Expectation Maximization Algorithm---opencv2.4.11

摘自官方例程：期望值最大化算法----代码源自opencv2.4.11版本

（opencv\sources\samples\cpp\em.cpp）

#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 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);// initialize model parametersparams.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 dataem_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 = 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 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.predict( sample ));Scalar c = colors[response];circle( img, Point(j, i), 1, c*0.75, CV_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)], CV_FILLED );}imshow( "EM-clustering result", img );waitKey(0);return 0;}

运行结果：