73 lines
1.8 KiB
Matlab
73 lines
1.8 KiB
Matlab
#! /usr/bin/env octave
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cv;
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highgui;
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MAX_CLUSTERS=5;
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function ret = randint(v1, v2)
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ret = int32(rand() * (v2 - v1) + v1);
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end
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color_tab = { \
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CV_RGB(255,0,0), \
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CV_RGB(0,255,0), \
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CV_RGB(100,100,255), \
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CV_RGB(255,0,255), \
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CV_RGB(255,255,0)};
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img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
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rng = cvRNG(-1);
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cvNamedWindow( "clusters", 1 );
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while (true),
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cluster_count = randint(2, MAX_CLUSTERS);
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sample_count = randint(1, 1000);
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points = cvCreateMat( sample_count, 1, CV_32FC2 );
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clusters = cvCreateMat( sample_count, 1, CV_32SC1 );
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## generate random sample from multigaussian distribution
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for k=0:cluster_count-1,
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center = CvPoint();
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center.x = mod(cvRandInt(rng), img.width);
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center.y = mod(cvRandInt(rng), img.height);
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first = k*sample_count/cluster_count;
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last = sample_count;
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if (k != cluster_count)
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last = (k+1)*sample_count/cluster_count;
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endif
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point_chunk = cvGetRows(points, first, last);
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cvRandArr( rng, point_chunk, CV_RAND_NORMAL, \
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cvScalar(center.x,center.y,0,0), \
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cvScalar(img.width*0.1,img.height*0.1,0,0));
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endfor
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## shuffle samples
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cvRandShuffle( points, rng );
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cvKMeans2( points, cluster_count, clusters, \
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cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ));
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cvZero( img );
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for i=0:sample_count-1,
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cluster_idx = clusters(i);
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pt = points(i);
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cvCircle( img, pt, 2, color_tab{cluster_idx + 1}, CV_FILLED, CV_AA, 0 );
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cvCircle( img, pt, 2, color_tab{cluster_idx + 1}, CV_FILLED, CV_AA, 0 );
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endfor
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cvShowImage( "clusters", img );
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key = cvWaitKey(0);
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if( key == 27 || key == 'q' || key == 'Q' )
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break;
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endif
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endwhile
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cvDestroyWindow( "clusters" );
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