/usr/share/doc/opencv-doc/examples/python2/gaussian_mix.py is in opencv-doc 2.4.9.1+dfsg-1.5ubuntu1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | #!/usr/bin/env python
import numpy as np
from numpy import random
import cv2
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
for i in xrange(cluster_n):
mean = (0.1 + 0.8*random.rand(2)) * img_size
a = (random.rand(2, 2)-0.5)*img_size*0.1
cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
n = 100 + random.randint(900)
pts = random.multivariate_normal(mean, cov, n)
points.append( pts )
ref_distrs.append( (mean, cov) )
points = np.float32( np.vstack(points) )
return points, ref_distrs
def draw_gaussain(img, mean, cov, color):
x, y = np.int32(mean)
w, u, vt = cv2.SVDecomp(cov)
ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
s1, s2 = np.sqrt(w)*3.0
cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
if __name__ == '__main__':
cluster_n = 5
img_size = 512
print 'press any key to update distributions, ESC - exit\n'
while True:
print 'sampling distributions...'
points, ref_distrs = make_gaussians(cluster_n, img_size)
print 'EM (opencv) ...'
em = cv2.EM(cluster_n, cv2.EM_COV_MAT_GENERIC)
em.train(points)
means = em.getMat('means')
covs = em.getMatVector('covs')
found_distrs = zip(means, covs)
print 'ready!\n'
img = np.zeros((img_size, img_size, 3), np.uint8)
for x, y in np.int32(points):
cv2.circle(img, (x, y), 1, (255, 255, 255), -1)
for m, cov in ref_distrs:
draw_gaussain(img, m, cov, (0, 255, 0))
for m, cov in found_distrs:
draw_gaussain(img, m, cov, (0, 0, 255))
cv2.imshow('gaussian mixture', img)
ch = 0xFF & cv2.waitKey(0)
if ch == 27:
break
cv2.destroyAllWindows()
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