def __init__(self, carvingGraphFilename, *args, **kwargs):
super(OpCarving, self).__init__(*args, **kwargs)
print "[Carving id=%d] CONSTRUCTOR" % id(self)
self._mst = MSTSegmentor.loadH5(carvingGraphFilename, "graph")
#supervoxels of finished and saved objects
self._done_lut = None
self._done_seg_lut = None
self._setCurrObjectName("")
self.HasSegmentation.setValue(False)
def propagateDirty(self, slot, subindex, roi):
key = roi.toSlice()
if slot == self.Trigger or slot == self.BackgroundPriority or slot == self.NoBiasBelow:
if self._mst is None:
return
if not self.BackgroundPriority.ready():
return
if not self.NoBiasBelow.ready():
return
bgPrio = self.BackgroundPriority.value
noBiasBelow = self.NoBiasBelow.value
print "compute new carving results with bg priority = %f, no bias below %d" % (bgPrio, noBiasBelow)
labelCount = 2
params = dict()
params["prios"] = [1.0, bgPrio, 1.0]
params["uncertainty"] = "none"
params["noBiasBelow"] = noBiasBelow
unaries = numpy.zeros((self._mst.numNodes,labelCount+1)).astype(numpy.float32)
#assert numpy.sum(self._mst.seeds > 2) == 0, "seeds > 2 at %r" % numpy.where(self._mst.seeds > 2)
self._mst.run(unaries, **params)
self.Segmentation.setDirty(slice(None))
self.HasSegmentation.setValue(True)
elif slot == self.CarvingGraphFile:
if self._mst is not None:
#if the carving graph file is not valid, all outputs must be invalid
for output in self.outputs.values():
output.setDirty(slice(0,None))
fname = self.CarvingGraphFile.value
self._mst = MSTSegmentor.loadH5(fname, "graph")
print "[Carving id=%d] loading graph file %s (mst=%d)" % (id(self), fname, id(self._mst))
self.Segmentation.setDirty(slice(None))
else:
super(OpCarving, self).notifyDirty(slot, key)
