removed 'bugfix' from snow for now, while we figure out the best soln
@@ -296,8 +296,10 @@ def snow_partitioning(im, dt=None, r_max=4, sigma=0.4, return_all=False,
mask_solid = None
regions = watershed(image=-dt, markers=peaks, mask=mask_solid)
# Catch any isolated regions that were missed
- labels = spim.label((regions == 0)*(im > 0))[0]
- regions += (labels + regions.max())*(labels > 0)
+ # TODO: I'm not sure if this approach is universal so I'm going to comment it
+ # out for now, and mark it as a todo
+ # labels = spim.label((regions == 0)*(im > 0))[0]
+ # regions += (labels + regions.max())*(labels > 0)
if randomize:
regions = randomize_colors(regions)
if return_all:
