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