Qt C++海康SDK类中注册回调代码

末蓝、 2024-05-28 10:18 58阅读 0赞

一、程序入口  
main.cpp

#include "mainwindow.h"
    #include <QApplication>
    using namespace std;
    
    int main(int argc, char *argv[])
    {
        
        QApplication a(argc, argv);
    
        MainWindow w;
    
        w.show();
        return a.exec();
    }

二、数据共享类  
CameraGlobalData头文件

#ifndef CAMERAGLOBALDATA_H
    #define CAMERAGLOBALDATA_H
    #include <iostream>
    #include <vector>
    using namespace std;
    
    struct CameraStruct{
        
        string ipaddress;
        string username;
        string password;
        int port;
    
    };
    class CameraGlobalData
    {
        
    public:
        CameraGlobalData();
        vector<CameraStruct> cameras;
        string rtsp[8];
    };
    
    #endif // CAMERAGLOBALDATA_H

CameraGlobalData源文件

#include "cameraglobaldata.h"
    
    CameraGlobalData::CameraGlobalData()
    {
        
        CameraStruct temp;
        temp.ipaddress="192.168.2.174";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.175";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.176";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.177";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.178";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.179";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.181";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        temp.ipaddress="192.168.2.184";
        temp.username="admin";
        temp.password="a12131415";
        temp.port=8000;
        cameras.push_back(temp);
    
        for(int i=0;i<8;i++){
        
    
            rtsp[i]="rtsp://"+cameras[i].username+":"+cameras[i].password+"@"+cameras[i].ipaddress+":554/h264/ch1/sub/av_stream";
    
        }
    }

三、线程类  
CameraThread 头文件

#ifndef CAMERATHREAD_H
    #define CAMERATHREAD_H
    
    #include <QThread>
    #include <QImage>
    #include <QLabel>
    #include <QTime>
    #include <QElapsedTimer>
    #include <opencv2/opencv.hpp>
    #include "Windows.h"
    #include "HCNetSDK.h"
    #include "cameraglobaldata.h"
    
    
    using namespace std;
    using namespace cv;
    
    
    class CameraThread : public QThread
    {
        
        Q_OBJECT
    
    public:
        CameraThread(QLabel *displayLabel, int cameraIndex, QObject *parent = nullptr);
        void gpu_process(UMat frame);
        void cpu_process(Mat frame);
        int gpu_CheckPoint(int spec_x, int spec_y, cv::UMat frame);
        int cpu_CheckPoint(int spec_x, int spec_y, cv::Mat frame);
    
    
        ~CameraThread();
    
    
    private:
         static void CALLBACK cbMessageCallback(LONG lCommand, NET_DVR_ALARMER *pAlarmer, char *pAlarmInfo, DWORD dwBufLen, void* pUser);
         void cbMessageCallbackInstance(LONG lCommand, NET_DVR_ALARMER *pAlarmer, char *pAlarmInfo, DWORD dwBufLen);
         int registerHikonSDKCallback(char* ipaddress,char* username,char* password,int port,int channelNO);
    protected:
        void run() override;
    
    signals:
        void frameReady(const QImage &frame, int index);
        void frameReady2(int index,int count);
    
    private:
        CameraGlobalData cgd;
    
        int count_1;
        int count_2;
        int count_3;
        int iNum;
    
    
    
        int framecount;
        double usedTimecnt;
        QLabel *displayLabel;
        int cameraIndex;
    
        cv::Mat currentFrame;
    
    
        cv::Mat prevFrame_cpu;//上一帧图像
        cv::UMat prevFrame_gpu;//上一帧图像
    
    
        int ret=0;
        int noContourFrameCount=0;//no Contour Frame count
        QTime beginTime;//开始时间
        cv::Mat elm_1;
        cv::Mat elm_2;
        int roiWidth = 40;//检测区宽度
        int roiHeight = 20;//检测区高度
        int count;//流量计数
        //车入标记
        bool carIn=false;
        bool carIn_isTimed=false;
        QTime carIn_datetime;
    
        //车出标记
        bool carOut=false;
        bool carOut_isTimed=false;
        QTime carOut_datetime;
        QTime lastCountTime;//上次计数时间
    
        //旧的轮廓检测法的参数
        cv::Mat curFrameImg;//当前帧图像
        cv::Mat preFrameImg_cpu;
        cv::Mat img_roi1,gray_1,gray_2,diff_img,thres_img,erode_img,median_img,dilate_img,img_color;
        int sl;
        int frameCount;
        double minarea;
        QString checked_Car_Tag;
        int pusleDataStatus;
    
        //新的轮廓检测法的参数
        cv::Mat grayFrame,subRegion_cpu,prevGrayFrame,frameDiff,thresholdedDiff;
        bool carin2=false; //车入
        bool carout2=true; //车出
        bool nocar2=false;  //无车
        bool isCount2=false;//是否计数
        //QTime countTime;//计数时间
        QElapsedTimer countTime;
        int carCount;//计数
        float speed;
    
        cv::UMat elm_1_gpu;
        cv::UMat elm_2_gpu;
        //旧的轮廓检测法的参数
        cv::UMat curFrameImg_gpu;//当前帧图像
        cv::UMat img_roi1_gpu,gray_1_gpu,gray_2_gpu,diff_img_gpu,thres_img_gpu,erode_img_gpu,median_img_gpu,dilate_img_gpu,img_color_gpu;
        cv::UMat grayFrame_gpu,subRegion_gpu,prevGrayFrame_gpu,frameDiff_gpu,thresholdedDiff_gpu;
    
    
    };
    
    #endif // CAMERATHREAD_H

CameraThread源文件

#include "camerathread.h"
    #include <QDebug>
    #include <QThread>
    #include <iostream>
    #include <opencv2/opencv.hpp>
    #include <opencv2/imgproc/types_c.h>
    #include <unistd.h>
    #include <pthread.h>
    #include <sys/time.h>
    #include <iconv.h>
    #include <sstream>
    #include <string>
    #include <string.h>
    #include <QImage>
    #include <QThread>
    #include <QTime>
    #include <QPen>
    #include <QPainter>
    #include <QPointF>
    #include <opencv2/core/ocl.hpp>
    #define GPU 0
    
    CameraThread::CameraThread(QLabel *displayLabel, int cameraIndex, QObject *parent)
        : QThread(parent), displayLabel(displayLabel), cameraIndex(cameraIndex)
    {
        
        // 初始化摄像头和图像捕获器的代码，这里省略...
    //    rtsp[0]="rtspsrc location=rtsp://admin:a12131415@192.168.2.184:554/h264/ch1/sub/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[1]="rtspsrc location=rtsp://admin:a12131415@192.168.2.174:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[2]="rtspsrc location=rtsp://admin:a12131415@192.168.2.176:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[3]="rtspsrc location=rtsp://admin:a12131415@192.168.2.177:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[4]="rtspsrc location=rtsp://admin:a12131415@192.168.2.178:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[5]="rtspsrc location=rtsp://admin:a12131415@192.168.2.179:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[6]="rtspsrc location=rtsp://admin:a12131415@192.168.2.175:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    //    rtsp[7]="rtspsrc location=rtsp://admin:a12131415@192.168.2.174:554/h264/ch1/main/av_stream latency=100 ! rtph264depay ! h264parse  config-interval=1 ! mppvideodec ! videoconvert ! appsink max-buffers=3 drop=false sync=false";
    
    
          if(cameraIndex==7)
             registerHikonSDKCallback((char *)cgd.cameras.at(cameraIndex).ipaddress.c_str(),(char *)cgd.cameras.at(cameraIndex).username.c_str(),(char *)cgd.cameras.at(cameraIndex).password.c_str(),cgd.cameras.at(cameraIndex).port,1);
    
    
    
    }
    
    CameraThread::~CameraThread()
    {
        
        delete displayLabel;
        // 清理摄像头和图像捕获器资源的代码，这里省略...
    }
    
    void CALLBACK CameraThread::cbMessageCallback(LONG lCommand, NET_DVR_ALARMER *pAlarmer, char *pAlarmInfo, DWORD dwBufLen, void* pUser)
    {
        
        if (pUser != nullptr)
           {
        
               CameraThread* pThis = static_cast<CameraThread*>(pUser);
               pThis->cbMessageCallbackInstance(lCommand, pAlarmer, pAlarmInfo, dwBufLen);
           }
    }
    
    void CameraThread::cbMessageCallbackInstance(LONG lCommand, NET_DVR_ALARMER *pAlarmer, char *pAlarmInfo, DWORD dwBufLen)
    {
        
        int i=0;
        char filename[100];
        FILE *fSnapPic=NULL;
        FILE *fSnapPicPlate=NULL;
    
        //以下代码仅供参考，实际应用中不建议在该回调函数中直接处理数据保存文件，
        //例如可以使用消息的方式(PostMessage)在消息响应函数里进行处理。
    
        switch (lCommand)
        {
        
        case COMM_UPLOAD_PLATE_RESULT:
        {
        
            NET_DVR_PLATE_RESULT struPlateResult={
        0};
            memcpy(&struPlateResult, pAlarmInfo, sizeof(struPlateResult));
            printf("%s %s\n",QString("车牌号:").toLocal8Bit().toStdString().c_str(), struPlateResult.struPlateInfo.sLicense);//车牌号
    
            switch(struPlateResult.struPlateInfo.byColor)//车牌颜色
            {
        
            case VCA_BLUE_PLATE:
                printf("车辆颜色: 蓝色\n");
                break;
            case VCA_YELLOW_PLATE:
                printf("车辆颜色: 黄色\n");
                break;
            case VCA_WHITE_PLATE:
                printf("车辆颜色: 白色\n");
                break;
            case VCA_BLACK_PLATE:
                printf("车辆颜色: 黑色\n");
                break;
            default:
                break;
            }
    
            //场景图
            if (struPlateResult.dwPicLen != 0 && struPlateResult.byResultType == 1 )
            {
        
                sprintf(filename,"testpic_%d.jpg",iNum);
                fSnapPic=fopen(filename,"wb");
                fwrite(struPlateResult.pBuffer1,struPlateResult.dwPicLen,1,fSnapPic);
                iNum++;
                fclose(fSnapPic);
            }
            //车牌图
            if (struPlateResult.dwPicPlateLen != 0 && struPlateResult.byResultType == 1)
            {
        
                sprintf(filename,"testPicPlate_%d.jpg",iNum);
                fSnapPicPlate=fopen(filename,"wb");
                fwrite(struPlateResult.pBuffer1,struPlateResult.dwPicLen,1,fSnapPicPlate);
                iNum++;
                fclose(fSnapPicPlate);
            }
    
            //其他信息处理......
            break;
        }
        case COMM_ITS_PLATE_RESULT:
        {
        
            //COMM_ALARM_TPS_REAL_TIME             0x3081  //TPS实时过车数据上传
    
            NET_ITS_PLATE_RESULT struITSPlateResult={
        0};
            memcpy(&struITSPlateResult, pAlarmInfo, sizeof(struITSPlateResult));
            // 将前12位转换为 QString
            QString absTimeStr = QString::fromLatin1((char *)struITSPlateResult.struPicInfo[0].byAbsTime);
            printf("%s %s\n",QString("车牌号:").toLocal8Bit().toStdString().c_str(),struITSPlateResult.struPlateInfo.sLicense);//车牌号
            qDebug()<<"current time:"<<absTimeStr;
    
            for (i=0;i < struITSPlateResult.dwPicNum-1;i++)
            {
        
                // printf("车牌号: %s\n", struITSPlateResult.struPlateInfo.sLicense);//车牌号
                //byDriveChan：车道号 东左：1 东直1：2 东直2：3
                if(struITSPlateResult.byDriveChan==1){
        
                    count_1++;
                    std::cout<<"count:"<<count_1<<std::endl;
                    WORD w_year= struITSPlateResult.struSnapFirstPicTime.wYear;
                    byte b_month=struITSPlateResult.struSnapFirstPicTime.byMonth;
                    byte b_day=struITSPlateResult.struSnapFirstPicTime.byDay;
                    byte b_hour=struITSPlateResult.struSnapFirstPicTime.byHour;
                    byte b_minute=struITSPlateResult.struSnapFirstPicTime.byMinute;
                    byte b_second=struITSPlateResult.struSnapFirstPicTime.bySecond;
                    byte b_milliSec=struITSPlateResult.struSnapFirstPicTime.wMilliSec;
                    QString year_str=QString::number(w_year);
                    QString month_str=QString::number(b_month);
                    if(b_month<10){
        
                        month_str="0"+month_str;
                    }
                    QString day_str=QString::number(b_day);
                    if(b_day<10){
        
                        day_str="0"+day_str;
                    }
                    QString hour_str=QString::number(b_hour);
                    if(b_hour<10){
        
                        hour_str="0"+hour_str;
                    }
    
                    QString minute_str=QString::number(b_minute);
                    if(b_minute<10){
        
                        minute_str="0"+minute_str;
                    }
    
                    QString second_str=QString::number(b_second);
                    if(b_second<10){
        
                        second_str="0"+second_str;
                    }
    
                    QString milliSec_str=QString::number(b_milliSec);
                    if(b_milliSec<10){
        
                        milliSec_str="0"+milliSec_str;
                    }
    
    
    
                    QString car_inTime=year_str+"-"+month_str+"-"+day_str+" "+hour_str+":"+minute_str+":"+second_str+"."+milliSec_str;
                    QString car_outTime=QDateTime::currentDateTime().toString("yyyy-MM-dd HH:mm:ss.zzz");
    
    
    
                    qDebug()<<"car_inTime:"<<car_inTime;
                    qDebug()<<"car_outTime:"<<car_outTime;
    
                    WORD speed=struITSPlateResult.struVehicleInfo.wSpeed;
                    std::cout<<"speed:"<<speed<<std::endl;
    
    
                }
    
            }
            break;
        }
        default:
            break;
        }
    
        return;
    }
    
    int CameraThread::registerHikonSDKCallback(char* ipaddress,char* username,char* password,int port,int channelNO)
    {
        
        //---------------------------------------
        //初始化
        NET_DVR_Init();
    
        //设置连接时间与重连时间
        NET_DVR_SetConnectTime(2000, 1);
        NET_DVR_SetReconnect(10000, true);
    
        //---------------------------------------
        //注册设备
    
        //登录参数，包括设备地址、登录用户、密码等
        LONG lUserID = -1; //初始化lUserID
        NET_DVR_USER_LOGIN_INFO struLoginInfo = {
        0};//创建一个数据结构对象并为每个属性赋初值为0
        struLoginInfo.bUseAsynLogin = 0; //设置为同步登录方式
        strcpy(struLoginInfo.sDeviceAddress, ipaddress); //设置设备IP地址为192.168.2.9
        struLoginInfo.wPort = port; //设置服务端口为8000
        strcpy(struLoginInfo.sUserName, username); //设置：用户名
        strcpy(struLoginInfo.sPassword, password); //设置：密码
    
        //设备信息, 输出参数
        NET_DVR_DEVICEINFO_V40 struDeviceInfoV40 = {
        0};//创建一个V40数据结构对象
    
        lUserID = NET_DVR_Login_V40(&struLoginInfo, &struDeviceInfoV40);//登录注册并将设备信息存储在struDeviceInfoV40结构中
        if (lUserID < 0)
        {
        
            //登录失败时提示错误信息
            qDebug("Login failed, error code: %d\n", NET_DVR_GetLastError());
            NET_DVR_Cleanup();//登出
            return 0;
        }
    
        //---------------------------------------
        //报警布防
    
        //设置报警回调函数
        //NET_DVR_SetDVRMessageCallBack_V31(MSesGCallback, NULL);
    
        /*注：多台设备对接时也只需要调用一次设置一个回调函数，不支持不同设备的事件在不同的回调函数里面返回*/
        NET_DVR_SetDVRMessageCallBack_V50(0, cbMessageCallback, this);//如果是主线程中调用第三个参数可为NULL，如果是在类中调用设置为this
    
    
    
        //启用布防
        NET_DVR_SETUPALARM_PARAM struSetupParam={
        0};//创建布防结构对象并初始化为0
        struSetupParam.dwSize=sizeof(NET_DVR_SETUPALARM_PARAM);//根据NET_DVR_SETUPALARM_PARAM的大小来设置dwSize的值
        struSetupParam.byLevel = 1; //布防优先级：0- 一等级（高），1- 二等级（中）
        struSetupParam.byAlarmInfoType = 1; //上传报警信息类型: 0- 老报警信息(NET_DVR_PLATE_RESULT), 1- 新报警信息(NET_ITS_PLATE_RESULT)
    
        LONG lHandle = NET_DVR_SetupAlarmChan_V41(lUserID,&struSetupParam);//开始布防并设置回调函数
        //如果lHandle<0则布防失败，然后出登出并清除
        if (lHandle < 0)
        {
        
            qDebug("NET_DVR_SetupAlarmChan_V41 failed, error code: %d\n", NET_DVR_GetLastError());
            NET_DVR_Logout(lUserID);
            NET_DVR_Cleanup();
            return 0;
        }
        qDebug("布防成功!\n");
    
        //---------------------------------------
        //网络触发抓拍
    
        NET_DVR_SNAPCFG struSnapCfg;
        memset(&struSnapCfg, 0, sizeof(NET_DVR_SNAPCFG));
    
        //结构体大小
        struSnapCfg.dwSize = sizeof(NET_DVR_SNAPCFG);
    
        //线圈抓拍次数，0-不抓拍，非0-连拍次数，目前最大5次
        struSnapCfg.bySnapTimes  = 0;
    
        //抓拍等待时间，单位ms，取值范围[0,60000]
        struSnapCfg.wSnapWaitTime   = 1;//1000
    
        //连拍间隔时间，单位ms，取值范围[67,60000]
        struSnapCfg.wIntervalTime[0]  = 67;//1000
        struSnapCfg.wIntervalTime[1]  = 67;//1000
    
        //触发IO关联的车道号，取值范围[0,9]
        struSnapCfg.byRelatedDriveWay = channelNO;
    
        //网络触发连拍
        if (!NET_DVR_ContinuousShoot(lUserID, &struSnapCfg))
        {
        
            qDebug("NET_DVR_ContinuousShoot failed, error code: %d\n", NET_DVR_GetLastError());
            return 0;
        }
        qDebug("网络触发连拍!\n");
    
        //    Sleep(20000); //等待接收数据
    
        //    //---------------------------------------
        //    //退出
    
        //    //撤销布防上传通道
        //    if (!NET_DVR_CloseAlarmChan_V30(lHandle))
        //    {
        
        //        qDebug("NET_DVR_CloseAlarmChan_V30 failed, error code: %d\n", NET_DVR_GetLastError());
        //        NET_DVR_Logout(lUserID);
        //        NET_DVR_Cleanup();
        //        return 0;
        //    }
    
        //    //注销用户
        //    NET_DVR_Logout(lUserID);
    
        //    //释放SDK资源
        //    NET_DVR_Cleanup();
    
        return 0;
    }
    
    int CameraThread::gpu_CheckPoint(int spec_x, int spec_y, cv::UMat frame)
    {
        
        ret=0;
        if(spec_x>frame.cols||spec_y>frame.rows||frame.empty())
        {
        
            return  0;
        }
        frameCount++;//不要放在上面，因为仅计算有效帧
        //根据分辨率的不同对检测区做相应的调整，只为适应不同的分辨率，默认值为w:50px,h:20px（352*255的分辨率）检测区域越大速度越慢，
        //漏检率就越高，建议用352*288的分辩率来检测车辆。
        roiWidth=50*frame.cols/352;
        if(frame.size().width==1920){
        
            roiHeight=20*1080/288;
        }else if(frame.size().width==1280){
        
            roiHeight=20*720/288;
        }else if(frame.size().width==704){
        
            roiHeight=20*576/288;
        }else if(frame.size().width==640){
        
            roiHeight=20*480/288;
        }else if(frame.size().width==352){
        
            roiHeight=20*288/288;
        }
        //定义矩形
        Rect virtualLine(spec_x, spec_y, roiWidth, roiHeight);//50 20
    
        //1、截取检测带
        frame(virtualLine).copyTo(subRegion_gpu);
    
        cv::cvtColor(subRegion_gpu, grayFrame_gpu,cv::COLOR_BGR2GRAY);
    
        // 增加亮度
        grayFrame_gpu.convertTo(grayFrame_gpu, -1, 1.5, 0); // 调整亮度的比例因子在这里设置
    
        // 如果有前一帧
        if (!prevFrame_gpu.empty())
        {
        
            // 计算当前帧与前一帧的差count值
            absdiff(prevFrame_gpu, grayFrame_gpu, frameDiff_gpu);
            //去噪点
            medianBlur(frameDiff_gpu,median_img_gpu,7); //防止抖动的关键代码不能去，去了就会自动加数，参数至少为7
            // 去除阴影
            cv::threshold(median_img_gpu, thres_img_gpu, 127, 255, cv::THRESH_BINARY);
    
            // 进行形态学操作以去除噪声和填充空洞,4,4最理想
            cv::Mat kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(2, 2));
            cv::morphologyEx(thres_img_gpu, thres_img_gpu, cv::MORPH_OPEN, kernel);
    
            // 必须做二值化处理差值图像，才能检到轮廓
            threshold(thres_img_gpu, thresholdedDiff_gpu, 30, 255, THRESH_BINARY);
    
            // 轮廓检测
            vector<vector<Point>> contours;
            vector<Vec4i> hierarchy;
            findContours(thresholdedDiff_gpu, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
    
    
            //         double t = 0.0;
            //         double t1 = 0.0;
            //         t = (double)cv::getTickCount();
            //         t1 = (double)cv::getTickCount();
            //         // 计算当前帧与前一帧的差值
            //         absdiff(prevFrame_gpu, grayFrame_gpu, frameDiff_gpu);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "absdiff Time Cost:" << t1 << std::endl;
            //         t1 = (double)cv::getTickCount();
            //         //去噪点
            //         medianBlur(frameDiff_gpu,median_img_gpu,7); //防止抖动的关键代码不能去，去了就会自动加数，参数至少为7
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "medianBlur Time Cost:" << t1 << "s" << std::endl;
            //         // 去除阴影
            //         t1 = (double)cv::getTickCount();
            //         cv::threshold(median_img_gpu, thres_img_gpu, 127, 255, cv::THRESH_BINARY);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "threshold Time Cost:" << t1 << "s" << std::endl;
            //         // 进行形态学操作以去除噪声和填充空洞,4,4最理想
            //         t1 = (double)cv::getTickCount();
            //         cv::Mat kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(2, 2));
            //         cv::morphologyEx(thres_img_gpu, thres_img_gpu, cv::MORPH_OPEN, kernel);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "morphologyEx Time Cost:" << t1 << "s" << std::endl;
    
            //         // 必须做二值化处理差值图像，才能检到轮廓
            //         t1 = (double)cv::getTickCount();
            //         threshold(thres_img_gpu, thresholdedDiff_gpu, 30, 255, THRESH_BINARY);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "threshold Time Cost:" << t1 << "s" << std::endl;
    
            //         // 轮廓检测
            //         t1 = (double)cv::getTickCount();
            //         vector<vector<Point>> contours;
            //         vector<Vec4i> hierarchy;
            //         findContours(thresholdedDiff_gpu, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "find Time Cost:" << t1 << "s" << std::endl;
    
            //         t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
            //         std::cout << "gpu Time Cost:" << t <<std::endl;
            //         std::cout<<"gpu access ended!"<<std::endl;
    
            //         framecount++;
            //         if(framecount>10){
        
            //             usedTimecnt+=t;
            //             double avg=usedTimecnt/(framecount-10);
    
            //             std::cout <<"framecount:"<<framecount<< " gpu Time avg:" << avg << std::endl;
            //         }
    
            //计数代码：
            //when the number of contours is not empty
            if(!contours.empty()){
        
                //come in area
                noContourFrameCount=0;//if have contours then the value=0
                if(!carin2){
        
                    cout<<"already come in"<<endl;
                    carin2=true;                         //come in detection area
                    carout2=false;                       //not leave detection area
                    nocar2=true;                         //have car
                    //               countTime=QTime::currentTime();
                    countTime.restart();
                    isCount2=false;                      //count flag
                    carIn_datetime=QTime::currentTime();
                    beginTime=QTime::currentTime();
                    if(pusleDataStatus==2){
        
                        pusleDataStatus =1;//表标车辆已进入检测区
                        checked_Car_Tag=QDateTime::currentDateTime().toString("yyyyMMddHHmmss_zzz");
                    }
    
                }
            }
            //when the number of contours is empty
            else{
        
                noContourFrameCount++;
                //when not counted
                if(noContourFrameCount>5){
        
                    if(!isCount2){
        
                        cout<<"already leave "<<endl;
                        carin2=false;          //car not come in detection area
                        carout2=true;          //car already leave detection area
                        nocar2=false;          //no have car
                        if(countTime.elapsed()>850){
        //设置这个参数主要是为了防止多检，比如客车过长容易多检 原：450
                            carCount++;
                            isCount2=true; //already count
                            carOut_datetime=QTime::currentTime();
                            if(pusleDataStatus==1){
        
                                pusleDataStatus =0;//表标车辆已离开检测区
                            }
                            ret=1;
                            cout<<"already count"<<endl;
                        }
                    }
                }
    
            }
        }
        // 保存当前帧及灰度图像作为前一帧
        prevFrame_gpu = grayFrame_gpu.clone();
    
        return ret;
    }
    
    int CameraThread::cpu_CheckPoint(int spec_x, int spec_y, cv::Mat frame)
    {
        
        ret=0;
        if(spec_x>frame.cols||spec_y>frame.rows||frame.empty())
        {
        
            return  0;
        }
        frameCount++;//不要放在上面，因为仅计算有效帧
        //根据分辨率的不同对检测区做相应的调整，只为适应不同的分辨率，默认值为w:50px,h:20px（352*255的分辨率）检测区域越大速度越慢，
        //漏检率就越高，建议用352*288的分辩率来检测车辆。
        roiWidth=50*frame.cols/352;
        if(frame.size().width==1920){
        
            roiHeight=20*1080/288;
        }else if(frame.size().width==1280){
        
            roiHeight=20*720/288;
        }else if(frame.size().width==704){
        
            roiHeight=20*576/288;
        }else if(frame.size().width==640){
        
            roiHeight=20*480/288;
        }else if(frame.size().width==352){
        
            roiHeight=20*288/288;
        }
        //定义矩形
        Rect virtualLine(spec_x, spec_y, roiWidth, roiHeight);//50 20
    
        //1、截取检测带
        frame(virtualLine).copyTo(subRegion_cpu);
    
        cv::cvtColor(subRegion_cpu, grayFrame,cv::COLOR_BGR2GRAY);
    
        // 增加亮度
        grayFrame.convertTo(grayFrame, -1, 1.5, 0); // 调整亮度的比例因子在这里设置
    
        // 如果有前一帧
        if (!prevFrame_cpu.empty())
        {
        
            // 计算当前帧与前一帧的差count值
            absdiff(prevFrame_cpu, grayFrame, frameDiff);
            //去噪点
            medianBlur(frameDiff,median_img,7); //防止抖动的关键代码不能去，去了就会自动加数，参数至少为7
            // 去除阴影
            cv::threshold(median_img, thres_img, 127, 255, cv::THRESH_BINARY);
    
            // 进行形态学操作以去除噪声和填充空洞,4,4最理想
            cv::Mat kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(2, 2));
            cv::morphologyEx(thres_img, thres_img, cv::MORPH_OPEN, kernel);
    
            // 必须做二值化处理差值图像，才能检到轮廓
            threshold(thres_img, thresholdedDiff, 30, 255, THRESH_BINARY);
    
            // 轮廓检测
            vector<vector<Point>> contours;
            vector<Vec4i> hierarchy;
            findContours(thresholdedDiff, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
    
            //         double t = 0.0;
            //         double t1 = 0.0;
            //         t = (double)cv::getTickCount();
            //         t1 = (double)cv::getTickCount();
            //         // 计算当前帧与前一帧的差值
            //         absdiff(prevFrame_cpu, grayFrame, frameDiff);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "absdiff Time Cost:" << t1 << "s" << std::endl;
            //         t1 = (double)cv::getTickCount();
            //         //去噪点
            //         medianBlur(frameDiff,median_img,7); //防止抖动的关键代码不能去，去了就会自动加数，参数至少为7
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "medianBlur Time Cost:" << t1 << "s" << std::endl;
            //         // 去除阴影
            //         t1 = (double)cv::getTickCount();
            //         cv::threshold(median_img, thres_img, 127, 255, cv::THRESH_BINARY);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "threshold Time Cost:" << t1 << "s" << std::endl;
            //         // 进行形态学操作以去除噪声和填充空洞,4,4最理想
            //         t1 = (double)cv::getTickCount();
            //         cv::Mat kernel = cv::getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(2, 2));
            //         cv::morphologyEx(thres_img, thres_img, cv::MORPH_OPEN, kernel);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "morphologyEx Time Cost:" << t1 << "s" << std::endl;
    
            //         // 必须做二值化处理差值图像，才能检到轮廓
            //         t1 = (double)cv::getTickCount();
            //         threshold(thres_img, thresholdedDiff, 30, 255, THRESH_BINARY);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "threshold Time Cost:" << t1 << "s" << std::endl;
    
            //         // 轮廓检测
            //         t1 = (double)cv::getTickCount();
            //         vector<vector<Point>> contours;
            //         vector<Vec4i> hierarchy;
            //         findContours(thresholdedDiff, contours, hierarchy, RETR_EXTERNAL, CHAIN_APPROX_SIMPLE);
            //         t1 = ((double)cv::getTickCount() - t1) / cv::getTickFrequency();
            //         std::cout << "find Time Cost:" << t1 << "s" << std::endl;
    
            //         t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
    
            //         std::cout << "cpu Time Cost:" << t << std::endl;
            //         std::cout<<"cpu access ended!"<<std::endl;
            //         framecount++;
            //         if(framecount>10){
        
            //             usedTimecnt+=t;
            //             double avg=usedTimecnt/(framecount-10);
            //             std::cout <<"framecount:"<<framecount<< " cpu Time avage:" << avg << std::endl;
            //         }
    
    
            //计数代码：
            //when the number of contours is not empty
            if(!contours.empty()){
        
                 noContourFrameCount=0;//if have contours then the value=0
                //come in area
                if(!carin2){
        
                    cout<<"already come in"<<endl;
                    carin2=true;                         //come in detection area
                    carout2=false;                       //not leave detection area
                    nocar2=true;                         //have car
                    //               countTime=QTime::currentTime();
                    countTime.restart();
                    isCount2=false;                      //count flag
                    carIn_datetime=QTime::currentTime();
                    beginTime=QTime::currentTime();
                    if(pusleDataStatus==2){
        
                        pusleDataStatus =1;//表标车辆已进入检测区
                        checked_Car_Tag=QDateTime::currentDateTime().toString("yyyyMMddHHmmss_zzz");
                    }
    
                }
            }
            //when the number of contours is empty
            else{
        
                noContourFrameCount++;
                //when not counted
                if(noContourFrameCount>10){
        
                    //when not counted
                    if(!isCount2){
        
                        cout<<"already leave "<<endl;
                        carin2=false;          //car not come in detection area
                        carout2=true;          //car already leave detection area
                        nocar2=false;          //no have car
                        if(countTime.elapsed()>650){
        //设置这个参数主要是为了防止多检，比如客车过长容易多检 原：450
                            carCount++;
                            isCount2=true; //already count
                            carOut_datetime=QTime::currentTime();
                            if(pusleDataStatus==1){
        
                                pusleDataStatus =0;//表标车辆已离开检测区
                            }
                            ret=1;
                            cout<<"already count"<<endl;
                        }
                    }
                }
    
            }
        }
        // 保存当前帧及灰度图像作为前一帧
        prevFrame_cpu = grayFrame.clone();
    
        return ret;
    }
    void CameraThread::gpu_process(UMat frame){
        
        std::vector<cv::ocl::PlatformInfo> plats;
        cv::ocl::getPlatfomsInfo(plats);
        const cv::ocl::PlatformInfo *platform = &plats[0];
        //cout << "Platform Name:" << platform->name().c_str() << endl;
        cv::ocl::Device dev;
        platform->getDevice(dev,0);
        //cout << "Device name:" << dev.name().c_str() << endl;
    
        cv::ocl::setUseOpenCL(true);
        //cout << "Use the OpenCL Deivice?" << cv::ocl::useOpenCL() << endl;
        double t = 0.0;
        UMat  grayFrame, edges;
        t = (double)cv::getTickCount();
        cv::cvtColor(frame,grayFrame,cv::COLOR_BGR2GRAY);
        // 进行边缘检测
        cv::Canny(grayFrame, edges, 50, 150);
        t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
        std::cout << "GPU Time Cost:" << t << "s" << std::endl;
        putText(edges, "Platform : " + platform->name(), Point(5, 30), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
        putText(edges, "Device : " + dev.name(), Point(5, 60), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
        putText(edges, "Time : " + std::to_string(t) + " s", Point(5, 90), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
    
    }
    void CameraThread::cpu_process(Mat frame){
        
        std::vector<cv::ocl::PlatformInfo> plats;
        cv::ocl::getPlatfomsInfo(plats);
        const cv::ocl::PlatformInfo *platform = &plats[0];
        //cout << "Platform Name:" << platform->name().c_str() << endl;
        cv::ocl::Device dev;
        platform->getDevice(dev,0);
        // cout << "Device name:" << dev.name().c_str() << endl;
    
        cv::ocl::setUseOpenCL(false);
        //cout << "Use the OpenCL Deivice?" << cv::ocl::useOpenCL() << endl;
        double t = 0.0;
        Mat grayFrame, edges;
        t = (double)cv::getTickCount();
        cv::cvtColor(frame,grayFrame,cv::COLOR_RGB2GRAY);
        // 进行边缘检测
        cv::Canny(grayFrame, edges, 50, 150);
        t = ((double)cv::getTickCount() - t) / cv::getTickFrequency();
        // std::cout << "CPU Time Cost:" << t << "s" << std::endl;
        putText(edges, "Time : " + std::to_string(t) + " s", Point(5, 30), FONT_HERSHEY_SIMPLEX, 1., Scalar(255, 100, 0), 2);
    }
    void CameraThread::run()
    {
        
    
        cv::VideoCapture cap(cgd.rtsp[cameraIndex],CAP_ANY);
        if (!cap.isOpened()) {
        
            qWarning() << "Failed to open camera" << cameraIndex;
            return;
        }
        int ms=0;
        int count=0;
        while (!isInterruptionRequested()) {
        
            cv::UMat frame;
            cap >> frame;
    
            if (frame.empty())
                continue;
    
            // 将OpenCV Mat转换为QImage
            Mat temp;
            frame.copyTo(temp);
            QImage qImage = QImage((uchar*)(temp.data), temp.cols, temp.rows, temp.step, QImage::Format_RGB888).copy();
            qImage = qImage.rgbSwapped(); // 交换BGR到RGB格式
            int x=0,y=0;
            if(cameraIndex==0){
        
                if(qImage.width()==1280){
        
                    x=360;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;
                    x=360*xs;
                    y=330*xs;
                }
            }else if(cameraIndex==1){
        
                if(qImage.width()==1280){
        
                    x=430;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=430*xs;
                    y=330*xs;
                }
    
            }else if(cameraIndex==2){
        
                if(qImage.width()==1280){
        
                    x=100;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=100*xs;
                    y=330*xs;
                }
    
            }else if(cameraIndex==3){
        
                if(qImage.width()==1280){
        
                    x=460;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=460*xs;
                    y=330*xs;
                }
    
            }else if(cameraIndex==4){
        
                if(qImage.width()==1280){
        
                    x=300;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=300*xs;
                    y=330*xs;
                }
    
    
    
            }else if(cameraIndex==5){
        
                if(qImage.width()==1280){
        
                    x=560;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=560*xs;
                    y=330*xs;
                }
    
            }else if(cameraIndex==6){
        
                if(qImage.width()==1280){
        
                    x=360;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=360*xs;
                    y=330*xs;
                }
    
            }else if(cameraIndex==7){
        
                if(qImage.width()==1280){
        
                    x=460;
                    y=330;
                }else if(qImage.width()==1920){
        
                    double xs=1920.0/1280.0;;
                    x=460*xs;
                    y=330*xs;
                }
    
    
            }
    
            //count+=gpu_CheckPoint(x,y,frame);//1080X720
            QFont font("Arial",36,QFont::Bold);
            QPen pen(Qt::yellow);
            pen.setWidth(1);
            QPainter painter(&qImage);
            painter.setFont(font);
            painter.setPen(pen);
    
            painter.drawText(QPointF(x,y),"Cnt:"+QString::number(count));
            pen.setWidth(2);
            painter.drawRect(x,y,1280/352*50,720/288*20);
    
            painter.end();
    
            // 发送信号更新主窗口的QLabel
            emit frameReady(qImage, cameraIndex);
            emit frameReady2(cameraIndex,count_1);
    
    
            //        QTime startTime=QTime::currentTime();
            //gpu_process(frame);
    
            //        count++;
            //        ms+=startTime.msecsTo(QTime::currentTime());
            //        std::cout<<"cpu:"<<ms<<" count:"<<count<<std::endl;
        }
        cap.release();
    }

四、主窗口  
头文件：

#ifndef MAINWINDOW_H
    #define MAINWINDOW_H
    
    #include <QMainWindow>
    #include <QLabel>
    #include <QVector>
    #include <QThread>
    #include "ui_mainwindow.h"
    using namespace  std;
    
    
    class MainWindow : public QWidget
    {
        
        Q_OBJECT
    
    public:
        explicit MainWindow(QWidget *parent = nullptr);
        ~MainWindow();
    
    
    
    private:
       // Ui::MainWindow *ui;
        QVector<QLabel*> labelList;
        QVector<QThread*> threadList;
    
    
    };
    
    #endif // MAINWINDOW_H

源文件：

#include "mainwindow.h"
    #include "camerathread.h"
    #include <QHBoxLayout>
    #include <QPen>
    #include <QPainter>
    #include <QPointF>
    
    MainWindow::MainWindow(QWidget *parent)
        : QWidget(parent)
    {
        
        QVBoxLayout *verticalLayout=new QVBoxLayout();
        QHBoxLayout *horizontaiLayout_1=new QHBoxLayout();
        QHBoxLayout *horizontaiLayout_2=new QHBoxLayout();
    
        // 初始化8个QLabel并添加到主窗口
        for (int k = 0; k < 4; ++k) {
        
            QLabel *label = new QLabel();
            label->setStyleSheet("border:1px solid gray");
            label->setFixedSize(352,288);
            label->setScaledContents(true);
            horizontaiLayout_1->addWidget(label);
            labelList.append(label);
        }
        for (int j = 0; j < 4; ++j) {
        
            QLabel *label2 = new QLabel();
            label2->setStyleSheet("border:1px solid gray");
            label2->setFixedSize(352,288);
            label2->setScaledContents(true);
            horizontaiLayout_2->addWidget(label2);
            labelList.append(label2);
        }
        verticalLayout->addLayout(horizontaiLayout_1);
        verticalLayout->addLayout(horizontaiLayout_2);
        setLayout(verticalLayout);
        setFixedSize(1600,800);
    
        std::cout<<"labelList length:"<<labelList.length()<<std::endl;
    
    
    
        for (int i = 0; i < 8; ++i) {
        
            CameraThread *cameraThread = new CameraThread(labelList[i],i,this);
            QThread *thread = new QThread(this);
            cameraThread->moveToThread(thread);
    
            connect(thread, &QThread::started, this, [cameraThread]() {
        
                cameraThread->start();
            });
            connect(cameraThread, &CameraThread::frameReady, this, [this](const QImage &frame, int index) {
        
                //std::cout<<"index:"<<index<<std::endl;
                labelList[index]->setPixmap(QPixmap::fromImage(frame));
            });
    //        connect(cameraThread, &CameraThread::frameReady2, this, [this](int index,int count) {
        
    //            std::cout<<"index:"<<index<<std::endl;
    //            labelList[index]->setText(QString::number(count));
    //        });
            connect(cameraThread, &QThread::finished, thread, &QThread::deleteLater);
            connect(thread, &QThread::finished, cameraThread, &CameraThread::deleteLater);
    
            threadList.append(thread);
            thread->start();
            QThread::msleep(30);
        }
    }
    
    MainWindow::~MainWindow()
    {
        
    
        for (QThread *thread : threadList) {
        
            thread->quit();
            thread->wait();
        }
    
    
    }

五、pro文件：

QT       += core gui
    
    greaterThan(QT_MAJOR_VERSION, 4): QT += widgets
    
    CONFIG += c++11
    
    # You can make your code fail to compile if it uses deprecated APIs.
    # In order to do so, uncomment the following line.
    #DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0
    win32{
        
        INCLUDEPATH += D:/opencv4.1.2/build/install/include/
        LIBS += D:/opencv4.1.2/build/install/x86/mingw/bin/libopencv_*.dll
    
    }
    
    SOURCES += \
        cameraglobaldata.cpp \
        camerathread.cpp \
        main.cpp \
        mainwindow.cpp
    
    HEADERS += \
        cameraglobaldata.h \
        camerathread.h \
        mainwindow.h
    
    FORMS += \
        mainwindow.ui
    
    # Default rules for deployment.
    qnx: target.path = /tmp/$${
        TARGET}/bin
    else: unix:!android: target.path = /opt/$${
        TARGET}/bin
    !isEmpty(target.path): INSTALLS += target
    
    unix:!macx: LIBS +=/usr/lib/aarch64-linux-gnu/libopencv_*.so.4.5.1
    
    INCLUDEPATH += $$PWD/../../../usr/include/opencv4
    DEPENDPATH += $$PWD/../../../usr/include/opencv4
    
    
    
    win32: LIBS += -L'D:/Program Files/HKVISION32/lib/' -lHCNetSDK
    INCLUDEPATH += 'D:/Program Files/HKVISION32/include'
    DEPENDPATH += 'D:/Program Files/HKVISION32/include'
    win32:!win32-g++: PRE_TARGETDEPS += 'D:/Program Files/HKVISION32/lib/HCNetSDK.lib'
    else:win32-g++: PRE_TARGETDEPS += 'D:/Program Files/HKVISION32/lib/HCNetSDK.lib'

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