def process2D(data,xM=None,yM=None):
if len(data.shape) > 2:
_ ,data = reg.correct_jitter(data)
alllabels = seg.watershed_segment(data,xM,yM)
ROIs = find_points(alllabels)
masks = np.ones((len(ROIs),data.shape[0],data.shape[1]))
for i,roi in enumerate(ROIs):
masks[i] = create_bitmask(roi,data)
return masks,[],data
def process3D(data,xM=None,yM=None):
"""used to apply watershed to 3D or even 2D data"""
#print 'Would you like to upsample by a factor of two? This will take longer, but might improve the result. (y/n)'
#yn = raw_input('> ')
allmasks,alllabels = [],[]
for i in range(len(data)):
#if yn == 'y':
# data[i] = ndi.interpolation.zoom(data[i],2)
alllabels.append(seg.watershed_segment(data[i],xM,yM))
ROIs = find_points(alllabels[i])
data = np.array(data)
masks = np.ones((len(ROIs),data.shape[1],data.shape[2]))
for j in range(len(ROIs)):
masks[j] = create_bitmask(ROIs[j],data[i])
allmasks.append(masks)
allmasks,prinROI = deleteMultiDetection(allmasks,data)
return allmasks,prinROI
