def __init__(self, operator, projectFileGroupName):
self.VERSION = 1
self._serialClassifierSlot = SerialClassifierSlot(
operator.Classifier,
operator.classifier_cache,
name="ClassifierForests")
slots = [
SerialListSlot(operator.LabelNames),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialPickleableSlot(operator.Bookmarks, self.VERSION),
BackwardsCompatibleLabelSerialBlockSlot(
operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name="LabelSets",
subname="labels{:03d}",
selfdepends=False,
shrink_to_bb=True,
),
SerialClassifierFactorySlot(operator.ClassifierFactory),
self._serialClassifierSlot,
]
super(PixelClassificationSerializer,
self).__init__(projectFileGroupName, slots, operator)
def __init__(self, operator, projectFileGroupName):
self.predictionSlot = SerialPredictionSlot(
operator.PredictionProbabilities,
operator,
name='Predictions',
subname='predictions{:04d}',
)
slots = [
SerialListSlot(
operator.LabelNames,
),
SerialListSlot(
operator.LabelColors,
transform=lambda x: tuple(x.flat),
),
SerialListSlot(
operator.PmapColors,
transform=lambda x: tuple(x.flat),
),
SerialBlockSlot(
operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name='LabelSets',
subname='labels{:0}',
selfdepends=False,
),
self.predictionSlot,
SerialBoxSlot(
operator.opTrain.BoxConstraintRois,
operator.opTrain,
name='Rois',
subname='rois{:04d}',
),
SerialBoxSlot(
operator.opTrain.BoxConstraintValues,
operator.opTrain,
name='Values',
subname='values{:04d}',
),
SerialSlot(
operator.opTrain.Sigma,
name='Sigma',
),
SerialBoxSlot(
operator.boxViewer.rois,
operator.boxViewer,
name='ViewRois',
subname='viewrois{:04d}',
),
SerialCountingSlot(
operator.Classifier,
operator.classifier_cache,
name='CountingWrappers',
),
]
super(CountingSerializer, self).__init__(projectFileGroupName, slots=slots)
self.predictionSlot.progressSignal.subscribe(self.progressSignal)
def __init__(self, operator, projectFileGroupName):
super(NanshePreprocessingSerializer, self).__init__(
projectFileGroupName,
slots=[
SerialSlot(operator.ToRemoveZeroedLines, selfdepends=True),
SerialListSlot(operator.ErosionShape, selfdepends=True),
SerialListSlot(operator.DilationShape, selfdepends=True),
SerialSlot(operator.ToExtractF0, selfdepends=True),
SerialSlot(operator.HalfWindowSize, selfdepends=True),
SerialSlot(operator.WhichQuantile, selfdepends=True),
SerialSlot(operator.TemporalSmoothingGaussianFilterStdev,
selfdepends=True),
SerialSlot(operator.SpatialSmoothingGaussianFilterStdev,
selfdepends=True),
SerialSlot(operator.TemporalSmoothingGaussianFilterWindowSize,
selfdepends=True),
SerialSlot(operator.SpatialSmoothingGaussianFilterWindowSize,
selfdepends=True),
SerialSlot(operator.BiasEnabled, selfdepends=True),
SerialSlot(operator.Bias, selfdepends=True),
SerialSlot(operator.ToWaveletTransform, selfdepends=True),
SerialListSlot(operator.Scale, selfdepends=True),
SerialBlockSlot(operator.CacheOutput,
operator.CacheInput,
operator.CleanBlocks,
selfdepends=True)
])
def _serialize(self, group, name, multislot):
#create subgroups, one for every imagelane
BoxDir = group.create_group(self.name)
self.deserialize(group)
for i, slot in enumerate(multislot):
g = BoxDir.create_group(self.subname.format(i))
s = SerialListSlot(slot)
s.serialize(g)
def _serialize(self, group, name, multislot):
#create subgroups, one for every imagelane
BoxDir = group.create_group(self.name)
self.deserialize(group)
for i, slot in enumerate(multislot):
g = BoxDir.create_group(self.subname.format(i))
s = SerialListSlot(slot)
s.serialize(g)
def __init__(self, operator, groupName):
self.TestSerialSlot = SerialSlot(operator.TestSlot)
self.TestMultiSerialSlot = SerialSlot(operator.TestMultiSlot)
self.TestSerialListSlot = SerialListSlot(operator.TestListSlot,
selfdepends=True)
slots = (self.TestSerialSlot, self.TestMultiSerialSlot,
self.TestSerialListSlot)
super(OpMockSerializer, self).__init__(groupName, slots)
def __init__(self, operator, projectFileGroupName):
slots = [SerialListSlot(operator.InputChannelIndexes, selfdepends=True),
SerialSlot(operator.WatershedPadding, selfdepends=True),
SerialSlot(operator.CacheBlockShape, selfdepends=True),
SerialSlot(operator.SeedThresholdValue, selfdepends=True),
SerialSlot(operator.MinSeedSize, selfdepends=True) ]
super(VigraWatershedViewerSerializer, self).__init__(projectFileGroupName,
slots=slots)
def __init__(self, operator, projectFileGroupName):
self._serialClassifierSlot = SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests")
slots = [SerialListSlot(operator.LabelNames,
transform=str),
SerialListSlot(operator.LabelColors, transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors, transform=lambda x: tuple(x.flat)),
SerialBlockSlot(operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name='LabelSets',
subname='labels{:03d}',
selfdepends=False,
shrink_to_bb=True),
SerialClassifierFactorySlot(operator.ClassifierFactory),
self._serialClassifierSlot ]
super(PixelClassificationSerializer, self).__init__(projectFileGroupName, slots, operator)
def __init__(self, operator, projectFileGroupName):
self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization
self._serialClassifierSlot = SerialClassifierSlot(
operator.Classifier,
operator.classifier_cache,
name="ClassifierForests")
slots = [
SerialListSlot(operator.LabelNames),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialPickleableSlot(operator.Bookmarks, self.VERSION),
SerialBlockSlot(
operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name="LabelSets",
subname="labels{:03d}",
selfdepends=False,
shrink_to_bb=True,
),
SerialClassifierFactorySlot(operator.ClassifierFactory),
self._serialClassifierSlot,
SerialBlockSlot(
operator.opSupervoxelFeaturesAndLabels.SupervoxelFeatures,
operator.opSupervoxelFeaturesAndLabels.
CacheSupervoxelFeaturesInput,
operator.opSupervoxelFeaturesAndLabels.
SupervoxelFeaturesCleanBlocks,
),
SerialBlockSlot(
operator.opSupervoxelFeaturesAndLabels.SupervoxelLabels,
operator.opSupervoxelFeaturesAndLabels.
CacheSupervoxelLabelsInput,
operator.opSupervoxelFeaturesAndLabels.
SupervoxelLabelsCleanBlocks,
),
]
super(VoxelSegmentationSerializer,
self).__init__(projectFileGroupName, slots, operator)
def __init__(self, topGroupName, operator):
serialSlots = [
SerialDictSlot(operator.SelectedFeatures, transform=str),
SerialListSlot(operator.LabelNames,
transform=str),
SerialListSlot(operator.LabelColors, transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors, transform=lambda x: tuple(x.flat)),
SerialDictSlot(operator.LabelInputs, transform=int),
SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests",
subname="Forest{:04d}"),
SerialDictSlot(operator.CachedProbabilities,
operator.InputProbabilities,
transform=int),
]
super(ObjectClassificationSerializer, self ).__init__(topGroupName,
slots=serialSlots,
operator=operator)
def __init__(self, topLevelOperator, projectFileGroupName):
self.VERSION = 1
slots = [
SerialListSlot(topLevelOperator.LabelNames),
SerialListSlot(topLevelOperator.LabelColors, transform=lambda x: tuple(x.flat)),
SerialListSlot(topLevelOperator.PmapColors, transform=lambda x: tuple(x.flat)),
SerialBlockSlot(
topLevelOperator.LabelImages,
topLevelOperator.LabelInputs,
topLevelOperator.NonzeroLabelBlocks,
name="LabelSets",
subname="labels{:03d}",
selfdepends=False,
shrink_to_bb=True,
),
BinarySlot(topLevelOperator.ModelBinary),
]
super().__init__(projectFileGroupName, slots)
def __init__(self, topGroupName, operator):
serialSlots = [
SerialDictSlot(operator.SelectedFeatures, transform=str),
SerialListSlot(operator.LabelNames, transform=str),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialDictSlot(operator.LabelInputs, transform=int),
SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests"),
SerialDictSlot(operator.CachedProbabilities,
operator.InputProbabilities,
transform=int),
#SerialDictSlotWithoutDeserialization(operator.Probabilities, operator, transform=str)
SerialPickledValueSlot(operator.ExportSettings)
]
super(ObjectClassificationSerializer, self).__init__(topGroupName,
slots=serialSlots,
operator=operator)
def __init__(self, topGroupName, operator):
self.VERSION = 1 # Make sure to bump the version in case you make any changes in the serialization
serialSlots = [
SerialDictSlot(operator.SelectedFeatures),
SerialListSlot(operator.LabelNames),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialDictSlot(operator.LabelInputs, transform=int),
SerialClassifierSlot(operator.Classifier,
operator.classifier_cache,
name="ClassifierForests"),
SerialDictSlot(operator.CachedProbabilities,
operator.InputProbabilities,
transform=int),
SerialSlot(operator.MaxNumObj),
SerialPickleableSlot(operator.ExportSettings, self.VERSION, None)
]
super(ObjectClassificationSerializer, self).__init__(topGroupName,
slots=serialSlots,
operator=operator)
def __init__(self, operator, projectFileGroupName):
self.predictionSlot = SerialPredictionSlot(
operator.PredictionProbabilities,
operator,
name='Predictions',
subname='predictions{:04d}',
)
slots = [
SerialListSlot(operator.LabelNames, transform=str),
SerialListSlot(operator.LabelColors,
transform=lambda x: tuple(x.flat)),
SerialListSlot(operator.PmapColors,
transform=lambda x: tuple(x.flat)),
SerialBlockSlot(operator.LabelImages,
operator.LabelInputs,
operator.NonzeroLabelBlocks,
name='LabelSets',
subname='labels{:0}',
selfdepends=False),
SerialCountingSlot(operator.Classifier,
operator.classifier_cache,
name="CountingWrappers",
subname="wrapper{:04d}"), self.predictionSlot,
SerialBoxSlot(operator.opTrain.BoxConstraintRois,
operator.opTrain,
name="Rois",
subname="rois{:04d}"),
SerialBoxSlot(operator.opTrain.BoxConstraintValues,
operator.opTrain,
name="Values",
subname="values{:04d}")
]
super(CountingSerializer, self).__init__(projectFileGroupName,
slots=slots)
self.predictionSlot.progressSignal.connect(self.progressSignal.emit)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialListSlot(operator.ChannelSelections),
SerialSlot(operator.Threshold),
SerialSlot(operator.MinSize),
SerialSlot(operator.Sigma),
SerialSlot(operator.Alpha),
SerialSlot(operator.PixelPitch),
SerialBlockSlot(
operator.Superpixels,
operator.SuperpixelCacheInput,
operator.CleanBlocks,
name="Superpixels",
subname="superpixels{:03d}",
selfdepends=False,
shrink_to_bb=False,
compression_level=1,
),
]
super(WsdtSerializer, self).__init__(projectFileGroupName, slots=slots, operator=operator)
def __init__(self, operator, projectFileGroupName):
slots = [
SerialListSlot(operator.ChannelSelections),
SerialSlot(operator.Pmin),
SerialSlot(operator.MinMembraneSize),
SerialSlot(operator.MinSegmentSize),
SerialSlot(operator.SigmaMinima),
SerialSlot(operator.SigmaWeights),
SerialSlot(operator.GroupSeeds),
SerialSlot(operator.PreserveMembranePmaps),
SerialBlockSlot(operator.Superpixels,
operator.SuperpixelCacheInput,
operator.CleanBlocks,
name='Superpixels',
subname='superpixels{:03d}',
selfdepends=False,
shrink_to_bb=False,
compression_level=1)
]
super(WsdtSerializer, self).__init__(projectFileGroupName,
slots=slots,
operator=operator)
def __init__(self, operator, projectFileGroupName):
super(NanshePostprocessingSerializer, self).__init__(projectFileGroupName,
slots=[SerialSlot(operator.SignificanceThreshold, selfdepends=True),
SerialListSlot(operator.WaveletTransformScale, selfdepends=True),
SerialSlot(operator.NoiseThreshold, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max, selfdepends=True),
SerialSlot(operator.AcceptedRegionShapeConstraints_MajorAxisLength_Max_Enabled, selfdepends=True),
SerialSlot(operator.MinLocalMaxDistance, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Area_Max_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Min_Enabled, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max, selfdepends=True),
SerialSlot(operator.AcceptedNeuronShapeConstraints_Eccentricity_Max_Enabled, selfdepends=True),
SerialSlot(operator.AlignmentMinThreshold, selfdepends=True),
SerialSlot(operator.OverlapMinThreshold, selfdepends=True),
SerialSlot(operator.Fuse_FractionMeanNeuronMaxThreshold, selfdepends=True),
SerialBlockSlot(operator.Output,
operator.CacheInput,
operator.CleanBlocks, selfdepends=True)])
