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95 lines
2.8 KiB
C++
95 lines
2.8 KiB
C++
#include "opencv2/ml/ml.hpp"
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#include "opencv2/highgui/highgui.hpp"
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using namespace cv;
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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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CvEM em_model;
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CvEMParams params;
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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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// initialize model parameters
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params.covs = NULL;
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params.means = NULL;
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params.weights = NULL;
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params.probs = NULL;
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params.nclusters = N;
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params.cov_mat_type = CvEM::COV_MAT_SPHERICAL;
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params.start_step = CvEM::START_AUTO_STEP;
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params.term_crit.max_iter = 300;
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params.term_crit.epsilon = 0.1;
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params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS;
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// cluster the data
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em_model.train( samples, Mat(), params, &labels );
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#if 0
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// the piece of code shows how to repeatedly optimize the model
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// with less-constrained parameters
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//(COV_MAT_DIAGONAL instead of COV_MAT_SPHERICAL)
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// when the output of the first stage is used as input for the second one.
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CvEM em_model2;
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params.cov_mat_type = CvEM::COV_MAT_DIAGONAL;
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params.start_step = CvEM::START_E_STEP;
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params.means = em_model.get_means();
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params.covs = (const CvMat**)em_model.get_covs();
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params.weights = em_model.get_weights();
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em_model2.train( samples, Mat(), params, &labels );
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// to use em_model2, replace em_model.predict()
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// with em_model2.predict() below
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#endif
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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.predict( sample ));
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Scalar c = colors[response];
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circle( img, Point(j, i), 1, c*0.75, CV_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)], CV_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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