YOLOv5目标检测+KCF单目标跟踪c++ 实现（QT creator +tensort GPU加速）

单目标的识别跟踪kcf算法结合yolov5的目标检测模块

镁顭易誶

3580人浏览 · 2023-05-19 09:35:17

镁顭易誶 · 2023-05-19 09:35:17 发布

1.YOLov5的检测相关dill 导出和模型文件.engine生成

这里参考我的另外一篇文章有详细说明

yolov5 windows 下训练+ c++ TensorRT 部署在qt (vs+qtcreator) 只要一篇文章即可_知新_ROL的博客-CSDN博客

2.qt creator +opencv 相关配置说明

1.版本说明

qt 的版本：5.12.10

opencv:4.5.3 --这里要用到其contrib的模块 tracking

OpenCV4.5.3+OpenCV_contrib4.5.3配置笔记_opencv4和opencvcontrib配置_lucky_yu_er的博客-CSDN博客

2.qt creator 的.pro 中的修改

这里根据大家自己的路径进行修改，如果改为之后还有问题，可能是环境变量没有配置，系统找不到dill 。


win32:CONFIG(release, debug|release): LIBS += -LD:/my_software/opencv/opencv/newbuil_with_contrib/x64/vc15/lib/ -lopencv_world453
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_software/opencv/opencv/newbuil_with_contrib/x64/vc15/lib/ -lopencv_world453d

INCLUDEPATH +=D:/my_software/opencv/opencv/newbuil_with_contrib/include
DEPENDPATH += D:/my_software/opencv/opencv/newbuil_with_contrib/include/opencv2

# 自己导出的dll 设置
INCLUDEPATH += "D:/my_code/my_yolov5_tensorrt"
DEPENDPATH += "D:/my_code/my_yolov5_tensorrt"
win32:CONFIG(release, debug|release): LIBS += -LD:/my_code/my_yolov5_tensorrt/build_dill/Release/ -lyolov5
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_code/my_yolov5_tensorrt/build_dill/Release/ -lyolov5d

### TensorRT-8.5.3.1设置
INCLUDEPATH +="D:/my_software/TensorRT-8.5.3.1/include"
DEPENDPATH +="D:/my_software/TensorRT-8.5.3.1/include"
win32:CONFIG(release, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvinfer
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvinferd

win32:CONFIG(release, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvinfer_plugin
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvinfer_plugind

win32:CONFIG(release, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvonnxparser
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvonnxparserd

win32:CONFIG(release, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvparsers
else:win32:CONFIG(debug, debug|release): LIBS += -LD:/my_software/TensorRT-8.5.3.1/lib/ -lnvparsersd

## CUDA 设置
CUDA_SOURCES += yololayer.cu
CUDA_DIR = "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.7"
SYSTEM_NAME = Win64
SYSTEM_TYPE = 64
CUDA_ARCH  = sm_86
NVCC_OPTIONS = --use_fast_math
## 头文件路径
INCLUDEPATH += "$$CUDA_DIR/include"
# 导入库文件路径
QMAKE_LIBDIR += "$$CUDA_DIR/lib/x64"
CUDA_INC = $$join(INCLUDEPATH,'" -I"','-I"','"')

3.将相关dill 移入运行生成的debug /release 文件夹中

找到releas/debug 文件夹中

将下面这些东西放进去

3.代码中的修改

1.ui 界面 -画两个label 两个按钮即可

2.mainwindow.h 下的配置

#ifndef MAINWINDOW_H
#define MAINWINDOW_H

#include <QMainWindow>
//c++的一些库
#include <iostream>
#include <string.h>
#include<iostream>
#include <stdio.h>
#include <windows.h>
#include <cmath>
#include <typeinfo>
//opencv的一些库
#include <opencv2/opencv.hpp>
#include<opencv2\core\core.hpp>
#include<opencv2\highgui\highgui.hpp>
#include<opencv2\imgproc\imgproc.hpp>

#include <opencv2\tracking\tracking.hpp>

//qt的一些库
#include <QMainWindow>
#include<QDebug>
#include<QMessageBox>
#include"QByteArray"
#include"QRect"
#include"QDateTime"
#include"QDir"
#include<QFileDialog>
#include<QTimer>
//qt 中和网络通信相关的一些库
#include<QJsonObject>
#include<QByteArray>
#include<QJsonDocument>

#include <QMetaObject>
#include <QEventLoop>
#include <QJsonDocument>            //以下是json数据传送所需头文件
#include <QJsonParseError>
#include <QJsonObject>
#include <QJsonValue>
#include <QJsonArray>
#include<QFileInfo>


#pragma execution_character_set("utf-8");//设置字体可中文显示
typedef unsigned char uchar;
using namespace std;



QT_BEGIN_NAMESPACE
namespace Ui { class MainWindow; }
QT_END_NAMESPACE

class MainWindow : public QMainWindow
{
    Q_OBJECT

public:
    MainWindow(QWidget *parent = nullptr);
    ~MainWindow();
  

private slots:
    void ReadFrame();

    void on_btnOpenVideo_clicked();

    void on_btnCloseVideo_clicked();

private:

    QTimer *timer;
    cv::VideoCapture capture;
    cv::Mat frame;
    cv::Mat result_frame;
    int frame_index=0;
bool is_checking=false;
    int truck_num=0;
    int car_id=0;
    bool is_tracking_object;
    void LabelDisplayMat(QLabel *label, cv::Mat &mat);
    void recv_change_openvideo(bool isopen_video);
    string QStr2Str(const QString qStr);
    // 跟踪测试
    cv::Ptr<cv::Tracker> tracker;
    cv::TrackerKCF::Params tracker_params;

private slots:
    void on_pushButton_clicked();

    void on_pushButton_2_clicked();

private:
    Ui::MainWindow *ui;
};
#endif // MAINWINDOW_H

2.mainwindows.cpp

代码中融合了目标失败后可以继续跟踪， 其中只检测叉车car_forklift 这一类中的一个

#include "mainwindow.h"
#include "ui_mainwindow.h"
#include"yololayer.h"
#include "Detection.h"
Connect connect_yolo;
YOLOV5* yolo_dll = connect_yolo.Create_YOLOV5_Object();
std::vector<cv::Rect> Boxes;                                         //检测的结果返回框框
std::vector<const char*>ClassLables;                                 //检测的分类类型
cv::Rect bbox;

MainWindow::MainWindow(QWidget *parent)
    : QMainWindow(parent)
    , ui(new Ui::MainWindow)
{
    ui->setupUi(this);


    timer = new QTimer(this);
    connect(timer,SIGNAL(timeout()),this,SLOT(ReadFrame()));

    yolo_dll->Initialize("yolov5sexp14best.engine", 0);                 //读取yolo的模型
    //    bbox=cv::Rect(500, 500, 100, 100);
    tracker_params.detect_thresh = 0.3f;
    tracker = cv::TrackerKCF::create(tracker_params);
}

MainWindow::~MainWindow()
{
    delete ui;
}


void MainWindow::ReadFrame()
{
    if(capture.isOpened())
    {
        capture >> frame;
        if(!frame.empty())
        {
            result_frame=frame.clone();
            cvtColor(frame, result_frame, cv::COLOR_BGR2RGB);     //  OpenCV中Mat读入的图像是BGR格式，要转换为RGB格式
            if(car_id==1)
            {
                qDebug()<<"bbox不是空的";
                is_checking=tracker->update(result_frame, bbox);
                cv::rectangle(result_frame,bbox,cv::Scalar(0, 0, 255), 2, 8, 0) ;// result_frame
                if(is_checking==false)
                {
                    car_id=0;
                    tracker.release();
                }


            }else
            {
                qDebug()<<"is_checking "<<is_checking;
                yolo_dll->Detecting(frame, Boxes, ClassLables);
                for(unsigned i=0;i<Boxes.size();i++)
                {

                    string  str_ship="car_forklift";
                    qDebug()<<ClassLables.at(i);
                    qDebug()<<str_ship.c_str();
                    string str_class(ClassLables.at(i));
                    if(str_class==str_ship.c_str())
                    {
                        qDebug()<<"ClassLables.at(i)"<<Boxes.at(i).x<<" "<<Boxes.at(i).y;
                        if(car_id==0)
                        {
                            bbox=Boxes.at(i);

                            qDebug()<<"bbox"<<bbox.x<<" "<<bbox.y;
                            car_id=1;
                            tracker_params.detect_thresh = 0.3f;
                            tracker = cv::TrackerKCF::create(tracker_params);
                            tracker->init(result_frame,bbox);
                        }
                    }
                }
            }

            cv::resize(frame, frame, cv::Size(640, 480));
            // 将抓取到的帧，转换为QImage格式。QImage::Format_RGB888不同的摄像头用不同的格式。
            QImage image((const uchar*)frame.data, frame.cols, frame.rows, QImage::Format_RGB888);
            ui->label->setPixmap(QPixmap::fromImage(image));    //  将图片显示到label上
            ui->label->resize( ui->label->pixmap()->size());    //  将label控件resize到fame的尺寸


            cv::resize(result_frame, result_frame, cv::Size(640, 480));
            // 将抓取到的帧，转换为QImage格式。QImage::Format_RGB888不同的摄像头用不同的格式。
            QImage image2((const uchar*)result_frame.data, result_frame.cols, result_frame.rows, QImage::Format_RGB888);
            ui->label_3->setPixmap(QPixmap::fromImage(image2));    //  将图片显示到label上
            ui->label_3->resize( ui->label_3->pixmap()->size());    //  将label控件resize到fame的尺寸

            Boxes.clear();
            ClassLables.clear();
        }



    }

}

void MainWindow::on_btnOpenVideo_clicked()
{
    QString file_name = QFileDialog::getOpenFileName(this, tr("Open Video"), ".", tr("Video File(*.avi *.mp4 *.h264)"));

    capture.open(file_name.toStdString());

    timer->start(25);   //  开始计时，每隔25毫秒更新一次，超时则发出timeout()信号
}

void MainWindow::on_btnCloseVideo_clicked()
{
    timer->stop();      //  停止读取数据。

    //  释放内存；
    capture.release();
    frame.release();

}
void MainWindow::on_pushButton_clicked()
{
    on_btnOpenVideo_clicked();
}

void MainWindow::on_pushButton_2_clicked()
{
    on_btnCloseVideo_clicked();
}