Обнаружение объектов OpenCV GPU происходит медленно и дает меньше обнаружений по сравнению с версией процессора

Ниже приведены версии CPU и GPU кода обнаружения объекта из OpenCV.

1) Реализация графического процессора медленная по сравнению с версией процессора

2) Скорость обнаружения медленна по сравнению с версией кода ЦП для того же классификатора

Любая идея, почему это так?

Версия процессора CODE

#include <windows.h>
#include <mmsystem.h>
#pragma comment(lib, "winmm.lib")

#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

int main(int argc, const char** argv)
{
    //create the cascade classifier object used for the face detection
    CascadeClassifier face_cascade;
    //use the haarcascade_frontalface_alt.xml library
    face_cascade.load("C:/cascades/haarcascade_frontalface_alt_tree.xml");

    //setup video capture device and link it to the first capture device
    VideoCapture captureDevice;
    captureDevice.open(3);

    //setup image files used in the capture process
    Mat captureFrame;
    Mat grayscaleFrame;

    //create a window to present the results
    namedWindow("outputCapture", 1);

    //create a loop to capture and find faces
    while(true)
    {
        //capture a new image frame
        captureDevice>>captureFrame;

        //convert captured image to gray scale and equalize
        cvtColor(captureFrame, grayscaleFrame, CV_BGR2GRAY);
        equalizeHist(grayscaleFrame, grayscaleFrame);

    //create a vector array to store the face found
    std::vector<Rect> faces;

    //find faces and store them in the vector array
    face_cascade.detectMultiScale(grayscaleFrame, faces);

    //draw a rectangle for all found faces in the vector array on the original image
    for(int i = 0; i < (int)faces.size(); i++)
    {
        Scalar color(0, 0, 255);

        Point pt1(faces[i].x + faces[i].width, faces[i].y + faces[i].height);
        Point pt2(faces[i].x, faces[i].y);

        rectangle(captureFrame, pt1, pt2, color, 1, 8, 0);

        string text = "Adam yuzi";
        int fontFace = FONT_HERSHEY_TRIPLEX;
        double fontScale = 1;
        int thickness = 2;  

        putText(captureFrame, text, pt2, fontFace, fontScale, color, thickness);
        //PlaySound(TEXT("C:/cascades/adam.wav"), NULL, SND_FILENAME | SND_SYNC);
        // the correct code
        //Sleep(1000);
        //break;
        //cout<<char(7);
        }
       //print the output
        imshow("outputCapture", captureFrame);

       //pause for 33ms
        waitKey(33);
    }
    return 0;
}

и реализация версии графического процессора представлена в этом примере чернил. GPU Version CODE

// WARNING: this sample is under construction! Use it on your own risk.
#if defined _MSC_VER && _MSC_VER >= 1400
#pragma warning(disable : 4100)
#endif


#include <iostream>
#include <iomanip>
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/cuda.hpp"
#include "opencv2/cudaimgproc.hpp"
#include "opencv2/cudawarping.hpp"

using namespace std;
using namespace cv;
using namespace cv::cuda;

static void help()
{
    cout << "Usage: ./cascadeclassifier_gpu \n\t--cascade <cascade_file>\n\t(<image>|--    video <video>|--camera <camera_id>)\n"
            "Using OpenCV version " << CV_VERSION << endl << endl;
}


static void convertAndResize(const Mat& src, Mat& gray, Mat& resized, double scale)
{
    if (src.channels() == 3)
    {
        cv::cvtColor( src, gray, COLOR_BGR2GRAY );
    }
    else
    {
        gray = src;
    }

    Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));

    if (scale != 1)
    {
        cv::resize(gray, resized, sz);
    }
    else
    {
        resized = gray;
    }
}

static void convertAndResize(const GpuMat& src, GpuMat& gray, GpuMat& resized, double     scale)
{
    if (src.channels() == 3)
    {
        cv::cuda::cvtColor( src, gray, COLOR_BGR2GRAY );
    }
    else
    {
        gray = src;
    }

    Size sz(cvRound(gray.cols * scale), cvRound(gray.rows * scale));

    if (scale != 1)
    {
        cv::cuda::resize(gray, resized, sz);
    }
    else
    {
        resized = gray;
    }
}
static void matPrint(Mat &img, int lineOffsY, Scalar fontColor, const string &ss)
{
    int fontFace = FONT_HERSHEY_DUPLEX;
    double fontScale = 0.8;
    int fontThickness = 2;
    Size fontSize = cv::getTextSize("T[]", fontFace, fontScale, fontThickness, 0);

    Point org;
    org.x = 1;
    org.y = 3 * fontSize.height * (lineOffsY + 1) / 2;
    putText(img, ss, org, fontFace, fontScale, Scalar(0,0,0), 5*fontThickness/2, 16);
    putText(img, ss, org, fontFace, fontScale, fontColor, fontThickness, 16);
}


static void displayState(Mat &canvas, bool bHelp, bool bGpu, bool bLargestFace, bool     bFilter, double fps)
{
    Scalar fontColorRed = Scalar(255,0,0);
    Scalar fontColorNV  = Scalar(118,185,0);

    ostringstream ss;
    ss << "FPS = " << setprecision(1) << fixed << fps;
    matPrint(canvas, 0, fontColorRed, ss.str());
    ss.str("");
    ss << "[" << canvas.cols << "x" << canvas.rows << "], " <<
        (bGpu ? "GPU, " : "CPU, ") <<
        (bLargestFace ? "OneFace, " : "MultiFace, ") <<
        (bFilter ? "Filter:ON" : "Filter:OFF");
    matPrint(canvas, 1, fontColorRed, ss.str());

    // by Anatoly. MacOS fix. ostringstream(const string&) is a private
    // matPrint(canvas, 2, fontColorNV, ostringstream("Space - switch GPU / CPU"));
   if (bHelp)
    {
        matPrint(canvas, 2, fontColorNV, "Space - switch GPU / CPU");
        matPrint(canvas, 3, fontColorNV, "M - switch OneFace / MultiFace");
        matPrint(canvas, 4, fontColorNV, "F - toggle rectangles Filter");
        matPrint(canvas, 5, fontColorNV, "H - toggle hotkeys help");
        matPrint(canvas, 6, fontColorNV, "1/Q - increase/decrease scale");
    }
    else
    {
        matPrint(canvas, 2, fontColorNV, "H - toggle hotkeys help");
    }
}


int main(int argc, const char *argv[])
{
    if (argc == 1)
    {
        help();
        return -1;
    }

    if (getCudaEnabledDeviceCount() == 0)
    {
        return cerr << "No GPU found or the library is compiled without CUDA support"     << endl, -1;
    }

    cv::cuda::printShortCudaDeviceInfo(cv::cuda::getDevice());

    string cascadeName;
    string inputName;
    bool isInputImage = false;
    bool isInputVideo = false;
    bool isInputCamera = false;

    for (int i = 1; i < argc; ++i)
    {
        if (string(argv[i]) == "--cascade")
            cascadeName = argv[++i];
        else if (string(argv[i]) == "--video")
        {
            inputName = argv[++i];
            isInputVideo = true;
        }
        else if (string(argv[i]) == "--camera")
        {
            inputName = argv[++i];
            isInputCamera = true;
        }
        else if (string(argv[i]) == "--help")
        {
            help();
            return -1;
        }    
        else if (!isInputImage)
        {
            inputName = argv[i];
            isInputImage = true;
        }
        else
        {
            cout << "Unknown key: " << argv[i] << endl;
            return -1;
        }
    }

    CascadeClassifier_CUDA cascade_gpu;
    if (!cascade_gpu.load(cascadeName)){
        return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName <<     "\"" << endl, help(), -1;
    }

    CascadeClassifier cascade_cpu;
    if (!cascade_cpu.load(cascadeName)) {
        return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName <<     "\"" << endl, help(), -1;
    }

    VideoCapture capture;
    Mat image;

    if (isInputImage) {
        image = imread(inputName);
        CV_Assert(!image.empty());
        }
    else if (isInputVideo) {
        capture.open(inputName);
        CV_Assert(capture.isOpened());
    }
else   {
        capture.open(atoi(inputName.c_str()));
        CV_Assert(capture.isOpened());
    }

    namedWindow("result", 1);

    Mat frame, frame_cpu, gray_cpu, resized_cpu, faces_downloaded, frameDisp;
    vector<Rect> facesBuf_cpu;

    GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;

/* parameters */
    bool useGPU = true;
    double scaleFactor = 1.0;
    bool findLargestObject = false;
    bool filterRects = true;
    bool helpScreen = false;

    int detections_num;
    for (;;)    {
        if (isInputCamera || isInputVideo)        {
            capture >> frame;
            if (frame.empty())            {
                break;
            }
        }

        (image.empty() ? frame : image).copyTo(frame_cpu);
        frame_gpu.upload(image.empty() ? frame : image);

        convertAndResize(frame_gpu, gray_gpu, resized_gpu, scaleFactor);
        convertAndResize(frame_cpu, gray_cpu, resized_cpu, scaleFactor);

        TickMeter tm;
        tm.start();

    if (useGPU)        {
            //cascade_gpu.visualizeInPlace = true;
            cascade_gpu.findLargestObject = findLargestObject;

            detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu,     1.2,
                                                          (filterRects ||     findLargestObject) ? 4 : 0);
            facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);
        }
        else        {
            Size minSize = cascade_gpu.getClassifierSize();
            cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
                                         (filterRects || findLargestObject) ? 4 : 0,
                                         (findLargestObject ?     CASCADE_FIND_BIGGEST_OBJECT : 0)
                                            | CASCADE_SCALE_IMAGE,
                                         minSize);
            detections_num = (int)facesBuf_cpu.size();
        }

        if (!useGPU && detections_num)      {
            for (int i = 0; i < detections_num; ++i)            {
                rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255));
            }
        }

        if (useGPU)        {
            resized_gpu.download(resized_cpu);
             for (int i = 0; i < detections_num; ++i)     {
                rectangle(resized_cpu, faces_downloaded.ptr<cv::Rect>()[i],     Scalar(255));
             }
        }

           tm.stop();
        double detectionTime = tm.getTimeMilli();
        double fps = 1000 / detectionTime;
        //print detections to console
        cout << setfill(' ') << setprecision(2);
        cout << setw(6) << fixed << fps << " FPS, " << detections_num << " det";
    if ((filterRects || findLargestObject) && detections_num > 0)        {
            Rect *faceRects = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0];
            for (int i = 0; i < min(detections_num, 2); ++i)            {
                cout << ", [" << setw(4) << faceRects[i].x
                     << ", " << setw(4) << faceRects[i].y
                         << ", " << setw(4) << faceRects[i].width
                         << ", " << setw(4) << faceRects[i].height << "]";
                    }
            }
            cout << endl;

            cv::cvtColor(resized_cpu, frameDisp, COLOR_GRAY2BGR);
            displayState(frameDisp, helpScreen, useGPU, findLargestObject, filterRects,     fps);
            imshow("result", frameDisp);

            char key = (char)waitKey(5);
            if (key == 27)        {
                break;
            }    
            switch (key)            {
            case ' ':
                useGPU = !useGPU;
                break;
            case 'm':
            case 'M':
                findLargestObject = !findLargestObject;
                break;
            case 'f':
                case 'F':
                filterRects = !filterRects;
                break;
            case '1':
                scaleFactor *= 1.05;
                break;
                case 'q':
            case 'Q':
                scaleFactor /= 1.05;
                break;
            case 'h':
            case 'H':
                helpScreen = !helpScreen;
                break;
            }
        }
        return 0;
    }

ПРИМЕЧАНИЕ. Я не писал этот код, я взял версию процессора и версию GPU отсюда. Я также отправил свои observatios в.