Open Source Computer Vision Library https://opencv.org/
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#!/usr/bin/python
from opencv.cv import *
from opencv.highgui import *
from random import randint
MAX_CLUSTERS = 5
if __name__ == "__main__":
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 in range(cluster_count):
center = CvPoint()
center.x = cvRandInt(rng)%img.width
center.y = cvRandInt(rng)%img.height
first = k*sample_count/cluster_count
last = sample_count
if k != cluster_count:
last = (k+1)*sample_count/cluster_count
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))
# shuffle samples
cvRandShuffle( points, rng )
cvKMeans2( points, cluster_count, clusters,
cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER, 10, 1.0 ))
cvZero( img )
for i in range(sample_count):
cluster_idx = clusters[i]
# a multi channel matrix access returns a scalar of
#dimension 4,0, which is not considerate a cvPoint
#we have to create a tuple with the first two elements
pt = (cvRound(points[i][0]), cvRound(points[i][1]))
cvCircle( img, pt, 2, color_tab[cluster_idx], CV_FILLED, CV_AA, 0 )
cvShowImage( "clusters", img )
key = cvWaitKey(0)
if( key == 27 or key == 'q' or key == 'Q' ): # 'ESC'
break
cvDestroyWindow( "clusters" )