def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1, 100, 100, 10, 1)
op.inputs["shape"].setValue(arrayshape)
blockshape = (1, 10, 10, 10, 1
) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue(blockshape)
op.eraser.setValue(100)
dummyData = vigra.VigraArray(arrayshape,
axistags=vigra.defaultAxistags("txyzc"),
dtype=numpy.uint8)
op.Input.setValue(dummyData)
slicing = numpy.s_[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = (3 * numpy.random.random(inDataShape)).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
# Sanity check...
assert (op.Output[:].wait()[slicing] == data[slicing]).all()
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.inputs["shape"].setValue( arrayshape )
blockshape = (1,10,10,10,1) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue( blockshape )
op.eraser.setValue(100)
op.Input.meta.axistags = vigra.defaultAxistags('txyzc')
op.Input.meta.has_mask = True
dummyData = numpy.zeros(arrayshape, dtype=numpy.uint8)
dummyData = numpy.ma.masked_array(dummyData, mask=numpy.ma.getmaskarray(dummyData), fill_value=numpy.uint8(0), shrink=False)
op.Input.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
inputData = numpy.ma.masked_array(inputData, mask=numpy.ma.getmaskarray(inputData), fill_value=numpy.uint8(0), shrink=False)
inputData[:, 0] = numpy.ma.masked
op.Input[slicing] = inputData
data = numpy.ma.zeros(arrayshape, dtype=numpy.uint8, fill_value=numpy.uint8(0))
data[slicing] = inputData
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def setup(self):
graph = Graph()
op = OpCompressedUserLabelArray(graph=graph)
arrayshape = (1,100,100,10,1)
op.inputs["shape"].setValue( arrayshape )
blockshape = (1,10,10,10,1) # Why doesn't this work if blockshape is an ndarray?
op.inputs["blockShape"].setValue( blockshape )
op.eraser.setValue(100)
dummyData = vigra.VigraArray(arrayshape, axistags=vigra.defaultAxistags('txyzc'))
op.Input.setValue( dummyData )
slicing = sl[0:1, 1:15, 2:36, 3:7, 0:1]
inDataShape = slicing2shape(slicing)
inputData = ( 3*numpy.random.random(inDataShape) ).astype(numpy.uint8)
op.Input[slicing] = inputData
data = numpy.zeros(arrayshape, dtype=numpy.uint8)
data[slicing] = inputData
# Sanity check...
assert (op.Output[:].wait()[slicing] == data[slicing]).all()
self.op = op
self.slicing = slicing
self.inData = inputData
self.data = data
def __init__(self, *args, **kwargs):
super(OpLabelPipeline, self).__init__(*args, **kwargs)
self.opLabelArray = OpCompressedUserLabelArray(parent=self)
self.opLabelArray.Input.connect(self.LabelInput)
self.opLabelArray.eraser.setValue(100)
self.opBoxArray = OpCompressedUserLabelArray(parent=self)
self.opBoxArray.Input.connect(self.BoxLabelInput)
self.opBoxArray.eraser.setValue(100)
self.opLabelArray.deleteLabel.connect(self.DeleteLabel)
# Connect external outputs to their internal sources
self.Output.connect(self.opLabelArray.Output)
self.nonzeroBlocks.connect(self.opLabelArray.nonzeroBlocks)
# self.MaxLabel.connect( self.opLabelArray.MaxLabelValue )
self.BoxOutput.connect(self.opBoxArray.Output)
def __init__(self, blockDims=None, *args, **kwargs):
"""
Instantiate all internal operators and connect them together.
"""
super(OpLabelingSingleLane, self).__init__(*args, **kwargs)
# Configuration options
if blockDims is None:
blockDims = {'t': 1, 'x': 100, 'y': 100, 'z': 100, 'c': 1}
assert isinstance(blockDims, dict)
self._blockDims = blockDims
# Create internal operator
self.opLabelArray = OpCompressedUserLabelArray(parent=self)
self.opLabelArray.Input.connect(self.LabelInput)
self.opLabelArray.eraser.connect(self.LabelEraserValue)
self.opLabelArray.deleteLabel.connect(self.LabelDelete)
# Connect our internal outputs to our external outputs
self.LabelImage.connect(self.opLabelArray.Output)
self.NonzeroLabelBlocks.connect(self.opLabelArray.nonzeroBlocks)
self.LabelNames.setValue(['Negative', 'Positive'])
self.LabelColors.setValue([])
def __init__(self, blockDims=None, *args, **kwargs):
"""
Instantiate all internal operators and connect them together.
"""
super(OpCroppingSingleLane, self).__init__(*args, **kwargs)
# Configuration options
if blockDims is None:
blockDims = {"t": 1, "x": 100, "y": 100, "z": 100, "c": 1}
assert isinstance(blockDims, dict)
self._blockDims = blockDims
# Create internal operator
self.opCropArray = OpCompressedUserLabelArray(parent=self)
self.opCropArray.Input.connect(self.CropInput)
self.opCropArray.eraser.connect(self.CropEraserValue)
self.opCropArray.deleteCrop.connect(self.CropDelete)
# Connect our internal outputs to our external outputs
self.CropImage.connect(self.opCropArray.Output)
self.NonzeroCropBlocks.connect(self.opCropArray.nonzeroBlocks)
self.CropNames.setValue([])
self.CropColors.setValue([])
