def from_labels(self, bgout=None, exclude=None, binary=True, pbinary=True,
inplace=False, neighbors=4, **pmangerkwds):
""" Get morphological labels from gray or binary image.
Parameters
----------
exclude : 0, 1, 255, 'w', 'b'
Exclude white, black or specified integer from labels. For example,
'b' will prevent black pixels from being labeled.
bgout : Valid canvas array/color
Background image of resulting canvas. If exclude, then there will
be unlabled regions. bgout='r' will overlay the labels onto a red
background. By default, self.background is used.
binary : bool
Use binary image; else use grayimage.
pbinary : bool
Use self.pbinary to generate thresholded image; else, use
self.threshfcn (implicit thresholding function) to binarize.
Only valid if binary = True
Notes
-----
Use binary=False with caution. Many grayimages would lead to tends of
thousands of labels due to minute color changes in each pixel. Whitle
skimage.label can handle this, pyparty will slow down severaly trying
to make so many particles from labels.
"""
if binary:
if pbinary:
image = self.pbinary #PBINARY NOT self.binaryimage
if len(self.particles) == 0:
logger.warn('from_labels() recieved "pbinary=True", but '
'no particles are stored. Use "pbinary=False" to '
'use implicit thresholding function.')
else:
image = self.binaryimage
else:
image = self.grayimage
logger.warn('Labels from grayimage can be very slow (fix coming)')
if exclude is None: # scikit api doesn't accept None
labels = morphology.label(image, neighbors)
else:
# Parse various cases
if exclude == 'w' or exclude == 'white':
if binary:
exclude = 1
else:
exclude = 255
elif exclude == 'b' or exclude == 'black':
exclude = 0
labels = morphology.label(image, neighbors, background=exclude)
pout = ParticleManager.from_labels(labels, **pmangerkwds)
if inplace:
self._particles = pout
else:
cout = Canvas.copy(self)
cout.particles = pout
if bgout is not None:
cout.background = bgout
return cout
