def test_wavelet_denoising_nd():
rstate = np.random.RandomState(1234)
for method in ['VisuShrink', 'BayesShrink']:
for ndim in range(1, 5):
# Generate a very simple test image
if ndim < 3:
img = 0.2*np.ones((128, )*ndim)
else:
img = 0.2*np.ones((16, )*ndim)
img[(slice(5, 13), ) * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Mark H. 2018.08:
# The issue arises because when ndim in [1, 2]
# ``waverecn`` calls ``_match_coeff_dims``
# Which includes a numpy 1.15 deprecation.
# for larger number of dimensions _match_coeff_dims isn't called
# for some reason.
anticipated_warnings = (PYWAVELET_ND_INDEXING_WARNING
if ndim < 3 else None)
with expected_warnings([anticipated_warnings]):
# Verify that SNR is improved with internally estimated sigma
denoised = restoration.denoise_wavelet(noisy, method=method)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def test_clear_border_non_binary_3d():
image3d = np.array(
[[[1, 2, 3, 1, 2],
[3, 3, 3, 4, 2],
[3, 4, 3, 4, 2],
[3, 3, 2, 1, 2]],
[[1, 2, 3, 1, 2],
[3, 3, 5, 4, 2],
[3, 4, 5, 4, 2],
[3, 3, 2, 1, 2]],
[[1, 2, 3, 1, 2],
[3, 3, 3, 4, 2],
[3, 4, 3, 4, 2],
[3, 3, 2, 1, 2]],
])
result = clear_border(image3d)
expected = np.array(
[[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 0, 5, 0, 0],
[0, 0, 5, 0, 0],
[0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]],
])
assert_array_equal(result, expected)
assert_(not np.all(image3d == result))
def test_wavelet_denoising_levels():
rstate = np.random.RandomState(1234)
ndim = 2
N = 256
wavelet = 'db1'
# Generate a very simple test image
img = 0.2*np.ones((N, )*ndim)
img[[slice(5, 13), ] * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet)
denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
wavelet_levels=1)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
psnr_denoised_1 = compare_psnr(img, denoised_1)
# multi-level case should outperform single level case
assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)
# invalid number of wavelet levels results in a ValueError
max_level = pywt.dwt_max_level(np.min(img.shape),
pywt.Wavelet(wavelet).dec_len)
with testing.raises(ValueError):
restoration.denoise_wavelet(
noisy,
wavelet=wavelet, wavelet_levels=max_level+1)
with testing.raises(ValueError):
restoration.denoise_wavelet(
noisy,
wavelet=wavelet, wavelet_levels=-1)
def test_mask():
length = 100
ramps = [np.linspace(0, 4 * np.pi, length),
np.linspace(0, 8 * np.pi, length),
np.linspace(0, 6 * np.pi, length)]
image = np.vstack(ramps)
mask_1d = np.ones((length,), dtype=np.bool)
mask_1d[0] = mask_1d[-1] = False
for i in range(len(ramps)):
# mask all ramps but the i'th one
mask = np.zeros(image.shape, dtype=np.bool)
mask |= mask_1d.reshape(1, -1)
mask[i, :] = False # unmask i'th ramp
image_wrapped = np.ma.array(np.angle(np.exp(1j * image)), mask=mask)
image_unwrapped = unwrap_phase(image_wrapped)
image_unwrapped -= image_unwrapped[0, 0] # remove phase shift
# The end of the unwrapped array should have value equal to the
# endpoint of the unmasked ramp
assert_array_almost_equal_nulp(image_unwrapped[:, -1], image[i, -1])
assert_(np.ma.isMaskedArray(image_unwrapped))
# Same tests, but forcing use of the 3D unwrapper by reshaping
with expected_warnings(['length 1 dimension']):
shape = (1,) + image_wrapped.shape
image_wrapped_3d = image_wrapped.reshape(shape)
image_unwrapped_3d = unwrap_phase(image_wrapped_3d)
# remove phase shift
image_unwrapped_3d -= image_unwrapped_3d[0, 0, 0]
assert_array_almost_equal_nulp(image_unwrapped_3d[:, :, -1],
image[i, -1])
def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, user-specified threshold instead of BayesShrink sigmas
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
threshold=sigma)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# either method or threshold must be defined
with testing.raises(ValueError):
_wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)
# warns if a threshold is provided in a case where it would be ignored
with expected_warnings(["Thresholding method ",
PYWAVELET_ND_INDEXING_WARNING]):
_wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
threshold=sigma)
def check_wrap_around(ndim, axis):
# create a ramp, but with the last pixel along axis equalling the first
elements = 100
ramp = np.linspace(0, 12 * np.pi, elements)
ramp[-1] = ramp[0]
image = ramp.reshape(tuple([elements if n == axis else 1
for n in range(ndim)]))
image_wrapped = np.angle(np.exp(1j * image))
index_first = tuple([0] * ndim)
index_last = tuple([-1 if n == axis else 0 for n in range(ndim)])
# unwrap the image without wrap around
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_no_wrap_around = unwrap_phase(image_wrapped, seed=0)
print('endpoints without wrap_around:',
image_unwrap_no_wrap_around[index_first],
image_unwrap_no_wrap_around[index_last])
# without wrap around, the endpoints of the image should differ
assert_(abs(image_unwrap_no_wrap_around[index_first] -
image_unwrap_no_wrap_around[index_last]) > np.pi)
# unwrap the image with wrap around
wrap_around = [n == axis for n in range(ndim)]
with warnings.catch_warnings():
# We do not want warnings about length 1 dimensions
warnings.simplefilter("ignore")
image_unwrap_wrap_around = unwrap_phase(image_wrapped, wrap_around,
seed=0)
print('endpoints with wrap_around:',
image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
# with wrap around, the endpoints of the image should be equal
assert_almost_equal(image_unwrap_wrap_around[index_first],
image_unwrap_wrap_around[index_last])
def test_denoise_tv_chambolle_1d():
"""Apply the TV denoising algorithm on a 1D sinusoid."""
x = 125 + 100*np.sin(np.linspace(0, 8*np.pi, 1000))
x += 20 * np.random.rand(x.size)
x = np.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_no_denoising_for_small_h():
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
img += 0.3*np.random.randn(*img.shape)
# very small h should result in no averaging with other patches
denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=True,
multichannel=True)
assert_(np.allclose(denoised, img))
denoised = restoration.denoise_nl_means(img, 7, 5, 0.01, fast_mode=False,
multichannel=True)
assert_(np.allclose(denoised, img))
def test_denoise_tv_chambolle_3d():
"""Apply the TV denoising algorithm on a 3D image representing a sphere."""
x, y, z = np.ogrid[0:40, 0:40, 0:40]
mask = (x - 22)**2 + (y - 20)**2 + (z - 17)**2 < 8**2
mask = 100 * mask.astype(np.float)
mask += 60
mask += 20 * np.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_bregman_3d():
img = checkerboard.copy()
# add some random noise
img += 0.5 * img.std() * np.random.rand(*img.shape)
img = np.clip(img, 0, 1)
out1 = restoration.denoise_tv_bregman(img, weight=10)
out2 = restoration.denoise_tv_bregman(img, weight=5)
# make sure noise is reduced in the checkerboard cells
assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
def test_denoise_nl_means_multichannel():
# for true 3D data, 3D denoising is better than denoising as 2D+channels
img = np.zeros((13, 10, 8))
img[6, 4:6, 2:-2] = 1.
sigma = 0.3
imgn = img + sigma * np.random.randn(*img.shape)
denoised_wrong_multichannel = restoration.denoise_nl_means(
imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=True)
denoised_ok_multichannel = restoration.denoise_nl_means(
imgn, 3, 4, 0.6 * sigma, fast_mode=True, multichannel=False)
psnr_wrong = compare_psnr(img, denoised_wrong_multichannel)
psnr_ok = compare_psnr(img, denoised_ok_multichannel)
assert_(psnr_ok > psnr_wrong)
def test_denoise_bilateral_color():
img = checkerboard.copy()[:50, :50]
# add some random noise
img += 0.5 * img.std() * np.random.rand(*img.shape)
img = np.clip(img, 0, 1)
out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
sigma_spatial=10, multichannel=True)
out2 = restoration.denoise_bilateral(img, sigma_color=0.2,
sigma_spatial=20, multichannel=True)
# make sure noise is reduced in the checkerboard cells
assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
def test_wavelet_denoising(img, multichannel, convert2ycbcr):
rstate = np.random.RandomState(1234)
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Verify that SNR is improved when true sigma is used
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy,
sigma=sigma,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# Verify that SNR is improved with internally estimated sigma
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# SNR is improved less with 1 wavelet level than with the default.
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised_1 = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
wavelet_levels=1,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)
assert_(psnr_denoised > psnr_denoised_1)
assert_(psnr_denoised_1 > psnr_noisy)
# Test changing noise_std (higher threshold, so less energy in signal)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res1 = restoration.denoise_wavelet(noisy,
sigma=2 * sigma,
multichannel=multichannel,
rescale_sigma=True)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res2 = restoration.denoise_wavelet(noisy,
sigma=sigma,
multichannel=multichannel,
rescale_sigma=True)
assert_(np.sum(res1**2) <= np.sum(res2**2))
def test_wavelet_denoising_levels(rescale_sigma):
rstate = np.random.RandomState(1234)
ndim = 2
N = 256
wavelet = 'db1'
# Generate a very simple test image
img = 0.2*np.ones((N, )*ndim)
img[(slice(5, 13), ) * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet,
rescale_sigma=rescale_sigma)
denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
wavelet_levels=1,
rescale_sigma=rescale_sigma)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)
# multi-level case should outperform single level case
assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)
# invalid number of wavelet levels results in a ValueError or UserWarning
max_level = pywt.dwt_max_level(np.min(img.shape),
pywt.Wavelet(wavelet).dec_len)
if Version(pywt.__version__) < '1.0.0':
# exceeding max_level raises a ValueError in PyWavelets 0.4-0.5.2
with testing.raises(ValueError):
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
restoration.denoise_wavelet(
noisy, wavelet=wavelet, wavelet_levels=max_level + 1,
rescale_sigma=rescale_sigma)
else:
# exceeding max_level raises a UserWarning in PyWavelets >= 1.0.0
with expected_warnings([
'all coefficients will experience boundary effects']):
restoration.denoise_wavelet(
noisy, wavelet=wavelet, wavelet_levels=max_level + 1,
rescale_sigma=rescale_sigma)
with testing.raises(ValueError):
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
restoration.denoise_wavelet(
noisy,
wavelet=wavelet, wavelet_levels=-1,
rescale_sigma=rescale_sigma)
def test_no_denoising_for_small_h(fast_mode, dtype):
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
img += 0.3*np.random.randn(*img.shape)
img = img.astype(dtype)
# very small h should result in no averaging with other patches
denoised = restoration.denoise_nl_means(img, 7, 5, 0.01,
fast_mode=fast_mode,
multichannel=True)
assert_(np.allclose(denoised, img))
denoised = restoration.denoise_nl_means(img, 7, 5, 0.01,
fast_mode=fast_mode,
multichannel=True)
assert_(np.allclose(denoised, img))
def test_wavelet_denoising():
rstate = np.random.RandomState(1234)
# version with one odd-sized dimension
astro_gray_odd = astro_gray[:, :-1]
astro_odd = astro[:, :-1]
for img, multichannel, convert2ycbcr in [(astro_gray, False, False),
(astro_gray_odd, False, False),
(astro_odd, True, False),
(astro_odd, True, True)]:
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Verify that SNR is improved when true sigma is used
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy, sigma=sigma,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# Verify that SNR is improved with internally estimated sigma
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# SNR is improved less with 1 wavelet level than with the default.
denoised_1 = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
wavelet_levels=1,
convert2ycbcr=convert2ycbcr)
psnr_denoised_1 = compare_psnr(img, denoised_1)
assert_(psnr_denoised > psnr_denoised_1)
assert_(psnr_denoised_1 > psnr_noisy)
# Test changing noise_std (higher threshold, so less energy in signal)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res1 = restoration.denoise_wavelet(noisy, sigma=2 * sigma,
multichannel=multichannel)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res2 = restoration.denoise_wavelet(noisy, sigma=sigma,
multichannel=multichannel)
assert_(np.sum(res1**2) <= np.sum(res2**2))
def test_denoise_nl_means_multichannel(fast_mode, dtype):
# for true 3D data, 3D denoising is better than denoising as 2D+channels
img = np.zeros((13, 10, 8), dtype=dtype)
img[6, 4:6, 2:-2] = 1.
sigma = 0.3
imgn = img + sigma * np.random.randn(*img.shape)
imgn = imgn.astype(dtype)
denoised_wrong_multichannel = restoration.denoise_nl_means(
imgn, 3, 4, 0.6 * sigma, fast_mode=fast_mode, multichannel=True)
denoised_ok_multichannel = restoration.denoise_nl_means(
imgn, 3, 4, 0.6 * sigma, fast_mode=fast_mode, multichannel=False)
psnr_wrong = peak_signal_noise_ratio(img, denoised_wrong_multichannel)
psnr_ok = peak_signal_noise_ratio(img, denoised_ok_multichannel)
assert_(psnr_ok > psnr_wrong)
def test_range():
"""Output of edge detection should be in [0, 1]"""
image = np.random.random((100, 100))
for detector in (filters.sobel, filters.scharr,
filters.prewitt, filters.roberts):
out = detector(image)
assert_(out.min() >= 0,
"Minimum of `{0}` is smaller than zero".format(
detector.__name__)
)
assert_(out.max() <= 1,
"Maximum of `{0}` is larger than 1".format(
detector.__name__)
)
def test_wavelet_denoising():
rstate = np.random.RandomState(1234)
# version with one odd-sized dimension
astro_gray_odd = astro_gray[:, :-1]
astro_odd = astro[:, :-1]
for img, multichannel, convert2ycbcr in [(astro_gray, False, False),
(astro_gray_odd, False, False),
(astro_odd, True, False),
(astro_odd, True, True)]:
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Verify that SNR is improved when true sigma is used
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy, sigma=sigma,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# Verify that SNR is improved with internally estimated sigma
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
convert2ycbcr=convert2ycbcr)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# SNR is improved less with 1 wavelet level than with the default.
denoised_1 = restoration.denoise_wavelet(noisy,
multichannel=multichannel,
wavelet_levels=1,
convert2ycbcr=convert2ycbcr)
psnr_denoised_1 = compare_psnr(img, denoised_1)
assert_(psnr_denoised > psnr_denoised_1)
assert_(psnr_denoised_1 > psnr_noisy)
# Test changing noise_std (higher threshold, so less energy in signal)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res1 = restoration.denoise_wavelet(noisy, sigma=2 * sigma,
multichannel=multichannel)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
res2 = restoration.denoise_wavelet(noisy, sigma=sigma,
multichannel=multichannel)
assert_(np.sum(res1**2) <= np.sum(res2**2))
def test_denoise_bilateral_color():
img = checkerboard.copy()[:50, :50]
# add some random noise
img += 0.5 * img.std() * np.random.rand(*img.shape)
img = np.clip(img, 0, 1)
out1 = restoration.denoise_bilateral(img, sigma_color=0.1,
sigma_spatial=10, multichannel=True)
out2 = restoration.denoise_bilateral(img, sigma_color=0.2,
sigma_spatial=20, multichannel=True)
# make sure noise is reduced in the checkerboard cells
assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
def test_denoise_tv_chambolle_weighting():
# make sure a specified weight gives consistent results regardless of
# the number of input image dimensions
rstate = np.random.RandomState(1234)
img2d = astro_gray.copy()
img2d += 0.15 * rstate.standard_normal(img2d.shape)
img2d = np.clip(img2d, 0, 1)
# generate 4D image by tiling
img4d = np.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_nl_means_2d():
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
sigma = 0.3
img += sigma * np.random.randn(*img.shape)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
fast_mode=False,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
def test_range():
"""Output of edge detection should be in [0, 1]"""
image = np.random.random((100, 100))
for detector in (filters.sobel, filters.scharr,
filters.prewitt, filters.roberts,
filters.farid):
out = detector(image)
assert_(out.min() >= 0,
"Minimum of `{0}` is smaller than zero".format(
detector.__name__)
)
assert_(out.max() <= 1,
"Maximum of `{0}` is larger than 1".format(
detector.__name__)
)
def test_denoise_nl_means_2d():
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
sigma = 0.3
img += sigma * np.random.randn(*img.shape)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(img, 7, 5, 0.2, fast_mode=True,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
fast_mode=False,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
def test_denoise_nl_means_3d():
img = np.zeros((20, 20, 10))
img[5:-5, 5:-5, 3:-3] = 1.
sigma = 0.3
img += sigma * np.random.randn(*img.shape)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(img, 5, 4, 0.2, fast_mode=True,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
denoised = restoration.denoise_nl_means(img, 5, 4, 0.2,
fast_mode=False,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
def test_denoise_nl_means_multichannel():
img = np.zeros((21, 20, 10))
img[10, 9:11, 2:-2] = 1.
img += 0.3 * np.random.randn(*img.shape)
denoised_wrong_multichannel = restoration.denoise_nl_means(
img, 5, 4, 0.1, fast_mode=True, multichannel=True)
denoised_ok_multichannel = restoration.denoise_nl_means(img,
5,
4,
0.1,
fast_mode=True,
multichannel=False)
snr_wrong = 10 * np.log10(1. /
((denoised_wrong_multichannel - img)**2).mean())
snr_ok = 10 * np.log10(1. / ((denoised_ok_multichannel - img)**2).mean())
assert_(snr_ok > snr_wrong)
def test_laplacian_pyramid_max_layers():
for downscale in [2, 3, 5, 7]:
img = np.random.randn(32, 8)
pyramid = pyramids.pyramid_laplacian(img, downscale=downscale,
multichannel=False)
max_layer = int(np.ceil(math.log(np.max(img.shape), downscale)))
for layer, out in enumerate(pyramid):
if layer < max_layer:
# should not reach all axes as size 1 prior to final level
assert_(np.max(out.shape) > 1)
# total number of images is max_layer + 1
assert_equal(max_layer, layer)
# final layer should be size 1 on all axes
assert_array_equal((out.shape), (1, 1))
def test_clear_border_non_binary_3d():
image3d = np.array([
[[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
[[1, 2, 3, 1, 2], [3, 3, 5, 4, 2], [3, 4, 5, 4, 2], [3, 3, 2, 1, 2]],
[[1, 2, 3, 1, 2], [3, 3, 3, 4, 2], [3, 4, 3, 4, 2], [3, 3, 2, 1, 2]],
])
result = clear_border(image3d)
expected = np.array([
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0], [0, 0, 5, 0, 0], [0, 0, 5, 0, 0], [0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]],
])
assert_array_equal(result, expected)
assert_(not np.all(image3d == result))
def test_unwrap_3d_all_masked():
# all elements masked
image = np.ma.zeros((10, 10, 10))
image[:] = np.ma.masked
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.all(unwrap.mask))
# 1 unmasked element, still zero edges
image = np.ma.zeros((10, 10, 10))
image[:] = np.ma.masked
image[0, 0, 0] = 0
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.sum(unwrap.mask) == 999) # all but one masked
assert_(unwrap[0, 0, 0] == 0)
def test_denoise_tv_chambolle_weighting():
# make sure a specified weight gives consistent results regardless of
# the number of input image dimensions
rstate = np.random.RandomState(1234)
img2d = astro_gray.copy()
img2d += 0.15 * rstate.standard_normal(img2d.shape)
img2d = np.clip(img2d, 0, 1)
# generate 4D image by tiling
img4d = np.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_(measure.compare_ssim(denoised_2d,
denoised_4d[:, :, 0, 0]) > 0.99)
def test_unwrap_3d_all_masked():
# all elements masked
image = np.ma.zeros((10, 10, 10))
image[:] = np.ma.masked
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.all(unwrap.mask))
# 1 unmasked element, still zero edges
image = np.ma.zeros((10, 10, 10))
image[:] = np.ma.masked
image[0, 0, 0] = 0
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.sum(unwrap.mask) == 999) # all but one masked
assert_(unwrap[0, 0, 0] == 0)
def test_wavelet_denoising(img, multichannel, convert2ycbcr):
rstate = np.random.RandomState(1234)
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
channel_axis = -1 if multichannel else None
# Verify that SNR is improved when true sigma is used
denoised = restoration.denoise_wavelet(noisy,
sigma=sigma,
channel_axis=channel_axis,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# Verify that SNR is improved with internally estimated sigma
denoised = restoration.denoise_wavelet(noisy,
channel_axis=channel_axis,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# SNR is improved less with 1 wavelet level than with the default.
denoised_1 = restoration.denoise_wavelet(noisy,
channel_axis=channel_axis,
wavelet_levels=1,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_denoised_1 = peak_signal_noise_ratio(img, denoised_1)
assert_(psnr_denoised > psnr_denoised_1)
assert_(psnr_denoised_1 > psnr_noisy)
# Test changing noise_std (higher threshold, so less energy in signal)
res1 = restoration.denoise_wavelet(noisy,
sigma=2 * sigma,
channel_axis=channel_axis,
rescale_sigma=True)
res2 = restoration.denoise_wavelet(noisy,
sigma=sigma,
channel_axis=channel_axis,
rescale_sigma=True)
assert_(np.sum(res1**2) <= np.sum(res2**2))
def test_denoise_nl_means_3d():
img = np.zeros((12, 12, 8))
img[5:-5, 5:-5, 2:-2] = 1.
sigma = 0.3
imgn = img + sigma * np.random.randn(*img.shape)
psnr_noisy = compare_psnr(img, imgn)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
fast_mode=True,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(compare_psnr(img, denoised) > psnr_noisy)
denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
fast_mode=False,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(compare_psnr(img, denoised) > psnr_noisy)
def test_calibrate_denoiser_extra_output():
parameter_ranges = {'sigma': np.linspace(0.1, 1, 5) / 2}
_, (parameters_tested,
losses) = calibrate_denoiser(noisy_img,
_denoise_wavelet,
denoise_parameters=parameter_ranges,
extra_output=True)
all_denoised = [
_invariant_denoise(noisy_img,
_denoise_wavelet,
denoiser_kwargs=denoiser_kwargs)
for denoiser_kwargs in parameters_tested
]
ground_truth_losses = [mse(img, test_img) for img in all_denoised]
assert_(np.argmin(losses) == np.argmin(ground_truth_losses))
def test_denoise_nl_means_3d():
img = np.zeros((12, 12, 8))
img[5:-5, 5:-5, 2:-2] = 1.
sigma = 0.3
imgn = img + sigma * np.random.randn(*img.shape)
psnr_noisy = compare_psnr(img, imgn)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
fast_mode=True,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(compare_psnr(img, denoised) > psnr_noisy)
denoised = restoration.denoise_nl_means(imgn, 3, 4, h=0.75 * sigma,
fast_mode=False,
multichannel=False, sigma=s)
# make sure noise is reduced
assert_(compare_psnr(img, denoised) > psnr_noisy)
def test_denoise_nl_means_3d(fast_mode, dtype):
img = np.zeros((12, 12, 8), dtype=dtype)
img[5:-5, 5:-5, 2:-2] = 1.
sigma = 0.3
imgn = img + sigma * np.random.randn(*img.shape)
imgn = imgn.astype(dtype)
psnr_noisy = peak_signal_noise_ratio(img, imgn)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(imgn,
3,
4,
h=0.75 * sigma,
fast_mode=fast_mode,
multichannel=False,
sigma=s)
# make sure noise is reduced
assert_(peak_signal_noise_ratio(img, denoised) > psnr_noisy)
def test_denoise_tv_bregman_float_result_range():
# astronaut image
img = astro_gray.copy()
int_astro = np.multiply(img, 255).astype(np.uint8)
assert_(np.max(int_astro) > 1)
denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert_(denoised_int_astro.dtype == np.float)
assert_(np.max(denoised_int_astro) <= 1.0)
assert_(np.min(denoised_int_astro) >= 0.0)
def test_denoise_tv_bregman_float_result_range():
# astronaut image
img = astro_gray.copy()
int_astro = np.multiply(img, 255).astype(np.uint8)
assert_(np.max(int_astro) > 1)
denoised_int_astro = restoration.denoise_tv_bregman(int_astro, weight=60.0)
# test if the value range of output float data is within [0.0:1.0]
assert_(denoised_int_astro.dtype == np.float)
assert_(np.max(denoised_int_astro) <= 1.0)
assert_(np.min(denoised_int_astro) >= 0.0)
def test_denoise_tv_chambolle_float_result_range():
# astronaut image
img = astro_grayT
int_astroT = torch.mul(img, 255).type(torch.uint8)
assert_(torch.max(int_astroT) > 1)
denoised_int_astroT = denoise_tv_chambolle_torch(int_astroT, weight=0.1)
# test if the value range of output float data is within [0.0:1.0]
assert_((denoised_int_astroT.numpy()).dtype == np.float)
assert_(torch.max(denoised_int_astroT) <= 1.0)
assert_(torch.min(denoised_int_astroT) >= 0.0)
def test_wavelet_denoising_levels():
rstate = np.random.RandomState(1234)
ndim = 2
N = 256
wavelet = 'db1'
# Generate a very simple test image
img = 0.2*np.ones((N, )*ndim)
img[(slice(5, 13), ) * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
denoised = restoration.denoise_wavelet(noisy, wavelet=wavelet)
denoised_1 = restoration.denoise_wavelet(noisy, wavelet=wavelet,
wavelet_levels=1)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
psnr_denoised_1 = compare_psnr(img, denoised_1)
# multi-level case should outperform single level case
assert_(psnr_denoised > psnr_denoised_1 > psnr_noisy)
# invalid number of wavelet levels results in a ValueError or UserWarning
max_level = pywt.dwt_max_level(np.min(img.shape),
pywt.Wavelet(wavelet).dec_len)
if Version(pywt.__version__) < '1.0.0':
# exceeding max_level raises a ValueError in PyWavelets 0.4-0.5.2
with testing.raises(ValueError):
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
restoration.denoise_wavelet(
noisy, wavelet=wavelet, wavelet_levels=max_level + 1)
else:
# exceeding max_level raises a UserWarning in PyWavelets >= 1.0.0
with expected_warnings([
'all coefficients will experience boundary effects']):
restoration.denoise_wavelet(
noisy, wavelet=wavelet, wavelet_levels=max_level + 1)
with testing.raises(ValueError):
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING]):
restoration.denoise_wavelet(
noisy,
wavelet=wavelet, wavelet_levels=-1)
def test_unwrap_2d_all_masked():
# Segmentation fault when image is masked array with a all elements masked
# GitHub issue #1347
# all elements masked
image = np.ma.zeros((10, 10))
image[:] = np.ma.masked
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.all(unwrap.mask))
# 1 unmasked element, still zero edges
image = np.ma.zeros((10, 10))
image[:] = np.ma.masked
image[0, 0] = 0
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.sum(unwrap.mask) == 99) # all but one masked
assert_(unwrap[0, 0] == 0)
def test_unwrap_2d_all_masked():
# Segmentation fault when image is masked array with a all elements masked
# GitHub issue #1347
# all elements masked
image = np.ma.zeros((10, 10))
image[:] = np.ma.masked
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.all(unwrap.mask))
# 1 unmasked element, still zero edges
image = np.ma.zeros((10, 10))
image[:] = np.ma.masked
image[0, 0] = 0
unwrap = unwrap_phase(image)
assert_(np.ma.isMaskedArray(unwrap))
assert_(np.sum(unwrap.mask) == 99) # all but one masked
assert_(unwrap[0, 0] == 0)
def test_denoise_nl_means_2drgb():
# reduce image size because nl means is very slow
img = np.copy(astro[:50, :50])
# add some random noise
sigma = 0.5
img += sigma * img.std() * np.random.random(img.shape)
img = np.clip(img, 0, 1)
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(img, 7, 9, 0.3,
fast_mode=True,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
denoised = restoration.denoise_nl_means(img, 7, 9, 0.3,
fast_mode=False,
multichannel=True, sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
def test_denoise_tv_chambolle_weighting():
# make sure a specified weight gives consistent results regardless of
# the number of input image dimensions
rstate = np.random.RandomState(1234)
img2d = astro_gray.copy()
img2d += 0.15 * rstate.standard_normal(img2d.shape)
img2d = np.clip(img2d, 0, 1)
# generate 4D image by tiling
img4d = np.tile(img2d[..., None, None], (1, 1, 2, 2))
img2dT = torch.tensor(img2d)
img4dT = torch.tensor(img4d)
w = 0.2
denoised_2d = denoise_tv_chambolle_torch(img2dT, weight=w)
denoised_4d = denoise_tv_chambolle_torch(img4dT, weight=w)
assert_(
measure.compare_ssim(denoised_2d.numpy(),
denoised_4d[:, :, 0, 0].numpy()) > 0.99)
def test_wavelet_denoising_channel_axis(channel_axis, convert2ycbcr):
rstate = np.random.RandomState(1234)
sigma = 0.1
img = astro_odd
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
img = np.moveaxis(img, -1, channel_axis)
noisy = np.moveaxis(noisy, -1, channel_axis)
# Verify that SNR is improved when true sigma is used
denoised = restoration.denoise_wavelet(noisy,
sigma=sigma,
channel_axis=channel_axis,
convert2ycbcr=convert2ycbcr,
rescale_sigma=True)
psnr_noisy = peak_signal_noise_ratio(img, noisy)
psnr_denoised = peak_signal_noise_ratio(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def assert_phase_almost_equal(a, b, *args, **kwargs):
"""An assert_almost_equal insensitive to phase shifts of n*2*pi."""
shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
print('assert_phase_allclose, rel',
np.max(np.abs((a - (b - shift)) / a)))
if np.ma.isMaskedArray(a):
assert_(np.ma.isMaskedArray(b))
assert_array_equal(a.mask, b.mask)
au = np.asarray(a)
bu = np.asarray(b)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
print('assert_phase_allclose, no mask, abs',
np.max(np.abs(au - (bu - shift))))
print('assert_phase_allclose, no mask, rel',
np.max(np.abs((au - (bu - shift)) / au)))
assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
def test_denoise_bilateral_multichannel_deprecation():
img = checkerboard.copy()[:50, :50]
# add some random noise
img += 0.5 * img.std() * np.random.rand(*img.shape)
img = np.clip(img, 0, 1)
with expected_warnings(["`multichannel` is a deprecated argument"]):
out1 = restoration.denoise_bilateral(img,
sigma_color=0.1,
sigma_spatial=10,
multichannel=True)
with expected_warnings(["`multichannel` is a deprecated argument"]):
out2 = restoration.denoise_bilateral(img,
sigma_color=0.2,
sigma_spatial=20,
multichannel=True)
# make sure noise is reduced in the checkerboard cells
assert_(img[30:45, 5:15].std() > out1[30:45, 5:15].std())
assert_(out1[30:45, 5:15].std() > out2[30:45, 5:15].std())
def test_wavelet_denoising_nd():
rstate = np.random.RandomState(1234)
for method in ['VisuShrink', 'BayesShrink']:
for ndim in range(1, 5):
# Generate a very simple test image
if ndim < 3:
img = 0.2*np.ones((128, )*ndim)
else:
img = 0.2*np.ones((16, )*ndim)
img[[slice(5, 13), ] * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Verify that SNR is improved with internally estimated sigma
denoised = restoration.denoise_wavelet(noisy, method=method)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def assert_phase_almost_equal(a, b, *args, **kwargs):
"""An assert_almost_equal insensitive to phase shifts of n*2*pi."""
shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
with warnings.catch_warnings():
warnings.simplefilter("ignore")
print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
print('assert_phase_allclose, rel',
np.max(np.abs((a - (b - shift)) / a)))
if np.ma.isMaskedArray(a):
assert_(np.ma.isMaskedArray(b))
assert_array_equal(a.mask, b.mask)
au = np.asarray(a)
bu = np.asarray(b)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
print('assert_phase_allclose, no mask, abs',
np.max(np.abs(au - (bu - shift))))
print('assert_phase_allclose, no mask, rel',
np.max(np.abs((au - (bu - shift)) / au)))
assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
def test_wavelet_denoising_nd():
rstate = np.random.RandomState(1234)
for method in ['VisuShrink', 'BayesShrink']:
for ndim in range(1, 5):
# Generate a very simple test image
if ndim < 3:
img = 0.2*np.ones((128, )*ndim)
else:
img = 0.2*np.ones((16, )*ndim)
img[[slice(5, 13), ] * ndim] = 0.8
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# Verify that SNR is improved with internally estimated sigma
denoised = restoration.denoise_wavelet(noisy, method=method)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
def assert_phase_almost_equal(a, b, *args, **kwargs):
"""An assert_almost_equal insensitive to phase shifts of n*2*pi."""
shift = 2 * np.pi * np.round((b.mean() - a.mean()) / (2 * np.pi))
with expected_warnings([r'invalid value encountered|\A\Z',
r'divide by zero encountered|\A\Z']):
print('assert_phase_allclose, abs', np.max(np.abs(a - (b - shift))))
print('assert_phase_allclose, rel',
np.max(np.abs((a - (b - shift)) / a)))
if np.ma.isMaskedArray(a):
assert_(np.ma.isMaskedArray(b))
assert_array_equal(a.mask, b.mask)
assert_(a.fill_value == b.fill_value)
au = np.asarray(a)
bu = np.asarray(b)
with expected_warnings([r'invalid value encountered|\A\Z',
r'divide by zero encountered|\A\Z']):
print('assert_phase_allclose, no mask, abs',
np.max(np.abs(au - (bu - shift))))
print('assert_phase_allclose, no mask, rel',
np.max(np.abs((au - (bu - shift)) / au)))
assert_array_almost_equal_nulp(a + shift, b, *args, **kwargs)
def test_denoise_nl_means_2d_multichannel():
# reduce image size because nl means is slow
img = np.copy(astro[:50, :50])
img = np.concatenate((img, ) * 2, ) # 6 channels
# add some random noise
sigma = 0.1
imgn = img + sigma * np.random.standard_normal(img.shape)
imgn = np.clip(imgn, 0, 1)
for fast_mode in [True, False]:
for s in [sigma, 0]:
for n_channels in [2, 3, 6]:
psnr_noisy = compare_psnr(img[..., :n_channels],
imgn[..., :n_channels])
denoised = restoration.denoise_nl_means(imgn[..., :n_channels],
3, 5, h=0.75 * sigma,
fast_mode=fast_mode,
multichannel=True,
sigma=s)
psnr_denoised = compare_psnr(denoised[..., :n_channels],
img[..., :n_channels])
# make sure noise is reduced
assert_(psnr_denoised > psnr_noisy)
def test_denoise_nl_means_2d_multichannel():
# reduce image size because nl means is slow
img = np.copy(astro[:50, :50])
img = np.concatenate((img, ) * 2, ) # 6 channels
# add some random noise
sigma = 0.1
imgn = img + sigma * np.random.standard_normal(img.shape)
imgn = np.clip(imgn, 0, 1)
for fast_mode in [True, False]:
for s in [sigma, 0]:
for n_channels in [2, 3, 6]:
psnr_noisy = compare_psnr(img[..., :n_channels],
imgn[..., :n_channels])
denoised = restoration.denoise_nl_means(imgn[..., :n_channels],
3, 5, h=0.75 * sigma,
fast_mode=fast_mode,
multichannel=True,
sigma=s)
psnr_denoised = compare_psnr(denoised[..., :n_channels],
img[..., :n_channels])
# make sure noise is reduced
assert_(psnr_denoised > psnr_noisy)
def test_wavelet_threshold():
rstate = np.random.RandomState(1234)
img = astro_gray
sigma = 0.1
noisy = img + sigma * rstate.randn(*(img.shape))
noisy = np.clip(noisy, 0, 1)
# employ a single, user-specified threshold instead of BayesShrink sigmas
denoised = _wavelet_threshold(noisy, wavelet='db1', method=None,
threshold=sigma)
psnr_noisy = compare_psnr(img, noisy)
psnr_denoised = compare_psnr(img, denoised)
assert_(psnr_denoised > psnr_noisy)
# either method or threshold must be defined
with testing.raises(ValueError):
_wavelet_threshold(noisy, wavelet='db1', method=None, threshold=None)
# warns if a threshold is provided in a case where it would be ignored
with expected_warnings(["Thresholding method "]):
_wavelet_threshold(noisy, wavelet='db1', method='BayesShrink',
threshold=sigma)
def test_denoise_nl_means_2d(fast_mode):
img = np.zeros((40, 40))
img[10:-10, 10:-10] = 1.
sigma = 0.3
img += sigma * np.random.randn(*img.shape)
img_f32 = img.astype('float32')
for s in [sigma, 0]:
denoised = restoration.denoise_nl_means(img, 7, 5, 0.2,
fast_mode=fast_mode,
multichannel=False,
sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised.std())
denoised_f32 = restoration.denoise_nl_means(img_f32, 7, 5, 0.2,
fast_mode=fast_mode,
multichannel=False,
sigma=s)
# make sure noise is reduced
assert_(img.std() > denoised_f32.std())
# Sheck single precision result
assert np.allclose(denoised_f32, denoised, atol=1e-2)
def test_denoise_tv_chambolle_2d():
# astronaut image
img = astro_gray.copy()
# add noise to astronaut
img += 0.5 * img.std() * np.random.rand(*img.shape)
# clip noise so that it does not exceed allowed range for float images.
img = np.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])
from scipy import ndimage as ndi
grad = ndi.morphological_gradient(img, size=((3, 3)))
grad_denoised = ndi.morphological_gradient(denoised_astro, 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_unwrap_3d_middle_wrap_around():
# Segmentation fault in 3D unwrap phase with middle dimension connected
# GitHub issue #1171
image = np.zeros((20, 30, 40), dtype=np.float32)
unwrap = unwrap_phase(image, wrap_around=[False, True, False])
assert_(np.all(unwrap == 0))
def test_unwrap_2d_compressed_mask():
# ValueError when image is masked array with a compressed mask (no masked
# elments). GitHub issue #1346
image = np.ma.zeros((10, 10))
unwrap = unwrap_phase(image)
assert_(np.all(unwrap == 0))
def test_denoise_tv_chambolle_4d():
""" TV denoising for a 4D input."""
im = 255 * np.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())
def test_cycle_spinning_multichannel():
sigma = 0.1
rstate = np.random.RandomState(1234)
for multichannel in True, False:
if multichannel:
img = astro
# can either omit or be 0 along the channels axis
valid_shifts = [1, (0, 1), (1, 0), (1, 1), (1, 1, 0)]
# can either omit or be 1 on channels axis.
valid_steps = [1, 2, (1, 2), (1, 2, 1)]
# too few or too many shifts or non-zero shift on channels
invalid_shifts = [(1, 1, 2), (1, ), (1, 1, 0, 1)]
# too few or too many shifts or any shifts <= 0
invalid_steps = [(1, ), (1, 1, 1, 1), (0, 1), (-1, -1)]
else:
img = astro_gray
valid_shifts = [1, (0, 1), (1, 0), (1, 1)]
valid_steps = [1, 2, (1, 2)]
invalid_shifts = [(1, 1, 2), (1, )]
invalid_steps = [(1, ), (1, 1, 1), (0, 1), (-1, -1)]
noisy = img.copy() + 0.1 * rstate.randn(*(img.shape))
denoise_func = restoration.denoise_wavelet
func_kw = dict(sigma=sigma, multichannel=multichannel)
# max_shifts=0 is equivalent to just calling denoise_func
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
DASK_NOT_INSTALLED_WARNING]):
dn_cc = restoration.cycle_spin(noisy, denoise_func, max_shifts=0,
func_kw=func_kw,
multichannel=multichannel)
dn = denoise_func(noisy, **func_kw)
assert_equal(dn, dn_cc)
# denoising with cycle spinning will give better PSNR than without
for max_shifts in valid_shifts:
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
DASK_NOT_INSTALLED_WARNING]):
dn_cc = restoration.cycle_spin(noisy, denoise_func,
max_shifts=max_shifts,
func_kw=func_kw,
multichannel=multichannel)
assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn))
for shift_steps in valid_steps:
with expected_warnings([PYWAVELET_ND_INDEXING_WARNING,
DASK_NOT_INSTALLED_WARNING]):
dn_cc = restoration.cycle_spin(noisy, denoise_func,
max_shifts=2,
shift_steps=shift_steps,
func_kw=func_kw,
multichannel=multichannel)
assert_(compare_psnr(img, dn_cc) > compare_psnr(img, dn))
for max_shifts in invalid_shifts:
with testing.raises(ValueError):
dn_cc = restoration.cycle_spin(noisy, denoise_func,
max_shifts=max_shifts,
func_kw=func_kw,
multichannel=multichannel)
for shift_steps in invalid_steps:
with testing.raises(ValueError):
dn_cc = restoration.cycle_spin(noisy, denoise_func,
max_shifts=2,
shift_steps=shift_steps,
func_kw=func_kw,
multichannel=multichannel)
