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以下是我的代码，它用于使用 SURF 提取特征，并将使用 flannBasedMatcher 匹配点。

Mat object = imread("S6E0.bmp",  CV_LOAD_IMAGE_GRAYSCALE);

    if( !object.data )
    {
    // std::cout<< "Error reading object " << std::endl;
    return -2;
    }

    //Detect the keypoints using SURF Detector

    int minHessian = 500;

    SurfFeatureDetector 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;
    char key = 'a';
    //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 );

    Mat image = imread("S6E0.bmp", CV_LOAD_IMAGE_GRAYSCALE);
    Mat des_image, img_matches;

    std::vector<KeyPoint> kp_image;
    std::vector<vector<DMatch >> matches;

    std::vector<std::vector<cv::DMatch>> matches1;
    std::vector<std::vector<cv::DMatch>> matches2;
    std::vector<cv::DMatch> matches3;
    std::vector<DMatch > good_matches;
    std::vector<Point2f> obj;
    std::vector<Point2f> scene;

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

    Mat H;

    //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)
    {
        printf("Images matching %d , %d", good_matches.size(), kp_object.size());

        //return 1;

        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 );
        //printf("Size : %d", H.size());
        //perspectiveTransform( obj_corners, scene_corners, H);
        //printf("Size : %d --- %d --- %d", H.size(), scene_corners.size()); 

    }else{

        printf("Images matching %d , %d", good_matches.size(), kp_object.size());
    }

        //Show detected matches

    imshow( "Good Matches", img_matches );
    waitKey(0);
    return 0;

在这段代码中，我想知道通过这种方法到底发生了什么

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

据我所知，我传递了匹配图像的两个描述符，并且匹配向量填充了 2 个最近的邻居。我想知道该方法中究竟发生了什么以及如何填充matches方法以及填充了哪些点。

在此代码段中

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]);
        }
    }

我使用最近邻距离比（nndr）作为 0.6，我想知道 good_matches 是如何找出的以及 nndr 值变化将如何影响。

如果我能解决此代码，那将是一个很大的帮助。谢谢。

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1 回答 1

3

FlannBasedMatcher基于 Muja 等人撰写的论文。人。; 您可以在那里找到确切的算法以及它们是如何进行的..

关于good_matches，您刚刚在代码片段本身中看到，它是您的结果基于标准的最佳匹配的集合，即nndr .. 它基本上是一个阈值，用于决定在丢弃之前允许匹配多远完全匹配.. 阈值越高，考虑的点越多，正匹配的数量越多（它们是否为真正将取决于您的数据集和您设置 nndr 级别的方式）..

希望这可以帮助。

于 2013-11-03T07:44:08.623 回答