineParser parser(argc, argv, keys);if(parser.has("help")){parser.about("YoloV8 demo v1.0.0");parser.printMessage();return 0;}threadCount=parser.get<int>("threadCount");showDet=parser.get<bool>("showDet");useGPU=parser.get<bool>("useGPU");printPerStepInfo=parser.get<bool>("printPerStepInfo");std::cout << std::boolalpha;std::cout << "threadCount:" << threadCount << ",showDet:" << showDet<< ",useGPU:" << useGPU << ",printPerStepInfo:" << printPerStepInfo << std::endl;for (size_t i = 0; i < threadCount; i++){std::thread thread(VideoDet, i, showDet, useGPU, printPerStepInfo);thread.detach();}while (true){sleep(100);}return 0;
}

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