def test_denoise_wavelet_biorthogonal():
"""Biorthogonal wavelets should raise a warning during thresholding."""
img = astro_gray
assert_warns(UserWarning,
restoration.denoise_wavelet,
img,
wavelet='bior2.2',
multichannel=False)
def test_denoise_wavelet_biorthogonal(rescale_sigma):
"""Biorthogonal wavelets should raise a warning during thresholding."""
img = astro_gray
assert_warns(UserWarning,
restoration.denoise_wavelet,
img,
wavelet='bior2.2',
channel_axis=None,
rescale_sigma=rescale_sigma)
def test_denoise_bilateral_nan():
img = np.full((50, 50), np.NaN)
out = assert_warns(RuntimeWarning,
restoration.denoise_bilateral,
img,
multichannel=False)
assert_equal(img, out)
def test_few_points():
image = np.array(
[[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0]],
dtype=np.uint8)
image3d = np.stack([image, image, image])
with testing.assert_warns(UserWarning):
chimage3d = convex_hull_image(image3d)
assert_array_equal(chimage3d, np.zeros(image3d.shape, dtype=bool))
def test_estimate_sigma_color():
rstate = np.random.RandomState(1234)
# astronaut image
img = astro.copy()
sigma = 0.1
# add noise to astronaut
img += sigma * rstate.standard_normal(img.shape)
sigma_est = restoration.estimate_sigma(img, multichannel=True,
average_sigmas=True)
assert_almost_equal(sigma, sigma_est, decimal=2)
sigma_list = restoration.estimate_sigma(img, multichannel=True,
average_sigmas=False)
assert_equal(len(sigma_list), img.shape[-1])
assert_almost_equal(sigma_list[0], sigma_est, decimal=2)
# default multichannel=False should raise a warning about last axis size
assert_warns(UserWarning, restoration.estimate_sigma, img)
def test_estimate_sigma_color():
rstate = np.random.RandomState(1234)
# astronaut image
img = astro.copy()
sigma = 0.1
# add noise to astronaut
img += sigma * rstate.standard_normal(img.shape)
sigma_est = restoration.estimate_sigma(img, multichannel=True,
average_sigmas=True)
assert_almost_equal(sigma, sigma_est, decimal=2)
sigma_list = restoration.estimate_sigma(img, multichannel=True,
average_sigmas=False)
assert_equal(len(sigma_list), img.shape[-1])
assert_almost_equal(sigma_list[0], sigma_est, decimal=2)
# default multichannel=False should raise a warning about last axis size
assert_warns(UserWarning, restoration.estimate_sigma, img)
def test_hessian_matrix():
square = np.zeros((5, 5))
square[2, 2] = 4
Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc')
assert_almost_equal(
Hrr,
np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [2, 0, -2, 0, 2],
[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]))
assert_almost_equal(
Hrc,
np.array([[0, 0, 0, 0, 0], [0, 1, 0, -1, 0], [0, 0, 0, 0, 0],
[0, -1, 0, 1, 0], [0, 0, 0, 0, 0]]))
assert_almost_equal(
Hcc,
np.array([[0, 0, 2, 0, 0], [0, 0, 0, 0, 0], [0, 0, -2, 0, 0],
[0, 0, 0, 0, 0], [0, 0, 2, 0, 0]]))
matrix2d = np.random.rand(3, 3)
assert_warns(UserWarning, hessian_matrix, matrix2d, sigma=0.1)
def test_3D():
grey_img = np.zeros((10, 10))
rgb_img = np.zeros((10, 10, 3))
three_d_img = np.zeros((10, 10, 10))
with assert_no_warnings():
felzenszwalb(grey_img, multichannel=True)
felzenszwalb(grey_img, multichannel=False)
felzenszwalb(rgb_img, multichannel=True)
with assert_warns(RuntimeWarning):
felzenszwalb(three_d_img, multichannel=True)
with testing.raises(ValueError):
felzenszwalb(rgb_img, multichannel=False)
felzenszwalb(three_d_img, multichannel=False)
def test_estimate_sigma_color(channel_axis):
rstate = np.random.RandomState(1234)
# astronaut image
img = astro.copy()
sigma = 0.1
# add noise to astronaut
img += sigma * rstate.standard_normal(img.shape)
img = np.moveaxis(img, -1, channel_axis)
sigma_est = restoration.estimate_sigma(img,
channel_axis=channel_axis,
average_sigmas=True)
assert_almost_equal(sigma, sigma_est, decimal=2)
sigma_list = restoration.estimate_sigma(img,
channel_axis=channel_axis,
average_sigmas=False)
assert_equal(len(sigma_list), img.shape[channel_axis])
assert_almost_equal(sigma_list[0], sigma_est, decimal=2)
if channel_axis % img.ndim == 2:
# default channel_axis=None should raise a warning about last axis size
assert_warns(UserWarning, restoration.estimate_sigma, img)
def test_hessian_matrix():
square = np.zeros((5, 5))
square[2, 2] = 4
Hrr, Hrc, Hcc = hessian_matrix(square, sigma=0.1, order='rc')
assert_almost_equal(Hrr, np.array([[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0],
[2, 0, -2, 0, 2],
[0, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]))
assert_almost_equal(Hrc, np.array([[0, 0, 0, 0, 0],
[0, 1, 0, -1, 0],
[0, 0, 0, 0, 0],
[0, -1, 0, 1, 0],
[0, 0, 0, 0, 0]]))
assert_almost_equal(Hcc, np.array([[0, 0, 2, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, -2, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 0, 0]]))
matrix2d = np.random.rand(3, 3)
assert_warns(UserWarning, hessian_matrix, matrix2d, sigma=0.1)
def test_3D(channel_axis):
grey_img = np.zeros((10, 10))
rgb_img = np.zeros((10, 10, 3))
three_d_img = np.zeros((10, 10, 10))
rgb_img = np.moveaxis(rgb_img, -1, channel_axis)
with assert_no_warnings():
felzenszwalb(grey_img, channel_axis=-1)
felzenszwalb(grey_img, channel_axis=None)
felzenszwalb(rgb_img, channel_axis=channel_axis)
with assert_warns(RuntimeWarning):
felzenszwalb(three_d_img, channel_axis=channel_axis)
with testing.raises(ValueError):
felzenszwalb(rgb_img, channel_axis=None)
felzenszwalb(three_d_img, channel_axis=None)
def test_wavelet_rescale_sigma_deprecation():
# No specifying rescale_sigma results in a DeprecationWarning
assert_warns(FutureWarning, restoration.denoise_wavelet, np.ones(16))
def test_negative_intensity():
labels = np.arange(100).reshape(10, 10)
image = np.full((10, 10), -1, dtype='float64')
assert_warns(UserWarning, label2rgb, labels, image, bg_label=-1)
def test_denoise_wavelet_biorthogonal():
"""Biorthogonal wavelets should raise a warning during thresholding."""
img = astro_gray
assert_warns(UserWarning, restoration.denoise_wavelet, img,
wavelet='bior2.2', multichannel=False)
def test_views_non_contiguous():
A = np.arange(16).reshape((4, 4))
A = A[::2, :]
assert_warns(RuntimeWarning, view_as_blocks, A, (2, 2))
assert_warns(RuntimeWarning, view_as_windows, A, (2, 2))
def test_views_non_contiguous():
A = np.arange(16).reshape((4, 4))
A = A[::2, :]
assert_warns(RuntimeWarning, view_as_blocks, A, (2, 2))
assert_warns(RuntimeWarning, view_as_windows, A, (2, 2))
def test_multichannel_warnings():
img = data.astronaut()
assert_warns(UserWarning, restoration.denoise_bilateral, img)
assert_warns(UserWarning, restoration.denoise_nl_means, img)
def test_negative_intensity():
labels = np.arange(100).reshape(10, 10)
image = -1 * np.ones((10, 10))
assert_warns(UserWarning, label2rgb, labels, image)
