def test_point_map(self):
h_f = Homography(ctypes.c_float)
h_d = Homography(ctypes.c_double)
p_f = EigenArray.from_iterable([2.2, 3.3], ctypes.c_float)
p_d = EigenArray.from_iterable([5.5, 6.6], ctypes.c_double)
# float-float
numpy.testing.assert_almost_equal(h_f.map(p_f), p_f)
# float-double
numpy.testing.assert_almost_equal(h_f.map(p_d), p_d)
# double-float
numpy.testing.assert_almost_equal(h_d.map(p_f), p_f)
# double-double
numpy.testing.assert_almost_equal(h_d.map(p_d), p_d)
def test_point_map(self):
h_f = Homography('f')
h_d = Homography('d')
p_af = EigenArray.from_array([[2.2, 3.3]], 'f')
p_f = p_af.get_matrix()[0]
p_ad = EigenArray.from_array([[5.5, 6.6]], 'd')
p_d = p_ad.get_matrix()[0]
# float-float
numpy.testing.assert_almost_equal(
h_f.map(p_f), p_f
)
# float-double
numpy.testing.assert_almost_equal(
h_f.map(p_d), p_d
)
# double-float
numpy.testing.assert_almost_equal(
h_d.map(p_f), p_f
)
# double-double
numpy.testing.assert_almost_equal(
h_d.map(p_d), p_d
)
# Code to generate truth
h = numpy.random.rand(3,3)
h = h/numpy.linalg.norm(h)
p0 = numpy.random.rand(3); p0[2] = 1
p1 = numpy.dot(h, p0)
p1 = p1[:2]/p1[2]
h_d = Homography.from_matrix(h, 'd')
# map from Numpy array.
numpy.testing.assert_almost_equal(
h_d.map(p0[:2]).ravel(), p1
)
# map from EigenArray
p0 = EigenArray.from_array([p0[:2]])
numpy.testing.assert_almost_equal(
h_d.map(p0.get_matrix()[0]).ravel(), p1
)
# Another explicit case.
p0 = numpy.array([1923.47,645.676,1])
h = numpy.array([[5.491496261770000276e-01,-1.125428185150000038e-01,
1.358427031619999923e+02],
[-1.429513389049999993e-02 ,6.035527375529999849e-01,
5.923971959490000216e+01],
[-2.042570000000000164e-06,-2.871670000000000197e-07,
1]])
p1 = numpy.dot(h, p0); p1 = p1[:2]/p1[2]
H = Homography.from_matrix(h)
P = EigenArray.from_array([p0[:2]])
numpy.testing.assert_almost_equal(
H.map(P.get_matrix()[0]).ravel(), p1
)
def test_point_map(self):
h_f = Homography(ctypes.c_float)
h_d = Homography(ctypes.c_double)
p_f = EigenArray.from_iterable([2.2, 3.3], ctypes.c_float)
p_d = EigenArray.from_iterable([5.5, 6.6], ctypes.c_double)
# float-float
numpy.testing.assert_almost_equal(
h_f.map(p_f), p_f
)
# float-double
numpy.testing.assert_almost_equal(
h_f.map(p_d), p_d
)
# double-float
numpy.testing.assert_almost_equal(
h_d.map(p_f), p_f
)
# double-double
numpy.testing.assert_almost_equal(
h_d.map(p_d), p_d
)
# Code to generate truth
h = numpy.random.rand(3,3)
h = h/numpy.linalg.norm(h)
p0 = numpy.random.rand(3); p0[2] = 1
p1 = numpy.dot(h, p0)
p1 = p1[:2]/p1[2]
h_d = Homography.from_matrix(h, ctypes.c_double)
# map from Numpy array.
numpy.testing.assert_almost_equal(
h_d.map(p0[:2]).ravel(), p1
)
# map from EigenArray
p0 = EigenArray.from_iterable(p0[:2])
numpy.testing.assert_almost_equal(
h_d.map(p0).ravel(), p1
)
# Another explicit case.
p0 = numpy.array([1923.47,645.676,1])
h = numpy.array([[5.491496261770000276e-01,-1.125428185150000038e-01,
1.358427031619999923e+02],
[-1.429513389049999993e-02 ,6.035527375529999849e-01,
5.923971959490000216e+01],
[-2.042570000000000164e-06,-2.871670000000000197e-07,
1]])
p1 = numpy.dot(h, p0); p1 = p1[:2]/p1[2]
H = Homography.from_matrix(h)
P = EigenArray.from_iterable(p0[:2])
numpy.testing.assert_almost_equal(
H.map(P).ravel(), p1
)
