Splits set of vectors by a given number of clusters.
Parameters: |
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The function cvKMeans2 implements a k-means algorithm that finds the centers of nclusters clusters and groups the input samples around the clusters. On output, contains a cluster index for samples stored in the i-th row of the samples matrix.
#include "cxcore.h"
#include "highgui.h"
void main( int argc, char** argv )
{
#define MAX_CLUSTERS 5
CvScalar color_tab[MAX_CLUSTERS];
IplImage* img = cvCreateImage( cvSize( 500, 500 ), 8, 3 );
CvRNG rng = cvRNG(0xffffffff);
color_tab[0] = CV_RGB(255,0,0);
color_tab[1] = CV_RGB(0,255,0);
color_tab[2] = CV_RGB(100,100,255);
color_tab[3] = CV_RGB(255,0,255);
color_tab[4] = CV_RGB(255,255,0);
cvNamedWindow( "clusters", 1 );
for(;;)
{
int k, cluster_count = cvRandInt(&rng)
int i, sample_count = cvRandInt(&rng)
CvMat* points = cvCreateMat( sample_count, 1, CV_32FC2 );
CvMat* clusters = cvCreateMat( sample_count, 1, CV_32SC1 );
/* generate random sample from multigaussian distribution */
for( k = 0; k < cluster_count; k++ )
{
CvPoint center;
CvMat point_chunk;
center.x = cvRandInt(&rng)
center.y = cvRandInt(&rng)
cvGetRows( points,
&point_chunk,
k*sample_count/cluster_count,
(k == (cluster_count - 1)) ?
sample_count :
(k+1)*sample_count/cluster_count );
cvRandArr( &rng, &point_chunk, CV_RAND_NORMAL,
cvScalar(center.x,center.y,0,0),
cvScalar(img->width/6, img->height/6,0,0) );
}
/* shuffle samples */
for( i = 0; i < sample_count/2; i++ )
{
CvPoint2D32f* pt1 =
(CvPoint2D32f*)points->data.fl + cvRandInt(&rng)
CvPoint2D32f* pt2 =
(CvPoint2D32f*)points->data.fl + cvRandInt(&rng)
CvPoint2D32f temp;
CV_SWAP( *pt1, *pt2, temp );
}
cvKMeans2( points, cluster_count, clusters,
cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ));
cvZero( img );
for( i = 0; i < sample_count; i++ )
{
CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];
int cluster_idx = clusters->data.i[i];
cvCircle( img,
cvPointFrom32f(pt),
2,
color_tab[cluster_idx],
CV_FILLED );
}
cvReleaseMat( &points );
cvReleaseMat( &clusters );
cvShowImage( "clusters", img );
int key = cvWaitKey(0);
if( key == 27 )
break;
}
}
Splits a sequence into equivalency classes.
Parameters: |
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typedef int (CV_CDECL* CvCmpFunc)(const void* a, const void* b, void* userdata);
The function cvSeqPartition implements a quadratic algorithm for splitting a set into one or more equivalancy classes. The function returns the number of equivalency classes.
#include "cxcore.h"
#include "highgui.h"
#include <stdio.h>
CvSeq* point_seq = 0;
IplImage* canvas = 0;
CvScalar* colors = 0;
int pos = 10;
int is_equal( const void* _a, const void* _b, void* userdata )
{
CvPoint a = *(const CvPoint*)_a;
CvPoint b = *(const CvPoint*)_b;
double threshold = *(double*)userdata;
return (double)((a.x - b.x)*(a.x - b.x) + (a.y - b.y)*(a.y - b.y)) <=
threshold;
}
void on_track( int pos )
{
CvSeq* labels = 0;
double threshold = pos*pos;
int i, class_count = cvSeqPartition( point_seq,
0,
&labels,
is_equal,
&threshold );
printf("
cvZero( canvas );
for( i = 0; i < labels->total; i++ )
{
CvPoint pt = *(CvPoint*)cvGetSeqElem( point_seq, i );
CvScalar color = colors[*(int*)cvGetSeqElem( labels, i )];
cvCircle( canvas, pt, 1, color, -1 );
}
cvShowImage( "points", canvas );
}
int main( int argc, char** argv )
{
CvMemStorage* storage = cvCreateMemStorage(0);
point_seq = cvCreateSeq( CV_32SC2,
sizeof(CvSeq),
sizeof(CvPoint),
storage );
CvRNG rng = cvRNG(0xffffffff);
int width = 500, height = 500;
int i, count = 1000;
canvas = cvCreateImage( cvSize(width,height), 8, 3 );
colors = (CvScalar*)cvAlloc( count*sizeof(colors[0]) );
for( i = 0; i < count; i++ )
{
CvPoint pt;
int icolor;
pt.x = cvRandInt( &rng )
pt.y = cvRandInt( &rng )
cvSeqPush( point_seq, &pt );
icolor = cvRandInt( &rng ) | 0x00404040;
colors[i] = CV_RGB(icolor & 255,
(icolor >> 8)&255,
(icolor >> 16)&255);
}
cvNamedWindow( "points", 1 );
cvCreateTrackbar( "threshold", "points", &pos, 50, on_track );
on_track(pos);
cvWaitKey(0);
return 0;
}