def _wf(data, params, cores, chunksize):
tomopy = importer.do_importing('tomopy')
# creating a dictionary with all possible params for this func
kwargs = dict(level=None,
wname=u'db5',
sigma=2,
pad=True,
ncore=cores,
nchunk=chunksize)
# process the input parameters
params = _get_params(params)
# dict.get returns a None if the keyword arg is not found
# this means if the user hasn't passed anything that matches the string
# then the default is used
kwargs['level'] = int(
params.get('level')) if params.get('level') else kwargs['level']
kwargs['wname'] = str(
params.get('wname')) if params.get('wname') else kwargs['wname']
kwargs['sigma'] = int(
params.get('sigma')) if params.get('sigma') else kwargs['sigma']
kwargs['pad'] = bool(
params.get('pad')) if params.get('pad') else kwargs['pad']
return tomopy.prep.stripe.remove_stripe_fw(data, **kwargs)
def filter_func(images: Images, minus_log=True, progress=None):
"""
This filter should be used on transmission images (background corrected
images).
It converts the images from transmission to attenuation.
:param images: Sample data which is to be processed. Expected in radiograms
:param minus_log: Specify whether to calculate minus log or just return.
:return: Inverted image
"""
progress = Progress.ensure_instance(progress, task_name='Minus Log')
if minus_log:
# import early to check if tomopy is available
tomopy = importer.do_importing('tomopy')
with progress:
progress.update(msg="Calculating -log on the sample data")
sample = images.data
# this check prevents division by 0 errors from the minus_log
sample[sample == 0] = 1e-6
# the operation is done in place
tomopy.prep.normalize.minus_log(sample, out=sample)
return images
def filter_func(data: Images,
circular_mask_ratio=0.95,
circular_mask_value=0.,
cores=None,
progress=None) -> Images:
"""
:param data: Input data as a 3D numpy.ndarray
:param circular_mask_ratio: The ratio to the full image.
The ratio must be 0 < ratio < 1
:param circular_mask_value: The value that all pixels in the mask
will be set to.
:return: The processed 3D numpy.ndarray
"""
progress = Progress.ensure_instance(progress,
num_steps=1,
task_name='Circular Mask')
if circular_mask_ratio and 0 < circular_mask_ratio < 1:
tomopy = importer.do_importing('tomopy')
with progress:
progress.update(msg="Applying circular mask")
# for some reason this doesn't like the ncore param, even though
# it's in the official tomopy docs
tomopy.circ_mask(arr=data.data,
axis=0,
ratio=circular_mask_ratio,
val=circular_mask_value,
ncore=cores)
return data
def filter_func(data,
diff=None,
radius=_default_radius,
mode=_default_mode,
axis=0,
type=_default_dim,
cores=None,
progress=None):
"""
Requires tomopy to be available.
:param data: Input data as a 3D numpy.ndarray
:param diff: Pixel value difference above which to crop bright pixels
:param radius: Size of the median filter to apply
:param mode: Spot brightness to remove.
Either 'bright' or 'dark'.
:param cores: The number of cores that will be used to process the data.
:return: The processed 3D numpy.ndarray
"""
progress = Progress.ensure_instance(progress,
task_name='Outliers',
num_steps=3)
if not utility.multiprocessing_necessary(data.data.shape, cores):
cores = 1
if diff and radius and diff > 0 and radius > 0:
with progress:
progress.update(
msg="Applying outliers with threshold: {0} and "
"radius {1}".format(diff, radius))
sample = data.data
# By default tomopy only clears bright outliers.
# As a workaround inverting the image makes the dark outliers the brightest
if mode == OUTLIERS_DARK:
np.negative(sample, out=sample)
tomopy = importer.do_importing('tomopy')
if type == "2D":
tomopy.misc.corr.remove_outlier(sample,
diff,
radius,
axis,
ncore=cores,
out=sample)
else:
tomopy.misc.corr.remove_outlier1d(sample,
diff,
radius,
axis,
ncore=cores,
out=sample)
progress.update()
# reverse the inversion
if mode == OUTLIERS_DARK:
np.negative(sample, out=sample)
return data
def _sf(data, params, cores, chunksize):
tomopy = importer.do_importing('tomopy')
# creating a dictionary with all possible params for this func
kwargs = dict(size=5, ncore=cores, nchunk=chunksize)
# process the input parameters
params = _get_params(params)
# dict.get returns a None if the keyword arg is not found
# this means if the user hasn't passed anything that matches the string
# then the default is used
kwargs['size'] = int(params.get('size')) if params.get('size') else kwargs['size']
return tomopy.prep.stripe.remove_stripe_sf(data, **kwargs)