initial commit

samples/cpp/em.cpp

@@ -0,0 +1,70 @@
+#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;
+}