opencv/samples/octave/kmeans.m

73 lines
1.8 KiB
Matlab

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