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#!/usr/bin/env python
# Python 2/3 compatibility
from __future__ import print_function
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
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
from tests_common import NewOpenCVTests
class gaussian_mix_test(NewOpenCVTests):
def test_gaussian_mix(self):
np.random.seed(10)
cluster_n = 5
img_size = 512
points, ref_distrs = make_gaussians(cluster_n, img_size)
em = cv2.EM(cluster_n,cv2.EM_COV_MAT_GENERIC)
em.train(points)
means = em.getMat("means")
covs = em.getMatVector("covs") # Known bug: https://github.com/opencv/opencv/pull/4232
found_distrs = zip(means, covs)
matches_count = 0
meanEps = 0.05
covEps = 0.1
for i in range(cluster_n):
for j in range(cluster_n):
if (cv2.norm(means[i] - ref_distrs[j][0], cv2.NORM_L2) / cv2.norm(ref_distrs[j][0], cv2.NORM_L2) < meanEps and
cv2.norm(covs[i] - ref_distrs[j][1], cv2.NORM_L2) / cv2.norm(ref_distrs[j][1], cv2.NORM_L2) < covEps):
matches_count += 1
self.assertEqual(matches_count, cluster_n)