Question

我知道这里已经问过几个问题,但我找不到任何帮助。

因此,我想对2个图像进行比较,以便了解这些图像的相似之处,利用众所周知的发现“obj.cpp demo”来提取表面描述器,然后对配对器,我使用斜体。

但是,正如你所知,这种方法确实抛弃了离散者,我很想知道真实的正比数字,因此,我可以说明这两个图像的相似之处。

我已经看到这个问题:。在SURF或SIFT算法中检测出的散射器有开放式CV,其中的颜色建议使用发现的FundamentalMat,但一旦获得基本矩阵,我如何能够从该矩阵中获取出动/真实数字? 谢谢。

Answer 1

这里是descriptor_extractor_matcher.cpp 。开放式中心提供的样本:

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 );

过滤的关键线如下:

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

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).

希望帮助!

Answer 2

you can use the size of the vector named "ptpairs" in order to decide how similiar the pictures are. this vector contains the matching keypoints, so his size/2 is the number of matches. i think you can use the size of ptpairs divided by the total number of keypoints in order to set an appropriate threshold. this will probably give you an estimation to the similiarty between them.

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