def filter(self,
stack: ImageStack,
in_place: bool = True) -> Optional[ImageStack]:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
Returns
-------
Optional[ImageStack] :
if in-place is False, return the results of filter as a new stack
"""
high_pass: Callable = partial(self.high_pass, sigma=self.sigma)
result = stack.apply(high_pass,
is_volume=self.is_volume,
verbose=self.verbose,
in_place=in_place)
if not in_place:
return result
return None
def filter(self,
stack: ImageStack,
in_place: bool = True) -> Optional[ImageStack]:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
Returns
-------
Optional[ImageStack] :
if in-place is False, return the results of filter as a new stack
"""
func: Callable = partial(self.richardson_lucy_deconv,
num_iter=self.num_iter,
psf=self.psf,
clip=self.clip)
result = stack.apply(func, in_place=in_place, verbose=self.verbose)
if not in_place:
return result
return None
def filter(self,
stack: ImageStack,
in_place: bool = True) -> Optional[ImageStack]:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
Returns
-------
Optional[ImageStack] :
if in-place is False, return the results of filter as a new stack
"""
clip = partial(self.clip, p_min=self.p_min, p_max=self.p_max)
result = stack.apply(clip,
is_volume=self.is_volume,
verbose=self.verbose,
in_place=in_place)
if not in_place:
return result
return None
def find(self, stack: ImageStack):
"""
Find spots.
Parameters
----------
stack : ImageStack
Stack where we find the spots in.
"""
spot_attributes: List[SpotAttributes] = stack.apply(
self.find_attributes,
in_place=False,
is_volume=self.is_volume,
verbose=self.verbose)
# TODO ambrosejcarr: do we need to find spots in the aux_dict too?
# TODO ambrosejcarr: this is where development stopped; spot_attributes is correct, but translating
# spot_attributes into an encoder_table is tricky without first implementing the new codebook. Do that first.
# create an encoded table
# encoded_spots = self.encode(spot_attributes.data)
return spot_attributes
def filter(self, stack: ImageStack, in_place: bool=True) -> Optional[ImageStack]:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
Returns
-------
Optional[ImageStack] :
if in-place is False, return the results of filter as a new stack
"""
bandpass_ = partial(
self.bandpass, lshort=self.lshort, llong=self.llong, threshold=self.threshold, truncate=self.truncate
)
result = stack.apply(bandpass_, verbose=self.verbose, in_place=in_place)
if not in_place:
return result
return None
def filter(self,
stack: ImageStack,
in_place: bool = True) -> Optional[ImageStack]:
"""Perform filtering of an image stack
Parameters
----------
stack : ImageStack
Stack to be filtered.
in_place : bool
if True, process ImageStack in-place, otherwise return a new stack
Returns
-------
Optional[ImageStack] :
if in-place is False, return the results of filter as a new stack
"""
from scipy.ndimage.filters import maximum_filter, minimum_filter
from skimage.morphology import disk
def white_tophat(image):
if image.dtype.kind != "u":
raise TypeError(
"images should be stored in an unsigned integer array")
structuring_element = disk(self.disk_size)
min_filtered = minimum_filter(image, footprint=structuring_element)
max_filtered = maximum_filter(min_filtered,
footprint=structuring_element)
filtered_image = image - np.minimum(image, max_filtered)
return filtered_image
result = stack.apply(white_tophat)
if not in_place:
return result
return None