def voronoi_expand(labels, return_line=False):
dist = distance_transform_edt(labels)
vor = skiwatershed(-dist, markers=labels)
if not return_line:
return vor
else:
mask = skiwatershed(-dist, markers=labels, watershed_line=True)
lines = mask == 0
return vor, lines
def label_watershed(labels, regmax):
# Since there are non-unique values for dist, add very small numbers. This will separate each marker by regmax at least.
dist = distance_transform_edt(labels) + np.random.rand(*labels.shape)*1e-10
labeled_maxima = label(peak_local_max(dist, min_distance=int(regmax), indices=False))
wshed = -dist
wshed = wshed - np.min(dist)
markers = np.zeros(wshed.shape, np.int16)
markers[labeled_maxima > 0] = -labeled_maxima[labeled_maxima > 0]
wlabel = skiwatershed(wshed, markers, connectivity=np.ones((3,3), bool), mask=labels!=0)
wlabel = -wlabel
wlabel = labels.max() + wlabel
wlabel[wlabel == labels.max()] = 0
all_label = label(labels + wlabel)
return all_label
