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Open Source Computer Vision Library
https://opencv.org/
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60 lines
1.7 KiB
60 lines
1.7 KiB
9 years ago
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#!/usr/bin/env python
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# Python 2/3 compatibility
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from __future__ import print_function
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import sys
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PY3 = sys.version_info[0] == 3
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if PY3:
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xrange = range
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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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from tests_common import NewOpenCVTests
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class gaussian_mix_test(NewOpenCVTests):
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def test_gaussian_mix(self):
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np.random.seed(10)
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cluster_n = 5
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img_size = 512
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points, ref_distrs = make_gaussians(cluster_n, img_size)
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em = cv2.ml.EM_create()
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em.setClustersNumber(cluster_n)
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em.setCovarianceMatrixType(cv2.ml.EM_COV_MAT_GENERIC)
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em.trainEM(points)
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means = em.getMeans()
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covs = em.getCovs() # Known bug: https://github.com/Itseez/opencv/pull/4232
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found_distrs = zip(means, covs)
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matches_count = 0
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meanEps = 0.05
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covEps = 0.1
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for i in range(cluster_n):
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for j in range(cluster_n):
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if (cv2.norm(means[i] - ref_distrs[j][0], cv2.NORM_L2) / cv2.norm(ref_distrs[j][0], cv2.NORM_L2) < meanEps and
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cv2.norm(covs[i] - ref_distrs[j][1], cv2.NORM_L2) / cv2.norm(ref_distrs[j][1], cv2.NORM_L2) < covEps):
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matches_count += 1
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self.assertEqual(matches_count, cluster_n)
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