def __init__(self, graph): Operator.__init__(self, graph=graph) self._was_executed = False self._inner_op = OpInner(self) self._inner_op.Input.connect(self.Input) self.Output.connect(self._inner_op.Output)
def __init__(self,parent): Operator.__init__(self,parent) self.source = OpArrayPiper(self) self.fixerSource = OpArrayPiper(self) self.source.inputs["Input"].connect(self.inputs["Input"]) self.fixerSource.inputs["Input"].connect(self.inputs["fixAtCurrent"])
def __init__(self, *args, **kwargs): Operator.__init__(self, *args, **kwargs) self.source = OpArrayPiper(parent=self) self.source.inputs["Input"].connect(self.inputs["Input"]) # Give our feature IDs input a default value (connected out of the box, but can be changed) self.inputs["FeatureIds"].setValue( self.DefaultFeatureIds )
def __init__(self,parent): Operator.__init__(self,parent) self.lock = threading.Lock() self._sparseNZ = None self._labelers = {} self.shape = None self.eraser = None
def __init__(self, graph, register=True): Operator.__init__(self, graph, register) self.graph = graph self.loader = OpStackLoader(self.graph) self.op5ifyer = Op5ifyer(self.graph) self.outpiper = OpArrayPiper(self.graph) self.inverter = OpGrayscaleInverter(self.graph) self.converter = OpRgbToGrayscale(self.graph)
def __init__(self, graph, register = True): Operator.__init__(self, graph, register) self.graph = graph self.loader = OpStackLoader(self.graph) self.op5ifyer = Op5ifyer(self.graph) self.outpiper = OpArrayPiper(self.graph) self.inverter = OpGrayscaleInverter(self.graph) self.converter = OpRgbToGrayscale(self.graph)
def __init__(self,parent): Operator.__init__(self, parent, register=True) self.multi = Op50ToMulti(graph=self.graph) self.stacker = OpMultiArrayStacker(graph=self.graph) self.smoother = OpGaussianSmoothing(graph=self.graph) self.destSigma = 1.0 self.windowSize = 4 self.operatorList = [OpGaussianSmoothing,OpLaplacianOfGaussian,\ OpStructureTensorEigenvalues,OpHessianOfGaussianEigenvalues,\ OpGaussianGradientMagnitude,OpDifferenceOfGaussians] self.opInstances = []
def __init__(self, *args, **kwargs): Operator.__init__(self, *args, **kwargs) self.source = OpArrayPiper(parent=self) self.source.inputs["Input"].connect(self.inputs["Input"]) self.stacker = OpMultiArrayStacker(parent=self) self.multi = Op50ToMulti(parent=self) self.stacker.inputs["Images"].connect(self.multi.outputs["Outputs"]) # Give our feature IDs input a default value (connected out of the box, but can be changed) self.inputs["FeatureIds"].setValue( self.DefaultFeatureIds )
def __init__(self, voluminaData, graph=None, parent=None): """ voluminaData - An array in txyzc order. """ Operator.__init__(self, graph=graph, parent=parent) # We store the data in a custom order self._data = voluminaData.transpose([0,3,2,1,4]) oslot = self.outputs["Data"] oslot.meta.shape = self._data.shape oslot.meta.dtype = self._data.dtype oslot.meta.axistags = vigra.defaultAxistags('tzyxc') # Non-volumina ordering: datasource will re-order self.inputs["Changedata"].meta.axistags = oslot.meta.axistags
def __init__(self, voluminaData, graph=None, parent=None): """ voluminaData - An array in txyzc order. """ Operator.__init__(self, graph=graph, parent=parent) # We store the data in a custom order self._data = voluminaData.transpose([0, 3, 2, 1, 4]) oslot = self.outputs["Data"] oslot.meta.shape = self._data.shape oslot.meta.dtype = self._data.dtype oslot.meta.axistags = vigra.defaultAxistags("tzyxc") # Non-volumina ordering: datasource will re-order self.inputs["Changedata"].meta.axistags = oslot.meta.axistags
def __init__(self, *args, **kwargs): Operator.__init__(self, *args, **kwargs) self._propagate_dirty = False
def __init__(self, *args, **kwargs): Operator.__init__(self, *args, **kwargs) self.source = OpArrayPiper(parent=self) self.source.Input.connect(self.Input)
def __init__(self, parent=None, graph=None): Operator.__init__(self,parent,graph)
def __init__(self, g, fn): Operator.__init__(self, g) self._data = np.load(fn) oslot = self.outputs["Data5D"] oslot.meta.shape = self._data.shape oslot.meta.dtype = self._data.dtype
def __init__(self, parent = None): Operator.__init__(self, parent) self._forest_count = 4 # TODO: Make treecount configurable via an InputSlot self._tree_count = 25
def __init__(self, parent=None, graph=None): Operator.__init__(self, parent=parent, graph=graph) self._configured = False
def __init__(self, g, fn): Operator.__init__(self,g) self._data = np.load(fn) oslot = self.outputs["Data5D"] oslot._shape = self._data.shape oslot._dtype = self._data.dtype
def __init__(self, parent=None, graph=None): Operator.__init__(self,parent=parent, graph=graph) self._configured = False
def __init__(self, parent=None, graph=None): Operator.__init__(self, parent, graph)
def __init__(self, parent): Operator.__init__(self, parent) self._lock = Lock() self._innerOps = []
def __init__(self, parent=None, graph=None): Operator.__init__(self, parent, graph) self.internalOp = OpB(self) self.internalOp.Input.connect(self.Input) self.inputBackup = self.Input
def __init__(self, parent): self.lock = threading.Lock() self._denseArray = None self._sparseNZ = None self._oldShape = (0,) Operator.__init__(self,parent)
def __init__(self, parent): Operator.__init__(self, parent)
def __init__(self,parent): Operator.__init__(self, parent)
def __init__(self, g, data): Operator.__init__(self,g) self._data = data oslot = self.outputs["Data"] oslot._shape = self._data.shape oslot._dtype = self._data.dtype