void Consensus::findConsensus(const vector<Point2f> & points, const vector<int> & classes,
        const float scale, const float rotation,
        Point2f & center, vector<Point2f> & points_inlier, vector<int> & classes_inlier)
{

    //If no points are available, reteurn nan
    if (points.size() == 0)
    {
        center.x = numeric_limits<float>::quiet_NaN();
        center.y = numeric_limits<float>::quiet_NaN();

        return;
    }

    //Compute votes 计算投票：基本方法就是计算点相对于正规化且计算其旋转加缩放后的点的相对位置 保持相对一致
    vector<Point2f> votes(points.size());
    for (size_t i = 0; i < points.size(); i++)
    {
        votes[i] = points[i] - scale * rotate(points_normalized[classes[i]], rotation);
    }

    t_index N = points.size();

    float * D = new float[N*(N-1)/2]; //This is a lot of memory, so we put it on the heap
    cluster_result Z(N-1);

    //Compute pairwise distances between votes
    //计算votes点之间的相对距离
    int index = 0;
    for (size_t i = 0; i < points.size(); i++)
    {
        for (size_t j = i+1; j < points.size(); j++)
        {
            //TODO: This index calculation is correct, but is it a good thing?
            //int index = i * (points.size() - 1) - (i*i + i) / 2 + j - 1;
            // 计算相对距离
            D[index] = norm(votes[i] - votes[j]);
            index++;
        }
    }

    MST_linkage_core(N,D,Z);

    union_find nodes(N);

    //Sort linkage by distance ascending
    std::stable_sort(Z[0], Z[N-1]);

    //S are cluster sizes
    int * S = new int[2*N-1];
    //TODO: Why does this loop go to 2*N-1? Shouldn‘t it be simply N? Everything > N gets overwritten later
    for(int i = 0; i < 2*N-1; i++)
    {
        S[i] = 1;
    }

    t_index parent = 0; //After the loop ends, parent contains the index of the last cluster
    for (node const * NN=Z[0]; NN!=Z[N-1]; ++NN)
    {
        // Get two data points whose clusters are merged in step i.
        // Find the cluster identifiers for these points.
        t_index node1 = nodes.Find(NN->node1);
        t_index node2 = nodes.Find(NN->node2);

        // Merge the nodes in the union-find data structure by making them
        // children of a new node
        // if the distance is appropriate
        if (NN->dist < thr_cutoff)
        {
            parent = nodes.Union(node1, node2);
            S[parent] = S[node1] + S[node2];
        }
    }

    //Get cluster labels
    int * T = new int[N];
    for (t_index i = 0; i < N; i++)
    {
        T[i] = nodes.Find(i);
    }

    //Find largest cluster
    int S_max = distance(S, max_element(S, S + 2*N-1));

    //Find inliers, compute center of votes
    points_inlier.reserve(S[S_max]);
    classes_inlier.reserve(S[S_max]);
    center.x = center.y = 0;

    for (size_t i = 0; i < points.size(); i++)
    {
        //If point is in consensus cluster
        if (T[i] == S_max)
        {
            points_inlier.push_back(points[i]);
            classes_inlier.push_back(classes[i]);
            center.x += votes[i].x;
            center.y += votes[i].y;
        }

    }

    center.x /= points_inlier.size();
    center.y /= points_inlier.size();

    delete[] D;
	delete[] S;
	delete[] T;

}

void Matcher::matchLocal(const vector<KeyPoint> & keypoints, const Mat descriptors,
        const Point2f center, const float scale, const float rotation,
        vector<Point2f> & points_matched, vector<int> & classes_matched)
{

    if (keypoints.size() == 0) {
        return;
    }

    //Transform initial points
    vector<Point2f> pts_fg_trans;
    pts_fg_trans.reserve(pts_fg_norm.size());
    for (size_t i = 0; i < pts_fg_norm.size(); i++)
    {
        // 同样是计算相对位置
        pts_fg_trans.push_back(scale * rotate(pts_fg_norm[i], -rotation));
    }

    //Perform local matching
    for (size_t i = 0; i < keypoints.size(); i++)
    {
        //Normalize keypoint with respect to center
        Point2f location_rel = keypoints[i].pt - center;

        //Find potential indices for matching
        vector<int> indices_potential;
        for (size_t j = 0; j < pts_fg_trans.size(); j++)
        {
            // 计算位置偏差
            float l2norm = norm(pts_fg_trans[j] - location_rel);

            // 设置一个阈值
            if (l2norm < thr_cutoff) {
                indices_potential.push_back(num_bg_points + j);
            }

        }

        //If there are no potential matches, continue
        if (indices_potential.size() == 0) continue;

        //Build descriptor matrix and classes from potential indices
        Mat database_potential = Mat(indices_potential.size(), database.cols, database.type());
        for (size_t j = 0; j < indices_potential.size(); j++) {
            database.row(indices_potential[j]).copyTo(database_potential.row(j));
        }

        //Find distances between descriptors
        vector<vector<DMatch> > matches;
        // 对选出的特征点进行特征匹配
        bfmatcher->knnMatch(descriptors.row(i), database_potential, matches, 2);

        vector<DMatch> m = matches[0];

        float distance1 = m[0].distance / desc_length;
        float distance2 = m.size() > 1 ? m[1].distance / desc_length : 1;

        if (distance1 > thr_dist) continue;
        if (distance1/distance2 > thr_ratio) continue;

        int matched_class = classes[indices_potential[m[0].trainIdx]];

        points_matched.push_back(keypoints[i].pt);
        classes_matched.push_back(matched_class);
    }

}

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