a lot of small corrections to bring down the number of undocumented functions, reported by the script; added em.cpp sample

2011-06-09 01:16:45 +00:00
parent 3b9e752be7
commit 20aca7440f
30 changed files with 474 additions and 746 deletions
--- a/samples/cpp/em.cpp
+++ b/samples/cpp/em.cpp
@@ -0,0 +1,94 @@
+#include "opencv2/ml/ml.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;
+}