在OpenCV中用k均值进行颜色分类 [英] Color classification with k-means in OpenCV

问题描述

我想用 K-Means 算法聚集很多图像。我想设置群集，使每个群集代表图像的主色或色调。我在文章使用K-Means的彩色图像聚类中阅读了一些内容

I want to cluster a lot of images with the K-Means Algorithm. I want to set up the clusters, so that each cluster represent the dominant color or the hue of the image. I've read something about this in the paper Colour Image Clustering using K-Means

有人在OpenCV中有想法吗？

Does someone have an idea to do this in OpenCV?

也许我可以比较每个图像的直方图。但是如果我有很多图片需要很长时间。

Maybe I can compare the histograms of each image. But if I have a lot of pictures it takes a very long time

推荐答案

的RGB，然后使用 cv :: kmeans 来聚类，例如：

You can vectorize your image so each row is a set of RGB, and than use cv::kmeans to cluster, something like:

    std::vector<cv::Mat> imgRGB;
    cv::split(img,imgRGB);
    int k=5;
    int n = img.rows *img.cols;
    cv::Mat img3xN(n,3,CV_8U);
    for(int i=0;i!=3;++i)  
      imgRGB[i].reshape(1,n).copyTo(img3xN.col(i));
    img3xN.convertTo(img3xN,CV_32F);
    cv::Mat bestLables;
    cv::kmeans(img3xN,k,bestLables,cv::TermCriteria(),10,cv::KMEANS_RANDOM_CENTERS );
    bestLables= bestLables.reshape(0,img.rows);
    cv::convertScaleAbs(bestLables,bestLables,int(255/k));
    cv::imshow("result",bestLables);
    cv::waitKey();

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