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#!/usr/bin/env python
'''
K-means clusterization test
'''
# Python 2/3 compatibility
from __future__ import print_function
import numpy as np
import cv2
from numpy import random
import sys
PY3 = sys.version_info[0] == 3
if PY3:
xrange = range
from tests_common import NewOpenCVTests
def make_gaussians(cluster_n, img_size):
points = []
ref_distrs = []
sizes = []
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) )
sizes.append(n)
points = np.float32( np.vstack(points) )
return points, ref_distrs, sizes
def getMainLabelConfidence(labels, nLabels):
n = len(labels)
labelsDict = dict.fromkeys(range(nLabels), 0)
labelsConfDict = dict.fromkeys(range(nLabels))
for i in range(n):
labelsDict[labels[i][0]] += 1
for i in range(nLabels):
labelsConfDict[i] = float(labelsDict[i]) / n
return max(labelsConfDict.values())
class kmeans_test(NewOpenCVTests):
def test_kmeans(self):
np.random.seed(10)
cluster_n = 5
img_size = 512
points, _, clusterSizes = make_gaussians(cluster_n, img_size)
term_crit = (cv2.TERM_CRITERIA_EPS, 30, 0.1)
ret, labels, centers = cv2.kmeans(points, cluster_n, None, term_crit, 10, 0)
self.assertEqual(len(centers), cluster_n)
offset = 0
for i in range(cluster_n):
confidence = getMainLabelConfidence(labels[offset : (offset + clusterSizes[i])], cluster_n)
offset += clusterSizes[i]
self.assertGreater(confidence, 0.9)
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