def test_denoise_tv_chambolle_weighting():
# make sure a specified weight gives consistent results regardless of
# the number of input image dimensions
rstate = cp.random.RandomState(1234)
img2d = astro_gray.copy()
img2d += 0.15 * rstate.standard_normal(img2d.shape)
img2d = cp.clip(img2d, 0, 1)
# generate 4D image by tiling
img4d = cp.tile(img2d[..., None, None], (1, 1, 2, 2))
w = 0.2
denoised_2d = restoration.denoise_tv_chambolle(img2d, weight=w)
denoised_4d = restoration.denoise_tv_chambolle(img4d, weight=w)
assert structural_similarity(denoised_2d, denoised_4d[:, :, 0, 0]) > 0.99
def test_denoise_tv_chambolle_multichannel():
denoised0 = restoration.denoise_tv_chambolle(astro[..., 0], weight=0.1)
denoised = restoration.denoise_tv_chambolle(
astro, weight=0.1, multichannel=True
)
assert_array_equal(denoised[..., 0], denoised0)
# tile astronaut subset to generate 3D+channels data
astro3 = cp.tile(astro[:64, :64, cp.newaxis, :], [1, 1, 2, 1])
# modify along tiled dimension to give non-zero gradient on 3rd axis
astro3[:, :, 0, :] = 2 * astro3[:, :, 0, :]
denoised0 = restoration.denoise_tv_chambolle(astro3[..., 0], weight=0.1)
denoised = restoration.denoise_tv_chambolle(
astro3, weight=0.1, multichannel=True
)
assert_array_equal(denoised[..., 0], denoised0)
def test_denoise_tv_chambolle_1d():
"""Apply the TV denoising algorithm on a 1D sinusoid."""
x = 125 + 100 * cp.sin(cp.linspace(0, 8 * cp.pi, 1000))
x += 20 * cp.random.rand(x.size)
x = cp.clip(x, 0, 255)
res = restoration.denoise_tv_chambolle(x.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < x.std()
def test_denoise_tv_chambolle_float_result_range():
# astronaut image
img = astro_gray
int_astro = cp.multiply(img, 255).astype(np.uint8)
assert cp.max(int_astro) > 1
denoised_int_astro = restoration.denoise_tv_chambolle(int_astro, weight=0.1)
# test if the value range of output float data is within [0.0:1.0]
assert denoised_int_astro.dtype == np.float
assert cp.max(denoised_int_astro) <= 1.0
assert cp.min(denoised_int_astro) >= 0.0
def test_denoise_tv_chambolle_3d():
"""Apply the TV denoising algorithm on a 3D image representing a sphere."""
x, y, z = cp.ogrid[0:40, 0:40, 0:40]
mask = (x - 22) ** 2 + (y - 20) ** 2 + (z - 17) ** 2 < 8 ** 2
mask = 100 * mask.astype(np.float64)
mask += 60
mask += 20 * cp.random.rand(*mask.shape)
mask[mask < 0] = 0
mask[mask > 255] = 255
res = restoration.denoise_tv_chambolle(mask.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < mask.std()
def test_denoise_tv_chambolle_2d():
# astronaut image
img = astro_gray.copy()
# add noise to astronaut
img += 0.5 * img.std() * cp.random.rand(*img.shape)
# clip noise so that it does not exceed allowed range for float images.
img = cp.clip(img, 0, 1)
# denoise
denoised_astro = restoration.denoise_tv_chambolle(img, weight=0.1)
# which dtype?
assert denoised_astro.dtype in [np.float, np.float32, np.float64]
# TODO: grlee77: remove device to host transfers if cuda
# morphological_gradient is implemented
grad = ndi.morphological_gradient(img.get(), size=((3, 3)))
grad_denoised = ndi.morphological_gradient(denoised_astro.get(),
size=((3, 3)))
# test if the total variation has decreased
assert grad_denoised.dtype == np.float
assert np.sqrt((grad_denoised**2).sum()) < np.sqrt((grad**2).sum())
def test_denoise_tv_chambolle_4d():
""" TV denoising for a 4D input."""
im = 255 * cp.random.rand(8, 8, 8, 8)
res = restoration.denoise_tv_chambolle(im.astype(np.uint8), weight=0.1)
assert res.dtype == np.float
assert res.std() * 255 < im.std()