Grabbing the amount of pixels in K-Means Clusters in C++

C++
1

I have the following code which takes an image and displays a color palette using K-means clustering.

#include "opencv2/core/core.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include "iostream"

using namespace cv;
using namespace std;



Mat showCenters(const Mat& centers, const Mat& data, int siz = 64) {

    Mat cent = centers.reshape(3, centers.rows);
    //april 4 int dat = data.total();

    // make  a horizontal bar of K color patches:
    Mat draw(siz, siz * cent.rows, cent.type(), Scalar::all(0));

    cout << endl << "BGR Dominance in order\n";
    for (int i = 0; i < cent.rows; i++) {
        // set the resp. ROI to that value (just fill it):
        draw(Rect(i * siz, 0, siz, siz)) = cent.at<Vec3f>(i, 0);
        cout << i+1 << ": " <<cent.at<Vec3f>(i, 0) << endl << endl;
        //cout << dat << endl;
    }
    draw.convertTo(draw, CV_8U);

    // optional visualization:

    imshow("CENTERS", draw);
    waitKey();

    //imwrite("centers.png", draw);

    return draw;
}

int main()
{

    Mat src;
    std::string image_path = samples::findFile(" "); //put file path within parameter

    // do kmeans


    //iterate through each and every compactness to determine optimal k:
    for (int k = 1; k < 15; k++)
    {
        src = imread(image_path, IMREAD_COLOR);

        Mat labels, centers;
        Mat data;
        src.convertTo(data, CV_32F);
        data = data.reshape(1, data.total());

        double compactness = kmeans(data, k, labels, TermCriteria(TermCriteria::MAX_ITER, 10, 1.0), 3, KMEANS_PP_CENTERS, centers);

        // reshape both to a single row of Vec3f pixels:
        
        centers = centers.reshape(3, centers.rows);
        data = data.reshape(3, data.rows);

        // replace pixel values with their center value:
        Vec3f* p = data.ptr<Vec3f>();
        for (size_t i = 0; i < data.rows; i++) {
            int center_id = labels.at<int>(i);
            p[i] = centers.at<Vec3f>(center_id);
        }

        // back to 2d, and uchar:
        src = data.reshape(3, src.rows);
        src.convertTo(src, CV_8U);

        std::cout << "compactness at K = " << k << ": " << compactness << endl;

        //visualize data:
        imshow("Image", src);
        showCenters(centers, data);

        destroyAllWindows();
    }





}

Now, I would like to grab the amount of data points (or pixels) that are initialized to a cluster. How would I be able to do that? Is there a function will allows me to do that?

Hopefully, I would be able to grab the percentage of colours using that data.

2

you have that array.

just count the occurrences of each label.

3

How? Not sure how to count each “0”, “1”, and so forth.

The OpenCV article on Kmeans has this:

A = z[labels==0]

B = z[labels==1]

Since that’s Python, I’m not sure how to implement that for C++.