def serialize(self, h5g, destbegin = (0,0,0), destend = (0,0,0), srcbegin = (0,0,0), srcend = (0,0,0), destshape = (0,0,0) ):
#for now save the labels and prediction in the old format
#TODO: change that when we are certain about the new project file format
vl = VolumeLabels(self.dataItemImage.overlayMgr["Classification/Labels"]._data)
vl.descriptions = self.classificationModuleMgr.dataMgr.module["Classification"]["labelDescriptions"]
vl.serialize(h5g, "labels", destbegin, destend, srcbegin, srcend, destshape)
if len(vl.descriptions) > 0:
prediction = numpy.zeros(self.dataItemImage.shape[0:-1] + (len(vl.descriptions),), 'float32')
for d in vl.descriptions:
if self.dataItemImage.overlayMgr["Classification/Prediction/" + d.name] is not None:
prediction[:,:,:,:,d.number-1] = self.dataItemImage.overlayMgr["Classification/Prediction/" + d.name][:,:,:,:,0]
prediction = DataAccessor(prediction)
prediction.serialize(h5g, 'prediction', destbegin, destend, srcbegin, srcend, destshape )
def deserialize(self, h5G, offsets = (0,0,0), shape = (0,0,0)):
labels = VolumeLabels.deserialize(h5G, "labels",offsets, shape)
self["labels"] = labels
if 'prediction' in h5G.keys():
self["prediction"] = DataAccessor.deserialize(h5G, 'prediction', offsets, shape)
def onAppend(self):
if self.objects is None:
l = numpy.zeros(self.dataItemImage.shape[0:-1] + (1, ), 'uint8')
self.objects = VolumeLabels(l)
def onAppend(self):
if self.background is None:
l = numpy.zeros(self.dataItemImage.shape[0:-1], 'uint8')
self.background = VolumeLabels(l)