def setup(self):
self.image = np.random.random((200, 200, 100))
self.image[:100, :100, :] += 1
self.image[150:, 150:, :] += 0.5
self.msk = np.zeros((200, 200, 100))
self.msk[10:-10, 10:-10, 10:-10] = 1
self.msk_slice = self.msk[..., 50]
if Version(skimage.__version__) >= Version('0.17.0'):
self.slic_kwargs = dict(start_label=1)
else:
self.slic_kwargs = {}
def setup(self, *args):
try:
# use a separate skeletonize_3d function on older scikit-image
if Version(skimage.__version__) < Version('0.16.0'):
self.skeletonize = morphology.skeletonize_3d
else:
self.skeletonize = morphology.skeletonize
except AttributeError:
raise NotImplementedError("3d skeletonize unavailable")
# we stack the horse data 5 times to get an example volume
self.image = np.stack(5 * [util.invert(data.horse())])
def setup(self):
try:
mask = np.zeros((64, 64)) > 0
mask[10:-10, 10:-10] = 1
segmentation.slic(np.ones_like(mask), mask=mask)
except TypeError:
raise NotImplementedError("masked slic unavailable")
self.image = np.random.random((200, 200, 100))
self.image[:100, :100, :] += 1
self.image[150:, 150:, :] += 0.5
self.msk = np.zeros((200, 200, 100))
self.msk[10:-10, 10:-10, 10:-10] = 1
self.msk_slice = self.msk[..., 50]
if Version(skimage.__version__) >= Version('0.17.0'):
self.slic_kwargs = dict(start_label=1)
else:
self.slic_kwargs = {}
from functools import partial
from multiprocessing import Pool
import numpy as np
from numpy.lib import NumpyVersion as Version
import scipy
if Version(scipy.__version__) >= Version('1.4.0'):
import scipy.fft
_fft = scipy.fft
else:
import scipy.fftpack
_fft = scipy.fftpack
def _image_tv(x, axis=0, n_points=3):
""" Computes total variation (TV) of matrix x across a given axis and
along two directions.
Parameters
----------
x : 2D ndarray
matrix x
axis : int (0 or 1)
Axis which TV will be calculated. Default a is set to 0.
n_points : int
Number of points to be included in TV calculation.
Returns
]
_scipy_150 = False
try:
import scipy
import scipy.fft as _scipy_fft
except ImportError:
class _DummyModule:
def __getattr__(self, name):
return None
_scipy_fft = _DummyModule()
else:
from numpy.lib import NumpyVersion as Version
_scipy_150 = Version(scipy.__version__) >= Version('1.5.0')
del Version
del scipy
# Backend support for scipy.fft
__ua_domain__ = 'numpy.scipy.fft'
_implemented = {}
def __ua_convert__(dispatchables, coerce):
if coerce:
try:
replaced = [
cupy.asarray(d.value)
if d.coercible and d.type is np.ndarray else d.value