opencv多维kmeans [英] opencv multidimensional kmeans

问题描述

我正在尝试对n维数据运行kmeans算法.

I'm trying to run the kmeans algorithm on a n-dimensional data.

我有N个点，每个点都有{ x, y, z, ... , n }个功能.

I Have N points and each point have { x, y, z, ... , n } features.

我的代码如下:

cv::Mat points(N, n, CV_32F);

// fill the data points

cv::Mat labels; cv::Mat centers;

cv::kmeans(points, k, labels, cv::TermCriteria(CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 1000, 0.001), 10, cv::KMEANS_PP_CENTERS, centers);

问题在于kmeans算法遇到了分割错误.

the problem is that the kmeans algorithm run into a segmentation fault.

感谢您的帮助

更新

Miki和Micka怎么说上述代码是正确的！

How Miki and Micka said the above code was correct!

我在填充数据点"中犯了一个错误，因此我破坏了内存

I had made a mistake in the "fill the data points" so that I corrupts the memory

推荐答案

代码看起来不错.您必须将数据选择为每列1维.

The code looks ok. You have to choose the data as 1 dimension per column.

您可以尝试运行此示例吗?

Can you try to run this example?

// k-means
int main(int argc, char* argv[])
{

    cv::Mat projectedPointsImage = cv::Mat(512, 512, CV_8UC3, cv::Scalar::all(255));

    int nReferenceCluster = 10;
    int nSamplesPerCluster = 100;

    int N = nReferenceCluster*nSamplesPerCluster; // number of samples
    int n = 10; // dimensionality of data

    // fill the data points
    // create n artificial clusters and randomly seed 100 points around them

    cv::Mat referenceCenters(nReferenceCluster, n, CV_32FC1);

    //std::cout << referenceCenters << std::endl;
    cv::randu(referenceCenters, cv::Scalar::all(0), cv::Scalar::all(512));
    //std::cout << "FILLED:" << "\n" << referenceCenters << std::endl;

    cv::Mat points = cv::Mat::zeros(N, n, CV_32FC1);
    cv::randu(points, cv::Scalar::all(-20), cv::Scalar::all(20)); // seed points around the center

    for (int j = 0; j < nReferenceCluster; ++j)
    {
        cv::Scalar clusterColor = cv::Scalar(rand() % 255, rand() % 255, rand() % 255);
        //cv::Mat & clusterCenter = referenceCenters.row(j);
        for (int i = 0; i < nSamplesPerCluster; ++i)
        {
            // creating a sample randomly around the artificial cluster:
            int index = j*nSamplesPerCluster + i;
            //samplesRow += clusterCenter;
            for (int k = 0; k < points.cols; ++k)
            {
                points.at<float>(index, k) += referenceCenters.at<float>(j, k);
            }

            // projecting the 10 dimensional clusters to 2 dimensions:
            cv::circle(projectedPointsImage, cv::Point(points.at<float>(index, 0), points.at<float>(index, 1)), 2, clusterColor, -1);

        }
    }


    cv::Mat labels; cv::Mat centers;
    int k = 10; // searched clusters in k-means

    cv::kmeans(points, k, labels, cv::TermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 1000, 0.001), 10, cv::KMEANS_PP_CENTERS, centers);

    for (int j = 0; j < centers.rows; ++j)
    {
        std::cout << centers.row(j) << std::endl;
        cv::circle(projectedPointsImage, cv::Point(centers.at<float>(j, 0), centers.at<float>(j, 1)), 30, cv::Scalar::all(0), 2);
    }

    cv::imshow("projected points", projectedPointsImage);
    cv::imwrite("C:/StackOverflow/Output/KMeans.png", projectedPointsImage);
    cv::waitKey(0);
    return 0;
}

我正在围绕人工集群中心创建10维数据.为了显示，我将它们投影到2D，得到以下结果:

I'm creating 10-dimensional data around artificial cluster centers there. For displaying I project them to 2D, getting this result:

这篇关于opencv多维kmeans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！