文章目录

• 前言
• 一、环境
• • 1、硬件
  • 2、软件
• 二、YOLO模型
• 三、新建Qt项目
• • 1、pro文件
  • 2、main函数
• 四、YOLO 类封装
• • 1、yolo.h
  • 2、yolo.cpp
  • 3、classes.txt
• 五、效果

前言

  最近工作中需要用yolov5训练模型，然后在win10系统下，完成推理部署。本篇主要介绍使用opencv-dnn模块实现模型推理部署。

一、环境

1、硬件

Intel® Core i5-7400 CPU @ 3.00GHZ
Intel® HD Graphics 630 内存4G 核显
内存 8G
win10 64位系统

2、软件

opencv4.6.0
yolov5 6.2版本

二、YOLO模型

我使用的是onnx模型，如果没有训练过自己的模型，可以使用官方的yolov5s.pt模型，将其转化为yolov5s.onnx模型，转化方法如下：

python export.py

在yolov5-master目录下，可以看到yolov5s.onnx模型已生成。

三、新建Qt项目

1、pro文件

在pro文件中，添加opencv相关配置，内容如下：

#-------------------------------------------------## Project created by QtCreator 2022-10-09T13:28:19##-------------------------------------------------QT       += core guigreaterThan(QT_MAJOR_VERSION, 4): QT += widgetsTARGET = untitled1TEMPLATE = appSOURCES += main.cpp\        mainwindow.cpp \    yolo.cppHEADERS  += mainwindow.h \    yolo.hFORMS    += mainwindow.uiINCLUDEPATH += C:/opencv4.6.0/build/include               C:/opencv4.6.0/build/include/opencv2LIBS += -LC:/opencv4.6.0/build/x64/vc14/lib/ -lopencv_world460

2、main函数

#include "mainwindow.h"#include #include #include "yolo.h"#include #include #include //const vector colors = { Scalar(255, 255, 0), Scalar(0, 255, 0), Scalar(0, 255, 255), Scalar(255, 0, 0) };int main(int argc, char *argv[]){    Mat frame;    VideoCapture capture("G:/1.mp4");//sample    if (!capture.isOpened())    {        std::cerr << "Error opening video file\n";        return -1;    }    cv::VideoWriter video("out.avi",cv::VideoWriter::fourcc('M','J','P','G'),10, cv::Size(1920,1080));    //    YOLO *yolov5 = new YOLO;    yolov5->init("F:/SVN-ZJKY/YiFeiShouJiRobot/yolov5-6.1/yolov5s.onnx");    //    std::vector<detect_result> output;    int total_frames = 0;    //    clock_t start_name = 0;    clock_t end_time = 0;    //    while ( true )    {        //        start_name = clock();        capture.read(frame);        if (frame.empty())        {            std::cout << "End of stream\n";            break;        }        //        total_frames++;//        auto start = std::chrono::system_clock::now();//        t = (double)cv::getTickCount();        yolov5->detect(frame,output);//        auto end = std::chrono::system_clock::now();//        auto detect_time =std::chrono::duration_cast(end - start).count();//ms//        std::cout<<detect_time<<":"<<total_frames<<":"<<output.size()<<std::endl;        ////        t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();//        fps = 1.0 / t;        end_time=clock();//ms        auto text = "FPS: " + std::to_string(1 / ((double)(end_time - start_name) / CLOCKS_PER_SEC));        qDebug() << "Frame time(ms): " << (double)(end_time - start_name) /*/ CLOCKS_PER_SEC*/;        cv::putText(frame, text, cv::Point(3, 25), cv::FONT_ITALIC, 0.8, cv::Scalar(0, 0, 255), 2);        //        yolov5->draw_frame(frame,output);        imshow("output", frame);        video.write(frame);        if (waitKey(1) != -1)        {            std::cout << "finished by user\n";            break;        }        output.clear();    }    //    capture.release();    video.release();    cv::destroyAllWindows();    waitKey(0);    //std::cout << "Total frames: " << total_frames << "\n";    return 0;}

四、YOLO 类封装

1、yolo.h

#include #include #include #include #include #include using namespace cv;using namespace dnn;using namespace std;class detect_result{public:    int classId;    float confidence;    cv::Rect_<float> box;};class YOLO{public:    YOLO();    ~YOLO();    void init(std::string onnxpath);    //Net loadNet(bool is_cuda);    //Mat format_yolov5(const Mat &source);    std::vector<std::string> classes_;    void detect(cv::Mat & frame, std::vector<detect_result> &result);    //void drawRect(Mat &image,vector &output);    //std::vector load_class_list();    void draw_frame(cv::Mat & frame, std::vector<detect_result> &results);private:    cv::dnn::Net net;    //cv::dnn::Net m_net;//    const float INPUT_WIDTH = 640.0;//    const float INPUT_HEIGHT = 640.0;//    const float SCORE_THRESHOLD = 0.2;//    const float NMS_THRESHOLD = 0.4;//    const float CONFIDENCE_THRESHOLD = 0.4;    const float confidence_threshold_ =0.25f;    const float nms_threshold_ = 0.4f;    const int model_input_width_ = 640;    const int model_input_height_ = 640;};

2、yolo.cpp

#include "yolo.h"YOLO::YOLO(){    //loadNet(false);}YOLO::~YOLO(){}//std::vector YOLO::load_class_list()//{//    std::vector class_list;//    std::ifstream ifs("G:/classes.txt");//    std::string line;//    while (getline(ifs, line))//    {//        class_list.push_back(line);//    }//    return class_list;//}//Net YOLO::loadNet(bool is_cuda)//{//    //F:\SVN-ZJKY\YiFeiShouJiRobot\yolov5-master//    //m_net = readNet("F:\\SVN-ZJKY\\YiFeiShouJiRobot\\yolov5-master\\yolov5s.onnx");//    m_net = readNet("F:\\SVN-ZJKY\\YiFeiShouJiRobot\\demo\\yolov5-opencv-dnn-cpp-main\\QtDemo\\build-yolov5-opencv-dnn-cpp-main-Qt5_6_2_MSVC2015_64bit-Release\\release\\yolov5s.onnx");//    if (is_cuda)//    {//        cout << "Attempty to use CUDA\n";//        m_net.setPreferableBackend(DNN_BACKEND_CUDA);//        m_net.setPreferableTarget(DNN_TARGET_CUDA_FP16);//    }//    else//    {//        cout << "Running on CPU12\n";//        m_net.setPreferableBackend(DNN_BACKEND_OPENCV);//        m_net.setPreferableTarget(DNN_TARGET_CPU);//    }//    return m_net;//}//Mat YOLO::format_yolov5(const Mat &source)//{//    int col = source.cols;//    int row = source.rows;//    int _max = MAX(col, row);//    Mat result = Mat::zeros(_max, _max, CV_8UC3);//    source.copyTo(result(Rect(0, 0, col, row)));//    return result;//}void YOLO::init(std::string onnxpath){    this->net = cv::dnn::readNetFromONNX(onnxpath);    std::string file="G:/classes.txt";    std::ifstream ifs(file);    if (!ifs.is_open())        CV_Error(cv::Error::StsError, "File " + file + " not found");    std::string line;    while (std::getline(ifs, line))    {        classes_.push_back(line);    }}void YOLO::detect(cv::Mat & frame, std::vector<detect_result> &results){    int w = frame.cols;    int h = frame.rows;    int _max = std::max(h, w);    cv::Mat image = cv::Mat::zeros(cv::Size(_max, _max), CV_8UC3);    cv::Rect roi(0, 0, w, h);    frame.copyTo(image(roi));    float x_factor = static_cast<float>(image.cols) / model_input_width_;    float y_factor = static_cast<float>(image.rows) / model_input_height_;    //std::cout<<x_factor<<std::endl;    //std::cout<<y_factor<<std::endl;    cv::Mat blob = cv::dnn::blobFromImage(image, 1 / 255.0, cv::Size(model_input_width_, model_input_height_), cv::Scalar(0, 0, 0), true, false);    this->net.setInput(blob);    cv::Mat preds = this->net.forward("output");//outputname    cv::Mat det_output(preds.size[1], preds.size[2], CV_32F, preds.ptr<float>());    std::vector<cv::Rect> boxes;    std::vector<int> classIds;    std::vector<float> confidences;    for (int i = 0; i < det_output.rows; i++)    {        float box_conf = det_output.at<float>(i, 4);        if (box_conf < nms_threshold_)        {            continue;        }        cv::Mat classes_confidences = det_output.row(i).colRange(5, 85);        cv::Point classIdPoint;        double cls_conf;        cv::minMaxLoc(classes_confidences, 0, &cls_conf, 0, &classIdPoint);        if (cls_conf > confidence_threshold_)        {            float cx = det_output.at<float>(i, 0);            float cy = det_output.at<float>(i, 1);            float ow = det_output.at<float>(i, 2);            float oh = det_output.at<float>(i, 3);            int x = static_cast<int>((cx - 0.5 * ow) * x_factor);            int y = static_cast<int>((cy - 0.5 * oh) * y_factor);            int width = static_cast<int>(ow * x_factor);            int height = static_cast<int>(oh * y_factor);            cv::Rect box;            box.x = x;            box.y = y;            box.width = width;            box.height = height;            boxes.push_back(box);            classIds.push_back(classIdPoint.x);            confidences.push_back(cls_conf * box_conf);        }    }    std::vector<int> indexes;    cv::dnn::NMSBoxes(boxes, confidences, confidence_threshold_, nms_threshold_, indexes);    for (size_t i = 0; i < indexes.size(); i++)    {        detect_result dr;        int index = indexes[i];        int idx = classIds[index];        dr.box = boxes[index];        dr.classId = idx;        dr.confidence = confidences[index];        results.push_back(dr);    }}void YOLO::draw_frame(cv::Mat & frame, std::vector<detect_result> &results){    for(auto dr : results)    {        cv::rectangle(frame, dr.box , cv::Scalar(0, 0, 255), 2, 8);        cv::rectangle(frame, cv::Point(dr.box .tl().x, dr.box .tl().y - 20), cv::Point(dr.box .br().x, dr.box .tl().y), cv::Scalar(255, 0, 0), -1);        std::string label = cv::format("%.2f", dr.confidence);        label = classes_[dr.classId ] + ":" + label;        cv::putText(frame, label, cv::Point(dr.box.x, dr.box.y + 6), 1, 2, cv::Scalar(0, 255, 0),2);    }}

3、classes.txt

官方yolov5s.pt模型，能够识别80种物体，classes.txt文件记录的就是这80种物体类别名，如下：

personbicyclecarmotorbikeaeroplanebustraintruckboattraffic lightfire hydrantstop signparking meterbenchbirdcatdoghorsesheepcowelephantbearzebragiraffebackpackumbrellahandbagtiesuitcasefrisbeeskissnowboardsports ballkitebaseball batbaseball gloveskateboardsurfboardtennis racketbottlewine glasscupforkknifespoonbowlbananaapplesandwichorangebroccolicarrothot dogpizzadonutcakechairsofapottedplantbeddiningtabletoilettvmonitorlaptopmouseremotekeyboardcell phonemicrowaveoventoastersinkrefrigeratorbookclockvasescissorsteddy bearhair driertoothbrush

五、效果

以本地视频文件1.mp4为例，效果如下：

可以看到，在我的机器上，视频卡顿现象非常严重，FPS较小。