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How to detect multiple objects with OpenCV in C++?

由 久未见 提交于 2019-12-05 00:55:06

I don't know a solution to your problem, but the following might help answer the questions you've asked.

In the comments it says that you might need an implementation of meanshift, which opencv already has. Here an example, here the documentation with a tutorial.

  1. The clusterCount for kmeansis the number of clusters you want to create link. I don't know how to estimate the number you want to create, but I guess you could know.

  2. You initialize descriptors_scene_clusters only with one element:

Mat descriptors_scene_clusters[3] = { Mat(descriptors_scene.rows, descriptors_scene.cols, CV_8U, Scalar(0)) };

And when you iterate over it:

for (int i=0; i<labels.rows; i++) {
    int clusterIndex = labels.at<int>(i);
    Point2f pt = keypoints_scene_points.at<Point2f>(i);
    descriptors_scene_clusters[clusterIndex].at<uchar>(pt) = descriptors_scene.at<uchar>(pt);  // ?????? error
}

clusterIndex is 2 and you access an uninitialized element in the array, which results in the EXEC_BAD_ACCESS error.

I hope this helps for further investigation!

You can use a simple clusterer to either directly compute the clusters, find the number of clusters and / or the cluster centers initialization for kmeans. Here below a possible implementation of an agglomerative clusterer, which groups together points closer to a specified distance - see parameter dist in constructor. In your case dist can be the biggest distance between keypoints in the small image.

Header file: 

class PointsClusterer {
public:
     PointsClusterer (int dist, std::vector <cv::Point2f> points);
     bool cluster();
     std::map <int, std::vector <cv::Point2f>> getClusters ();

private:

    double distance (int i, int j);
    void merge (int i, int j);

private:

    std::vector <cv::Point2f> m_Points;
    std::map <int, std::vector <int>> m_Clusters;
    std::map <int, cv::Point2f> m_Sums;
    int m_dist = 0;
};

Cpp file:

PointsClusterer::PointsClusterer (int dist, std::vector <cv::Point2f> points) :
m_dist(dist), m_Points (points)
{}

bool PointsClusterer::cluster()
{
//initialization
    for (int i = 0; i < m_Points.size(); ++i)
    {
        clusters[i] = std::vector<int>(1, i);
        sum_clusters[i] = m_Points[i];
    }

    bool still_merge = true;
    //Merge clusters
    while (still_merge)
    {
        still_merge = false;
        bool break_i = false;

        for (int i=0; i < m_Clusters.size () && !break_i ;++i)
        for (int j=i+1; j < m_Clusters.size ();++j)
        {
            if (distance(i, j) < m_dist)
            {
                merge(i, j);
                break_i = true;
                still_merge = true;
                break;
            }
        }
    }
//final conversion to std::map <int, std::vector <cv::Point2f>> is missing

}


void PointsClusterer::merge(int i, int j)
{
    auto it = m_Clusters.begin();
    auto iti = it+i;
    auto itj = it+j;

    for (val : itj->second)
    {
         iti->second.push_back(val);
         m_Sums[iti->first]+=m_Points[val];
    }
    m_Clusters.erase(itj);
}

double PointsClusterer::distance(int i, int j)
{
    auto it = m_Clusters.begin();
    auto iti = it + i;
    auto itj = it + j;
    auto vali = m_Sums[iti->first] / iti->second.size();
    auto valj = m_Sums[itj->first] / itj->second.size();
    return cv::norm(vali - valj);
}

The implementation of the cluster method was oversimplified such that it is obvious how it works. Its performance can be improved, but I believe that is beyond the scope of your question.

标签
c++
OpenCV
k-means
opencv3.0
mean-shift
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