69 lines
1.9 KiB
C++
69 lines
1.9 KiB
C++
#include "opencv2/highgui.hpp"
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#include "opencv2/imgproc.hpp"
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#include "opencv2/ml.hpp"
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using namespace cv;
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using namespace cv::ml;
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int main( int /*argc*/, char** /*argv*/ )
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{
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const int N = 4;
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const int N1 = (int)sqrt((double)N);
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const Scalar colors[] =
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{
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Scalar(0,0,255), Scalar(0,255,0),
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Scalar(0,255,255),Scalar(255,255,0)
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};
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int i, j;
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int nsamples = 100;
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Mat samples( nsamples, 2, CV_32FC1 );
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Mat labels;
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Mat img = Mat::zeros( Size( 500, 500 ), CV_8UC3 );
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Mat sample( 1, 2, CV_32FC1 );
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samples = samples.reshape(2, 0);
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for( i = 0; i < N; i++ )
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{
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// form the training samples
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Mat samples_part = samples.rowRange(i*nsamples/N, (i+1)*nsamples/N );
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Scalar mean(((i%N1)+1)*img.rows/(N1+1),
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((i/N1)+1)*img.rows/(N1+1));
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Scalar sigma(30,30);
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randn( samples_part, mean, sigma );
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}
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samples = samples.reshape(1, 0);
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// cluster the data
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Ptr<EM> em_model = EM::train( samples, noArray(), labels, noArray(),
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EM::Params(N, EM::COV_MAT_SPHERICAL,
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TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1)));
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// classify every image pixel
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for( i = 0; i < img.rows; i++ )
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{
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for( j = 0; j < img.cols; j++ )
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{
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sample.at<float>(0) = (float)j;
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sample.at<float>(1) = (float)i;
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int response = cvRound(em_model->predict2( sample, noArray() )[1]);
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Scalar c = colors[response];
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circle( img, Point(j, i), 1, c*0.75, FILLED );
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}
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}
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//draw the clustered samples
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for( i = 0; i < nsamples; i++ )
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{
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Point pt(cvRound(samples.at<float>(i, 0)), cvRound(samples.at<float>(i, 1)));
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circle( img, pt, 1, colors[labels.at<int>(i)], FILLED );
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}
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imshow( "EM-clustering result", img );
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waitKey(0);
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return 0;
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}
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