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70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
#!/usr/bin/env python
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'''
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K-means clusterization test
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'''
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# Python 2/3 compatibility
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from __future__ import print_function
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import numpy as np
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import cv2
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from numpy import random
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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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from tests_common import NewOpenCVTests
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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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sizes = []
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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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sizes.append(n)
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points = np.float32( np.vstack(points) )
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return points, ref_distrs, sizes
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def getMainLabelConfidence(labels, nLabels):
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n = len(labels)
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labelsDict = dict.fromkeys(range(nLabels), 0)
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labelsConfDict = dict.fromkeys(range(nLabels))
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for i in range(n):
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labelsDict[labels[i][0]] += 1
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for i in range(nLabels):
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labelsConfDict[i] = float(labelsDict[i]) / n
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return max(labelsConfDict.values())
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class kmeans_test(NewOpenCVTests):
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def test_kmeans(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, _, clusterSizes = make_gaussians(cluster_n, img_size)
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term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
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ret, labels, centers = cv2.kmeans(points, cluster_n, None, term_crit, 10, 0)
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self.assertEqual(len(centers), cluster_n)
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offset = 0
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for i in range(cluster_n):
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confidence = getMainLabelConfidence(labels[offset : (offset + clusterSizes[i])], cluster_n)
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offset += clusterSizes[i]
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self.assertGreater(confidence, 0.9) |