C++ 开放式CV对象检测：ORB_GPU检测器和SURF_GPU描述符提取器

我只是做了一个小实验，用不同的检测器/描述符组合进行测试

我的代码使用ORB_GPU检测器检测特征，使用SURF_GPU描述符计算描述符。我使用一个BruteForceMatcherGPU来匹配描述符，我正在使用knnMatch方法来获得匹配。问题是我得到了很多不需要的匹配，代码实际上匹配了它在两幅图像中找到的每一个特征。我对这种行为感到很困惑。以下是我的代码（GPU版本）

#包括“stdafx.h”
#包括
#包括
#包括“opencv2/core/core.hpp”
#包括“opencv2/nonfree/features2d.hpp”
#包括“opencv2/highgui/highgui.hpp”
#包括“opencv2/imgproc/imgproc.hpp”
#包括“opencv2/calib3d/calib3d.hpp”
#包括“opencv2/gpu/gpu.hpp”
#包括“opencv2/nonfree/gpu.hpp”
使用名称空间cv；
使用名称空间cv:：gpu；
int main（）
{
Mat object=imread（“140614-194209.jpg”，CV\u LOAD\u IMAGE\u灰度）；
如果（！object.data）
{
标准：：cout=4）
{
for（int i=0；i

---

任何帮助都将不胜感激

你有没有找到解决问题的办法？我在使用OpenCV 3.2时遇到了同样的问题。对不起，我不记得了。我记得我曾试图解决这个问题，但我不记得我是否使用了ORB和SURF

    #include "stdafx.h"
    #include <stdio.h>
    #include <iostream>
    #include "opencv2/core/core.hpp"
    #include "opencv2/nonfree/features2d.hpp"
    #include "opencv2/highgui/highgui.hpp"
    #include "opencv2/imgproc/imgproc.hpp"
    #include "opencv2/calib3d/calib3d.hpp"
    #include "opencv2/gpu/gpu.hpp"
    #include "opencv2/nonfree/gpu.hpp"

    using namespace cv;
    using namespace cv::gpu;

    int main()
    {
    Mat object = imread( "140614-194209.jpg", CV_LOAD_IMAGE_GRAYSCALE );
    if( !object.data )
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    GpuMat object_gpu; 
    GpuMat object_gpukp;
    GpuMat object_gpudsc;
    vector<float> desc_object_cpu;
    std::vector<KeyPoint> kp_object;
    int minHessian = 400;

    object_gpu.upload(object);
    if( !object_gpu.data)
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    GpuMat mask(object_gpu.size(), CV_8U, 0xFF);
    mask.setTo(0xFF);

    ORB_GPU detector = ORB_GPU(minHessian);
    detector.blurForDescriptor = true;

    SURF_GPU extractor;

    detector(object_gpu,GpuMat(),object_gpukp);
    extractor(object_gpu,GpuMat(),object_gpukp,object_gpudsc,true);

    BruteForceMatcher_GPU<L2 <float>> matcher;

    detector.downloadKeyPoints(object_gpukp,kp_object);
    extractor.downloadDescriptors(object_gpudsc,desc_object_cpu);
    Mat descriptors_test_CPU_Mat(desc_object_cpu);

    VideoCapture cap(0);

    namedWindow("Good Matches");

    std::vector<Point2f> obj_corners(4);

    //Get the corners from the object
    obj_corners[0] = cvPoint(0,0);
    obj_corners[1] = cvPoint( object.cols, 0 );
    obj_corners[2] = cvPoint( object.cols, object.rows );
    obj_corners[3] = cvPoint( 0, object.rows );
    unsigned long AAtime=0, BBtime=0; 
    unsigned long Time[110];

    char key = 'a';
    int framecount = 0;
    int count = 0;

    while (key != 27)
    {
        Mat frame;
        Mat img_matches;
        std::vector<KeyPoint> kp_image;
        std::vector<vector<DMatch > > matches;
        std::vector<DMatch > good_matches;
        std::vector<Point2f> obj;
        std::vector<Point2f> scene;
        std::vector<Point2f> scene_corners(4);
        vector<float> desc_image_cpu;
        Mat H;
        Mat image;
        GpuMat image_gpu;
        GpuMat image_gpukp;
        GpuMat image_gpudsc;

        cap >> frame;

        if (framecount < 5)
        {
            framecount++;
            continue;
        }

        if(count == 0) 
        {
            AAtime = getTickCount(); 
        }

        cvtColor(frame, image, CV_RGB2GRAY);


        image_gpu.upload(image);
        detector(image_gpu,GpuMat(),image_gpukp);
        extractor(image_gpu,GpuMat(),image_gpukp,image_gpudsc,true);

        matcher.knnMatch(object_gpudsc,image_gpudsc,matches,2);
        detector.downloadKeyPoints(image_gpukp,kp_image);
        extractor.downloadDescriptors(image_gpudsc,desc_image_cpu);

        Mat des_image(desc_image_cpu);

        for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
        {

            if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
            {
                good_matches.push_back(matches[i][0]);
            }
        }

        //Draw only "good" matches
        drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
        if (good_matches.size() >= 14)
        {
            for( int i = 0; i < good_matches.size(); i++ )
            {
               //Get the keypoints from the good matches
               obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
               scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
            }

            H = findHomography( obj, scene, CV_RANSAC );

            perspectiveTransform( obj_corners, scene_corners, H);

            //Draw lines between the corners (the mapped object in the scene image )
            line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
            line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
        }

        //Show detected matches
        imshow( "Good Matches", img_matches );
        matcher.clear();
        detector.release();
        BBtime = getTickCount(); 
        count++;

        if(count == 10000)
        {
            BBtime = getTickCount();
            printf("Processing time = %.2lf(sec) \n",  (BBtime - AAtime)/getTickFrequency() );
            break;
        }
        extractor.releaseMemory();
        detector.release();
        key = waitKey(1);
    }
    return 0;
}

#include "stdafx.h"
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "opencv2/calib3d/calib3d.hpp"

using namespace cv;

int main()
{
    Mat object = imread( "140614-194209.jpg", CV_LOAD_IMAGE_GRAYSCALE );

    if( !object.data )
    {
        std::cout<< "Error reading object " << std::endl;
        return -1;
    }

    //Detect the keypoints using SURF Detector
    int minHessian = 500;

    OrbFeatureDetector detector( minHessian );
    std::vector<KeyPoint> kp_object;

    detector.detect( object, kp_object );

    //Calculate descriptors (feature vectors)
    SurfDescriptorExtractor extractor;
    Mat des_object;

    extractor.compute( object, kp_object, des_object );

    FlannBasedMatcher matcher;

    VideoCapture cap(0);

    namedWindow("Good Matches");

    std::vector<Point2f> obj_corners(4);

    //Get the corners from the object
    obj_corners[0] = cvPoint(0,0);
    obj_corners[1] = cvPoint( object.cols, 0 );
    obj_corners[2] = cvPoint( object.cols, object.rows );
    obj_corners[3] = cvPoint( 0, object.rows );

    char key = 'a';
    int framecount = 0;
    while (key != 27)
    {
        Mat frame;
        cap >> frame;

        if (framecount < 5)
        {
            framecount++;
            continue;
        }

        Mat des_image, img_matches;
        std::vector<KeyPoint> kp_image;
        std::vector<vector<DMatch > > matches;
        std::vector<DMatch > good_matches;
        std::vector<Point2f> obj;
        std::vector<Point2f> scene;
        std::vector<Point2f> scene_corners(4);
        Mat H;
        Mat image;

        cvtColor(frame, image, CV_RGB2GRAY);

        detector.detect( image, kp_image );
        extractor.compute( image, kp_image, des_image );

        matcher.knnMatch(des_object, des_image, matches, 2);

        for(int i = 0; i < min(des_image.rows-1,(int) matches.size()); i++) //THIS LOOP IS SENSITIVE TO SEGFAULTS
        {
            if((matches[i][0].distance < 0.6*(matches[i][1].distance)) && ((int) matches[i].size()<=2 && (int) matches[i].size()>0))
            {
                good_matches.push_back(matches[i][0]);
            }
        }

        //Draw only "good" matches
        drawMatches( object, kp_object, image, kp_image, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

        if (good_matches.size() >= 4)
        {
            for( int i = 0; i < good_matches.size(); i++ )
            {
                //Get the keypoints from the good matches
                obj.push_back( kp_object[ good_matches[i].queryIdx ].pt );
                scene.push_back( kp_image[ good_matches[i].trainIdx ].pt );
            }

            H = findHomography( obj, scene, CV_RANSAC );

            perspectiveTransform( obj_corners, scene_corners, H);

            //Draw lines between the corners (the mapped object in the scene image )
            line( img_matches, scene_corners[0] + Point2f( object.cols, 0), scene_corners[1] + Point2f( object.cols, 0), Scalar(0, 255, 0), 4 );
            line( img_matches, scene_corners[1] + Point2f( object.cols, 0), scene_corners[2] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[2] + Point2f( object.cols, 0), scene_corners[3] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
            line( img_matches, scene_corners[3] + Point2f( object.cols, 0), scene_corners[0] + Point2f( object.cols, 0), Scalar( 0, 255, 0), 4 );
        }

        //Show detected matches
        imshow( "Good Matches", img_matches );

        key = waitKey(1);
    }
    return 0;
}