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Open Source Computer Vision Library
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55 lines
1.4 KiB
55 lines
1.4 KiB
15 years ago
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#!/usr/bin/env python
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# 2009-01-16, Xavier Delacour <xavier.delacour@gmail.com>
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import unittest
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from numpy import *;
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from numpy.linalg import *;
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import sys;
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import cvtestutils
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from cv import *;
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from adaptors import *;
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def planted_neighbors(query_points, R = .4):
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n,d = query_points.shape
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data = zeros(query_points.shape)
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for i in range(0,n):
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a = random.rand(d)
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a = random.rand()*R*a/sqrt(sum(a**2))
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data[i] = query_points[i] + a
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return data
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class feature_tree_test(unittest.TestCase):
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def test_kdtree_basic(self):
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n = 1000;
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d = 64;
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query_points = random.rand(n,d)*2-1;
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data = planted_neighbors(query_points)
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tr = cvCreateKDTree(data);
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indices,dist = cvFindFeatures(tr, query_points, 1, 100);
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correct = sum([i == j for j,i in enumerate(indices)])
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assert(correct >= n * .75);
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def test_spilltree_basic(self):
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n = 1000;
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d = 64;
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query_points = random.rand(n,d)*2-1;
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data = planted_neighbors(query_points)
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tr = cvCreateSpillTree(data);
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indices,dist = cvFindFeatures(tr, query_points, 1, 100);
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correct = sum([i == j for j,i in enumerate(indices)])
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assert(correct >= n * .75);
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def suite():
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return unittest.TestLoader().loadTestsFromTestCase(feature_tree_test)
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if __name__ == '__main__':
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suite = suite()
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unittest.TextTestRunner(verbosity=2).run(suite)
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