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, 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, *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, 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.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 _after_init(self):
"""
Overridden from Operator.
"""
Operator._after_init(self)
# When a new image is added to the workflow, each applet should get a new lane.
self.imageNameListSlot.notifyInserted( self._createNewImageLane )
self.imageNameListSlot.notifyRemove( self._removeImageLane )
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 _after_init(self):
"""
Overridden from Operator.
"""
Operator._after_init(self)
# When a new image is added to the workflow, each applet should get a new lane.
self.imageNameListSlot.notifyInserted(self._createNewImageLane)
self.imageNameListSlot.notifyRemove(self._removeImageLane)
def _after_init(self):
"""
Overridden from Operator.
"""
Operator._after_init(self)
# When a new image is added to the workflow, each applet should get a new lane.
self.imageNameListSlot.notifyInserted( self._createNewImageLane )
self.imageNameListSlot.notifyRemove( self._removeImageLane )
for applet in self.applets:
applet.appletStateUpdateRequested.connect( self.handleAppletStateUpdateRequested )
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 _after_init(self):
"""
Overridden from Operator.
"""
Operator._after_init(self)
# When a new image is added to the workflow, each applet should get a new lane.
self.imageNameListSlot.notifyInserted(self._createNewImageLane)
self.imageNameListSlot.notifyRemove(self._removeImageLane)
for applet in self.applets:
applet.appletStateUpdateRequested.subscribe(self.handleAppletStateUpdateRequested)
applet.sendMessageToServer.subscribe(self.handleSendMessageToServer)
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, 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, graph)
self.internalOp = OpB(self)
self.internalOp.Input.connect(self.Input)
self.inputBackup = self.Input
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, *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=parent, graph=graph)
self._configured = False
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
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):
Operator.__init__(self, parent)
self._lock = Lock()
self._innerOps = []
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, *args, **kwargs):
Operator.__init__(self, *args, **kwargs)
self._propagate_dirty = False
def __init__(self, parent=None, graph=None):
Operator.__init__(self, parent, graph)
def __init__(self,parent):
Operator.__init__(self, parent)
def __init__(self, parent=None, graph=None):
Operator.__init__(self, parent=parent, graph=graph)
self._configured = False
def __init__(self, parent):
Operator.__init__(self, parent)
def __init__(self, parent):
self.lock = threading.Lock()
self._denseArray = None
self._sparseNZ = None
self._oldShape = (0,)
Operator.__init__(self,parent)
