c++ - Open CV オブジェクト検出: ORB_GPU 検出器と SURF_GPU 記述子抽出器

Question

私は、さまざまな検出器/記述子の組み合わせをいじる小さな実験を行っていました。

私のコードは、特徴の検出に ORB_GPU 検出器を使用し、記述子の計算に SURF_GPU 記述子を使用します。BruteForceMatcher_GPU を使用して記述子を照合し、knnMatch メソッドを使用して一致を取得しています。問題は、多くの不要な一致を取得していることです。コードは、両方の画像で見つけることができるすべての機能に文字通り一致しています。私はこの行動にかなり混乱しています。以下は私のコードです（GPUバージョン）

    #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;
}

図に見られるように、コードは何に対してもランダムな一致を与えています。通常のCPU機能を使用して同じことを試してみましたが、かなり正確です。CPUバージョンのコードは以下です

#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;
}

どんな助けでも大歓迎です。