Add k-means python test, fix loading images in calibration test

modules/python/test/test.py

@@ -27,6 +27,7 @@ from test_houghcircles import houghcircles_test
 from test_houghlines import houghlines_test
 from test_gaussian_mix import gaussian_mix_test
 from test_facedetect import facedetect_test
+from test_kmeans import kmeans_test
 
 # Python 3 moved urlopen to urllib.requests
 try:

modules/python/test/test_calibration.py

@@ -23,7 +23,7 @@ class calibration_test(NewOpenCVTests):
         for i in range(1, 15):
             if i < 10:
                 img_names.append('samples/data/left0{}.jpg'.format(str(i)))
-            else:
+            elif i != 10:
                 img_names.append('samples/data/left{}.jpg'.format(str(i)))
 
         square_size = 1.0

modules/python/test/test_kmeans.py

@@ -0,0 +1,73 @@
+#!/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
+
+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
+
+        # generating bright palette
+        colors = np.zeros((1, cluster_n, 3), np.uint8)
+        colors[0,:] = 255
+        colors[0,:,0] = np.arange(0, 180, 180.0/cluster_n)
+        colors = cv2.cvtColor(colors, cv2.COLOR_HSV2BGR)[0]
+
+        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)