def test_gradient_takeo_double():
t = takeo.copy()
t.pixels = t.pixels.astype(np.float64)
grad_image = gradient(t)
np_grad = _np_gradient(t.pixels)
assert_allclose(grad_image.pixels, np_grad)
def test_gradient_takeo_double():
t = takeo.copy()
t.pixels = t.pixels.astype(np.float64)
grad_image = gradient(t)
np_grad = _np_gradient(t.pixels)
assert_allclose(grad_image.pixels, np_grad)
def test_gradient_takeo_float32():
dtype = np.float32
t = takeo.copy()
t.pixels = t.pixels.astype(dtype)
grad_image = gradient(t)
_check_assertions(grad_image, t.shape, t.n_channels * 2, dtype)
np_grad = _np_gradient(t.pixels)
assert_allclose(grad_image.pixels, np_grad)
def test_gradient_takeo_float32():
dtype = np.float32
t = takeo.copy()
t.pixels = t.pixels.astype(dtype)
grad_image = gradient(t)
_check_assertions(grad_image, t.shape, t.n_channels * 2,
dtype)
np_grad = _np_gradient(t.pixels)
assert_allclose(grad_image.pixels, np_grad)