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#!/usr/bin/python
import urllib2
import cv2.cv as cv
from random import randint
MAX_CLUSTERS = 5
if __name__ == "__main__":
color_tab = [
cv.CV_RGB(255, 0,0),
cv.CV_RGB(0, 255, 0),
cv.CV_RGB(100, 100, 255),
cv.CV_RGB(255, 0,255),
cv.CV_RGB(255, 255, 0)]
img = cv.CreateImage((500, 500), 8, 3)
rng = cv.RNG(-1)
cv.NamedWindow("clusters", 1)
while True:
cluster_count = randint(2, MAX_CLUSTERS)
sample_count = randint(1, 1000)
points = cv.CreateMat(sample_count, 1, cv.CV_32FC2)
clusters = cv.CreateMat(sample_count, 1, cv.CV_32SC1)
# generate random sample from multigaussian distribution
for k in range(cluster_count):
center = (cv.RandInt(rng)%img.width, cv.RandInt(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 = cv.GetRows(points, first, last)
cv.RandArr(rng, point_chunk, cv.CV_RAND_NORMAL,
cv.Scalar(center[0], center[1], 0, 0),
cv.Scalar(img.width*0.1, img.height*0.1, 0, 0))
# shuffle samples
cv.RandShuffle(points, rng)
cv.KMeans2(points, cluster_count, clusters,
(cv.CV_TERMCRIT_EPS + cv.CV_TERMCRIT_ITER, 10, 1.0))
cv.Zero(img)
for i in range(sample_count):
cluster_idx = int(clusters[i, 0])
pt = (cv.Round(points[i, 0][0]), cv.Round(points[i, 0][1]))
cv.Circle(img, pt, 2, color_tab[cluster_idx], cv.CV_FILLED, cv.CV_AA, 0)
cv.ShowImage("clusters", img)
key = cv.WaitKey(0) % 0x100
if key in [27, ord('q'), ord('Q')]:
break
cv.DestroyWindow("clusters")
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