opencv/samples/cpp/em.cpp

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#include "opencv2/legacy/legacy.hpp"
#include "opencv2/highgui/highgui.hpp"
using namespace cv;
int main( int /*argc*/, char** /*argv*/ )
{
const int N = 4;
const int N1 = (int)sqrt((double)N);
const Scalar colors[] =
{
Scalar(0,0,255), Scalar(0,255,0),
Scalar(0,255,255),Scalar(255,255,0)
};
int i, j;
int nsamples = 100;
Mat samples( nsamples, 2, CV_32FC1 );
Mat labels;
Mat img = Mat::zeros( Size( 500, 500 ), CV_8UC3 );
Mat sample( 1, 2, CV_32FC1 );
CvEM em_model;
CvEMParams params;
samples = samples.reshape(2, 0);
for( i = 0; i < N; i++ )
{
// form the training samples
Mat samples_part = samples.rowRange(i*nsamples/N, (i+1)*nsamples/N );
Scalar mean(((i%N1)+1)*img.rows/(N1+1),
((i/N1)+1)*img.rows/(N1+1));
Scalar sigma(30,30);
randn( samples_part, mean, sigma );
}
samples = samples.reshape(1, 0);
// initialize model parameters
params.covs = NULL;
params.means = NULL;
params.weights = NULL;
params.probs = NULL;
params.nclusters = N;
params.cov_mat_type = CvEM::COV_MAT_SPHERICAL;
params.start_step = CvEM::START_AUTO_STEP;
params.term_crit.max_iter = 300;
params.term_crit.epsilon = 0.1;
params.term_crit.type = CV_TERMCRIT_ITER|CV_TERMCRIT_EPS;
// cluster the data
em_model.train( samples, Mat(), params, &labels );
#if 0
// the piece of code shows how to repeatedly optimize the model
// with less-constrained parameters
//(COV_MAT_DIAGONAL instead of COV_MAT_SPHERICAL)
// when the output of the first stage is used as input for the second one.
CvEM em_model2;
params.cov_mat_type = CvEM::COV_MAT_DIAGONAL;
params.start_step = CvEM::START_E_STEP;
params.means = em_model.get_means();
params.covs = (const CvMat**)em_model.get_covs();
params.weights = em_model.get_weights();
em_model2.train( samples, Mat(), params, &labels );
// to use em_model2, replace em_model.predict()
// with em_model2.predict() below
#endif
// classify every image pixel
for( i = 0; i < img.rows; i++ )
{
for( j = 0; j < img.cols; j++ )
{
sample.at<float>(0) = (float)j;
sample.at<float>(1) = (float)i;
int response = cvRound(em_model.predict( sample ));
Scalar c = colors[response];
circle( img, Point(j, i), 1, c*0.75, CV_FILLED );
}
}
//draw the clustered samples
for( i = 0; i < nsamples; i++ )
{
Point pt(cvRound(samples.at<float>(i, 0)), cvRound(samples.at<float>(i, 1)));
circle( img, pt, 1, colors[labels.at<int>(i)], CV_FILLED );
}
imshow( "EM-clustering result", img );
waitKey(0);
return 0;
}