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Open Source Computer Vision Library
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58 lines
1.7 KiB
58 lines
1.7 KiB
#!/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.EM(cluster_n,cv2.EM_COV_MAT_GENERIC) |
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em.train(points) |
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means = em.getMat("means") |
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covs = em.getMatVector("covs") # Known bug: https://github.com/opencv/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) |