Open CV object detection : ORB_GPU detector and SURF_GPU descriptor extractor

asked 2014-06-17 10:05:58 -0500

I was just making a small experiment to play around with different detector/descriptor combinations.

My code uses an ORB_GPU detector for detection of features and SURF_GPU descriptor for calculating the descriptors. I uses a BruteForceMatcher_GPU to match the descriptors and i am suing the knnMatch method to get the matches. The problem is I am getting a lot of unwanted matches, the code is literally matching every feature it could find in both the images. I am quite confused with this behavior. Following is my code ( GPU version ).

    #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( ! )
        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;

    if( !
        std::cout<< "Error reading object " << std::endl;
        return -1;

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

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

    SURF_GPU extractor;


    BruteForceMatcher_GPU<L2 <float>> matcher;

    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)

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

        cvtColor(frame, image, CV_RGB2GRAY);



        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))

        //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 ...
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