Commit 3a1d050f authored by jay's avatar jay
Browse files

Updates fundamental error computations

parent b83fe5aa

autocnet/transformation/fundamental_matrix.py

+5 −1
Original line number Diff line number Diff line
@@ -45,7 +45,9 @@ def compute_reprojection_error(F, x, x1, index=None): 
    if x1.shape[1] != 3:

        x1 = make_homogeneous(x1)



    if isinstance(x, (pd.Series, pd.DataFrame)):

        x = x.values

    if isinstance(x1, (pd.Series, pd.DataFrame)):

        x1 = x1.values



    # Normalize the vector
@@ -56,7 +58,7 @@ def compute_reprojection_error(F, x, x1, index=None): 
    dist2 = np.sum(l2.conj() * x.T, axis=0)

    F_error = np.sqrt(dist1**2 + dist2**2)



    if index:

    if index is not None:

        F_error = pd.Series(F_error, index=index)



    return F_error
@@ -64,6 +66,8 @@ def compute_reprojection_error(F, x, x1, index=None): 
    l_norms = normalize_vector(x.dot(F.T))

    F_error = np.abs(np.sum(l_norms * x1, axis=1))



    if index:

        F_error = pd.Series(F_error, index=index)

    return F_error



def compute_fundamental_error(F, x, x1):

autocnet/transformation/tests/test_fundamental_matrix.py

+10 −1
Original line number Diff line number Diff line
@@ -86,6 +86,15 @@ class TestFundamentalMatrix(unittest.TestCase): 
                                            self.fixed_x2)

        self.assertTrue(err.mean() < 0.5)



    def test_f_reprojection_error_pd(self):

        index = np.arange(10)[::-1]

        err = fm.compute_reprojection_error(self.fixed_f,

                                            self.fixed_x1,

                                            self.fixed_x2,

                                            index=index)

        self.assertIsInstance(err, pd.Series)

        np.testing.assert_array_equal(err.index, index)



    def test_f_fundamental_error(self):

        err = fm.compute_fundamental_error(self.fixed_f,

                                           self.fixed_x1,
@@ -101,7 +110,7 @@ class TestFundamentalMatrix(unittest.TestCase): 
        F, mask = fm.compute_fundamental_matrix(fp, tp, method='ransac')



        new_mask = fm.update_fundamental_mask(F, fp, tp, threshold=0.5, method='reprojection')

        self.assertEqual(10, new_mask['fundamental'].sum())

        self.assertEqual(8, new_mask['fundamental'].sum())



    def test_update_fundamental_mask_with_index(self):

        np.random.seed(12345)

autocnet/utils/tests/test_utils.py

+0 −10
Original line number Diff line number Diff line
@@ -110,16 +110,6 @@ class TestUtils(unittest.TestCase): 
        cleaned_array = utils.remove_field_name(starray, 'index')

        np.testing.assert_array_equal(cleaned_array, truth)



    def test_normalize_vector(self):

        x = np.array([1,1,1], dtype=np.float)

        y = utils.normalize_vector(x)

        np.testing.assert_array_almost_equal(np.array([ 0.70710678,  0.70710678,  0.70710678]), y)



        x = np.repeat(np.arange(1,5), 3).reshape(-1, 3)

        y = utils.normalize_vector(x)

        truth = np.tile(np.array([ 0.70710678,  0.70710678,  0.70710678]), 4).reshape(4,3)

        np.testing.assert_array_almost_equal(truth, y)



    def test_slope(self):

        x1 = pd.DataFrame({'x': np.arange(1, 11),

                           'y': np.arange(1, 11)})

autocnet/utils/utils.py

+4 −8
Original line number Diff line number Diff line
@@ -42,18 +42,14 @@ def normalize_vector(line): 


    Examples

    --------

    >>> x = np.random.random((3,3))

    >>> x = np.array([3, 1, 2])

    >>> normalize_vector(x)

    array([[ 0.88280225,  0.4697448 ,  0.11460811],

       [ 0.26090555,  0.96536433,  0.91648305],

       [ 0.58271501,  0.81267657,  0.30796395]])

    array([ 0.80178373,  0.26726124,  0.53452248])

    """

    if isinstance(line, pd.DataFrame):

        line = line.values

    n = line[0]**2 + line[1]**2 + line[2]**2

    line /= np.sqrt(n)

    return line



    n = np.sqrt((line[0]**2 + line[1]**2 + line[2]**2))

    return line / abs(n)



def getnearest(iterable, value):

    """