def test_wiener(dtype):
psf = np.ones((5, 5), dtype=dtype) / 25
data = convolve2d(test_img, psf, 'same')
np.random.seed(0)
data += 0.1 * data.std() * np.random.standard_normal(data.shape)
data = data.astype(dtype, copy=False)
deconvolved = restoration.wiener(data, psf, 0.05)
assert deconvolved.dtype == _supported_float_type(dtype)
rtol, atol = _get_rtol_atol(dtype)
path = fetch('restoration/tests/camera_wiener.npy')
np.testing.assert_allclose(deconvolved,
np.load(path),
rtol=rtol,
atol=atol)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
assert otf.real.dtype == _supported_float_type(dtype)
deconvolved = restoration.wiener(data,
otf,
0.05,
reg=laplacian,
is_real=False)
assert deconvolved.real.dtype == _supported_float_type(dtype)
np.testing.assert_allclose(np.real(deconvolved),
np.load(path),
rtol=rtol,
atol=atol)
def test_wiener():
psf = np.ones((5, 5)) / 25
data = convolve2d(test_img, psf, "same")
np.random.seed(0)
data += 0.1 * data.std() * np.random.standard_normal(data.shape)
deconvolved = restoration.wiener(data, psf, 0.05)
path = pjoin(dirname(abspath(__file__)), "camera_wiener.npy")
np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
deconvolved = restoration.wiener(data, otf, 0.05, reg=laplacian, is_real=False)
np.testing.assert_allclose(np.real(deconvolved), np.load(path), rtol=1e-3)
def test_unsupervised_wiener_deprecated_user_param():
psf = np.ones((5, 5), dtype=float) / 25
data = convolve2d(test_img, psf, 'same')
otf = uft.ir2tf(psf, data.shape, is_real=False)
_, laplacian = uft.laplacian(2, data.shape)
with expected_warnings(
["`max_iter` is a deprecated key", "`min_iter` is a deprecated key"]):
restoration.unsupervised_wiener(data,
otf,
reg=laplacian,
is_real=False,
user_params={
"max_iter": 200,
"min_iter": 30
},
random_state=5)
def test_wiener():
psf = np.ones((5, 5)) / 25
data = convolve2d(test_img, psf, 'same')
np.random.seed(0)
data += 0.1 * data.std() * np.random.standard_normal(data.shape)
deconvolved = restoration.wiener(data, psf, 0.05)
path = fetch('restoration/tests/camera_wiener.npy')
np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
deconvolved = restoration.wiener(data,
otf,
0.05,
reg=laplacian,
is_real=False)
np.testing.assert_allclose(np.real(deconvolved), np.load(path), rtol=1e-3)
def test_unsupervised_wiener():
psf = np.ones((5, 5)) / 25
data = convolve2d(test_img, psf, "same")
np.random.seed(0)
data += 0.1 * data.std() * np.random.standard_normal(data.shape)
deconvolved, _ = restoration.unsupervised_wiener(data, psf)
path = pjoin(dirname(abspath(__file__)), "camera_unsup.npy")
np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
np.random.seed(0)
deconvolved = restoration.unsupervised_wiener(
data, otf, reg=laplacian, is_real=False, user_params={"callback": lambda x: None}
)[0]
path = pjoin(dirname(abspath(__file__)), "camera_unsup2.npy")
np.testing.assert_allclose(np.real(deconvolved), np.load(path), rtol=1e-3)
def test_unsupervised_wiener(dtype):
psf = np.ones((5, 5), dtype=dtype) / 25
data = convolve2d(test_img, psf, 'same')
seed = 16829302
# keep old-style RandomState here for compatibility with previously stored
# reference data in camera_unsup.npy and camera_unsup2.npy
rng = np.random.RandomState(seed)
data += 0.1 * data.std() * rng.standard_normal(data.shape)
data = data.astype(dtype, copy=False)
deconvolved, _ = restoration.unsupervised_wiener(data,
psf,
random_state=seed)
float_type = _supported_float_type(dtype)
assert deconvolved.dtype == float_type
rtol, atol = _get_rtol_atol(dtype)
path = fetch('restoration/tests/camera_unsup.npy')
np.testing.assert_allclose(deconvolved,
np.load(path),
rtol=rtol,
atol=atol)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
assert otf.real.dtype == _supported_float_type(dtype)
deconvolved2 = restoration.unsupervised_wiener(data,
otf,
reg=laplacian,
is_real=False,
user_params={
"callback":
lambda x: None,
"max_num_iter": 200,
"min_num_iter": 30,
},
random_state=seed)[0]
assert deconvolved2.real.dtype == float_type
path = fetch('restoration/tests/camera_unsup2.npy')
np.testing.assert_allclose(np.real(deconvolved2),
np.load(path),
rtol=rtol,
atol=atol)
def test_unsupervised_wiener():
psf = np.ones((5, 5)) / 25
data = convolve2d(test_img, psf, 'same')
np.random.seed(0)
data += 0.1 * data.std() * np.random.standard_normal(data.shape)
deconvolved, _ = restoration.unsupervised_wiener(data, psf)
path = fetch('restoration/tests/camera_unsup.npy')
np.testing.assert_allclose(deconvolved, np.load(path), rtol=1e-3)
_, laplacian = uft.laplacian(2, data.shape)
otf = uft.ir2tf(psf, data.shape, is_real=False)
np.random.seed(0)
deconvolved = restoration.unsupervised_wiener(
data, otf, reg=laplacian, is_real=False,
user_params={"callback": lambda x: None})[0]
path = fetch('restoration/tests/camera_unsup2.npy')
np.testing.assert_allclose(np.real(deconvolved),
np.load(path),
rtol=1e-3)
