f4334277
 Hu Chunming
提交3rdparty
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#include "opencv2/highgui.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/ml.hpp"
using namespace cv;
using namespace cv::ml;
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 );
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);
// cluster the data
Ptr<EM> em_model = EM::create();
em_model->setClustersNumber(N);
em_model->setCovarianceMatrixType(EM::COV_MAT_SPHERICAL);
em_model->setTermCriteria(TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 300, 0.1));
em_model->trainEM( samples, noArray(), labels, noArray() );
// 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->predict2( sample, noArray() )[1]);
Scalar c = colors[response];
circle( img, Point(j, i), 1, c*0.75, 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)], FILLED );
}
imshow( "EM-clustering result", img );
waitKey(0);
return 0;
}
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