OpenCV: samples/cpp/kmeans.cpp

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OpenCV 4.0.1

Open Source Computer Vision

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samples/cpp/kmeans.cpp

An example on K-means clustering

#include "opencv2/highgui.hpp"

#include "opencv2/core.hpp"

#include "opencv2/imgproc.hpp"

#include <iostream>

using namespace cv;

using namespace std;

// static void help()

// {

// cout << "\nThis program demonstrates kmeans clustering.\n"

// "It generates an image with random points, then assigns a random number of cluster\n"

// "centers and uses kmeans to move those cluster centers to their representitive location\n"

// "Call\n"

// "./kmeans\n" << endl;

// }

int main( int /*argc*/, char** /*argv*/ )

{

const int MAX_CLUSTERS = 5;

Scalar colorTab[] =

{

Scalar(0, 0, 255),

Scalar(0,255,0),

Scalar(255,100,100),

Scalar(255,0,255),

Scalar(0,255,255)

};

Mat img(500, 500, CV_8UC3);

RNG rng(12345);

for(;;)

{

int k, clusterCount = rng.uniform(2, MAX_CLUSTERS+1);

int i, sampleCount = rng.uniform(1, 1001);

Mat points(sampleCount, 1, CV_32FC2), labels;

clusterCount = MIN(clusterCount, sampleCount);

std::vector<Point2f> centers;

/* generate random sample from multigaussian distribution */

for( k = 0; k < clusterCount; k++ )

{

Point center;

center.x = rng.uniform(0, img.cols);

center.y = rng.uniform(0, img.rows);

Mat pointChunk = points.rowRange(k*sampleCount/clusterCount,

k == clusterCount - 1 ? sampleCount :

(k+1)*sampleCount/clusterCount);

rng.fill(pointChunk, RNG::NORMAL, Scalar(center.x, center.y), Scalar(img.cols*0.05, img.rows*0.05));

}

randShuffle(points, 1, &rng);

double compactness = kmeans(points, clusterCount, labels,

TermCriteria( TermCriteria::EPS+TermCriteria::COUNT, 10, 1.0),

3, KMEANS_PP_CENTERS, centers);

img = Scalar::all(0);

for( i = 0; i < sampleCount; i++ )

{

int clusterIdx = labels.at<int>(i);

Point ipt = points.at<Point2f>(i);

circle( img, ipt, 2, colorTab[clusterIdx], FILLED, LINE_AA );

}

for (i = 0; i < (int)centers.size(); ++i)

{

Point2f c = centers[i];

circle( img, c, 40, colorTab[i], 1, LINE_AA );

}

cout << "Compactness: " << compactness << endl;

imshow("clusters", img);

char key = (char)waitKey();

if( key == 27 || key == 'q' || key == 'Q' ) // 'ESC'

break;

}

return 0;

}

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