57 lines
1.9 KiB
Python
57 lines
1.9 KiB
Python
import numpy as np
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from numpy import random
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import cv2
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def make_gaussians(cluster_n, img_size):
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points = []
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ref_distrs = []
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for i in xrange(cluster_n):
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mean = (0.1 + 0.8*random.rand(2)) * img_size
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a = (random.rand(2, 2)-0.5)*img_size*0.1
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cov = np.dot(a.T, a) + img_size*0.05*np.eye(2)
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n = 100 + random.randint(900)
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pts = random.multivariate_normal(mean, cov, n)
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points.append( pts )
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ref_distrs.append( (mean, cov) )
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points = np.float32( np.vstack(points) )
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return points, ref_distrs
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def draw_gaussain(img, mean, cov, color):
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x, y = np.int32(mean)
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w, u, vt = cv2.SVDecomp(cov)
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ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
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s1, s2 = np.sqrt(w)*3.0
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cv2.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv2.CV_AA)
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if __name__ == '__main__':
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cluster_n = 5
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img_size = 512
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print 'press any key to update distributions, ESC - exit\n'
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while True:
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print 'sampling distributions...'
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points, ref_distrs = make_gaussians(cluster_n, img_size)
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print 'EM (opencv) ...'
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em = cv2.EM(points, params = dict( nclusters = cluster_n, cov_mat_type = cv2.EM_COV_MAT_GENERIC) )
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means = em.getMeans()
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covs = np.zeros((cluster_n, 2, 2), np.float32)
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covs = em.getCovs(covs) # FIXME
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found_distrs = zip(means, covs)
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print 'ready!\n'
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img = np.zeros((img_size, img_size, 3), np.uint8)
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for x, y in np.int32(points):
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cv2.circle(img, (x, y), 1, (255, 255, 255), -1)
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for m, cov in ref_distrs:
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draw_gaussain(img, m, cov, (0, 255, 0))
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for m, cov in found_distrs:
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draw_gaussain(img, m, cov, (0, 0, 255))
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cv2.imshow('gaussian mixture', img)
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ch = 0xFF & cv2.waitKey(0)
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if ch == 27:
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break
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