Esempio n. 1
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    def test_fitting_identity_list(self):

        listA = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 2]]
        listB = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 2]]

        expectedError = 0
        expectedTransformation = np.asmatrix(np.eye(4, 4))

        transformation, err = psf.pointSetFitting(listA, listB)

        np.testing.assert_array_almost_equal(expectedTransformation,
                                             transformation,
                                             decimal=5)
        self.assertAlmostEqual(expectedError, err, delta=0.0001)
Esempio n. 2
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    def test_fitting_identity(self):

        setA = np.mat('0 0 0; 1 0 0; 0 1 0; 0 0 2').transpose()
        setB = np.mat('0 0 0; 1 0 0; 0 1 0; 0 0 2').transpose()

        expectedError = 0
        expectedTransformation = np.asmatrix(np.eye(4, 4))

        transformation, err = psf.pointSetFitting(setA, setB)

        np.testing.assert_array_almost_equal(expectedTransformation,
                                             transformation,
                                             decimal=5)
        self.assertAlmostEqual(expectedError, err, delta=0.0001)
Esempio n. 3
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    def test_fitting_translation(self):

        setA = np.mat('0 0 0; 1 0 0; 0 1 0; 0 0 2').transpose()
        expectedTransformation = compose([10, 2, 5], np.eye(3, 3), np.ones(3))
        setB = expectedTransformation * psf.toHomogeneous(setA)

        expectedError = 0

        transformation, err = psf.pointSetFitting(setA, setB[0:3, :])

        np.testing.assert_array_almost_equal(expectedTransformation,
                                             transformation,
                                             decimal=5)
        self.assertAlmostEqual(expectedError, err, delta=0.0001)
Esempio n. 4
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    def test_fitting_identity_arrinput(self):

        setA = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0,
                                                           2]]).transpose()
        setB = np.array([[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0,
                                                           2]]).transpose()

        expectedError = 0
        expectedTransformation = np.asmatrix(np.eye(4, 4))

        transformation, err = psf.pointSetFitting(setA, setB)

        np.testing.assert_array_almost_equal(expectedTransformation,
                                             transformation,
                                             decimal=5)
        self.assertAlmostEqual(expectedError, err, delta=0.0001)
Esempio n. 5
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    def test_fitting_noisefree_10P(self):

        for k in xrange(1000):
            setA = np.asmatrix(np.random.rand(3, 10))
            trans = np.random.rand(1, 3)
            expTr = compose(
                trans[0, :],
                euler2mat(np.random.rand(), np.random.rand(),
                          np.random.rand()), np.ones(3))
            setB = expTr * psf.toHomogeneous(setA)

            expError = 0

            transformation, err = psf.pointSetFitting(setA, setB[0:3, :])

            np.testing.assert_array_almost_equal(expTr,
                                                 transformation,
                                                 decimal=5)
            self.assertAlmostEqual(expError, err, delta=0.0001)
Esempio n. 6
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    def test_fitting_vector_dimension(self):
        with self.assertRaises(Exception):
            listA = [[0, 0], [1, 0], [0, 1]]
            listB = [[0, 0], [1, 0], [0, 1]]

            psf.pointSetFitting(listA, listB)
Esempio n. 7
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    def test_fitting_too_few_points(self):
        with self.assertRaises(Exception):
            listA = [[0, 0, 0], [1, 0, 0]]
            listB = [[0, 0, 0], [1, 0, 0]]

            psf.pointSetFitting(listA, listB)
Esempio n. 8
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 def test_fitting_differen_set_lengths(self):
     with self.assertRaises(Exception):
         listA = [[0, 0, 0], [1, 0, 0], [0, 1, 0], [0, 0, 2]]
         listB = [[0, 0, 0], [1, 0, 0], [0, 1, 0]]
         psf.pointSetFitting(listA, listB)