最大期望值算法（EM算法）---opencv3.1

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摘自opencv3.1源码例程

#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 samplesMat 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 dataPtr<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 pixelfor( 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 samplesfor( 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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