61 lines
1.9 KiB
Python
61 lines
1.9 KiB
Python
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#!/usr/bin/python
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import urllib2
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import cv
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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.CV_RGB(255, 0,0),
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cv.CV_RGB(0, 255, 0),
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cv.CV_RGB(100, 100, 255),
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cv.CV_RGB(255, 0,255),
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cv.CV_RGB(255, 255, 0)]
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img = cv.CreateImage((500, 500), 8, 3)
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rng = cv.RNG(-1)
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cv.NamedWindow("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 = cv.CreateMat(sample_count, 1, cv.CV_32FC2)
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clusters = cv.CreateMat(sample_count, 1, cv.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 = (cv.RandInt(rng)%img.width, cv.RandInt(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 = cv.GetRows(points, first, last)
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cv.RandArr(rng, point_chunk, cv.CV_RAND_NORMAL,
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cv.Scalar(center[0], center[1], 0, 0),
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cv.Scalar(img.width*0.1, img.height*0.1, 0, 0))
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# shuffle samples
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cv.RandShuffle(points, rng)
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cv.KMeans2(points, cluster_count, clusters,
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(cv.CV_TERMCRIT_EPS + cv.CV_TERMCRIT_ITER, 10, 1.0))
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cv.Zero(img)
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for i in range(sample_count):
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cluster_idx = int(clusters[i, 0])
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pt = (cv.Round(points[i, 0][0]), cv.Round(points[i, 0][1]))
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cv.Circle(img, pt, 2, color_tab[cluster_idx], cv.CV_FILLED, cv.CV_AA, 0)
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cv.ShowImage("clusters", img)
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key = cv.WaitKey(0) % 0x100
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if key in [27, ord('q'), ord('Q')]:
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break
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cv.DestroyWindow("clusters")
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