OpenCV Surf和离群值检测 [英] OpenCV Surf and Outliers detection

问题描述

我知道这里已经有几个关于同一主题的问题，但是我找不到任何帮助.

I know there are already several questions with the same subject asked here, but I couldn't find any help.

因此，我想比较2张图像以查看它们的相似程度，因此我使用了众所周知的find_obj.cpp演示程序来提取冲浪描述符，然后使用flannFindPairs进行匹配.

So I want to compare 2 images in order to see how similar they are and I'm using the well known find_obj.cpp demo to extract surf descriptors and then for the matching I use the flannFindPairs.

但是，正如您所知，该方法不会丢弃异常值，因此我想知道真实正匹配的次数，因此我可以确定这两个图像的相似程度.

But as you know this method doesn't discard the outliers and I'd like to know the number of true positive matches so I can figure how similar those two images are.

我已经看到了以下问题:在SURF中检测异常值或带有OpenCV的SIFT算法，那里的人建议使用findFundamentalMat，但是一旦获得基本矩阵，如何从该矩阵中获得离群值/真实正值?谢谢你.

I have already seen this question: Detecting outliers in SURF or SIFT algorithm with OpenCV and the guy there suggests to use the findFundamentalMat but once you get the fundamental matrix how can I get the number of outliers/true positive from that matrix? Thank you.

推荐答案

以下是

Here is a snippet from the descriptor_extractor_matcher.cpp sample available from OpenCV:

if( !isWarpPerspective && ransacReprojThreshold >= 0 )
    {
        cout << "< Computing homography (RANSAC)..." << endl;
        vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
        vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
        H12 = findHomography( Mat(points1), Mat(points2), CV_RANSAC, ransacReprojThreshold );
        cout << ">" << endl;
    }

    Mat drawImg;
    if( !H12.empty() ) // filter outliers
    {
        vector<char> matchesMask( filteredMatches.size(), 0 );
        vector<Point2f> points1; KeyPoint::convert(keypoints1, points1, queryIdxs);
        vector<Point2f> points2; KeyPoint::convert(keypoints2, points2, trainIdxs);
        Mat points1t; perspectiveTransform(Mat(points1), points1t, H12);

        double maxInlierDist = ransacReprojThreshold < 0 ? 3 : ransacReprojThreshold;
        for( size_t i1 = 0; i1 < points1.size(); i1++ )
        {
            if( norm(points2[i1] - points1t.at<Point2f>((int)i1,0)) <= maxInlierDist ) // inlier
                matchesMask[i1] = 1;
        }
        // draw inliers
        drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg, CV_RGB(0, 255, 0), CV_RGB(0, 0, 255), matchesMask
#if DRAW_RICH_KEYPOINTS_MODE
                     , DrawMatchesFlags::DRAW_RICH_KEYPOINTS
#endif
                   );

#if DRAW_OUTLIERS_MODE
        // draw outliers
        for( size_t i1 = 0; i1 < matchesMask.size(); i1++ )
            matchesMask[i1] = !matchesMask[i1];
        drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg, CV_RGB(0, 0, 255), CV_RGB(255, 0, 0), matchesMask,
                     DrawMatchesFlags::DRAW_OVER_OUTIMG | DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );
#endif
    }
    else
        drawMatches( img1, keypoints1, img2, keypoints2, filteredMatches, drawImg );

用于过滤的关键行在这里执行:

The key lines for the filtering are performed here:

if( norm(points2[i1] - points1t.at<Point2f>((int)i1,0)) <= maxInlierDist ) // inlier
                matchesMask[i1] = 1;

正在测量点之间的L2范数距离(如果未指定，则为3像素，或者用户定义的像素重投影误差数.)

Which is measuring the L2-norm distance between the points (either 3 pixels if nothing was specified, or user-defined number of pixels reprojection error).

希望有帮助！

这篇关于OpenCV Surf和离群值检测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！