def setUp(self):
segimg = segImage()
labels = {
0: np.array([0, 1, 2]),
1: np.array([
0,
0,
0,
0,
])
}
rawimg = np.indices(segimg.shape).sum(0).astype(np.float32)
rawimg = rawimg.view(vigra.VigraArray)
rawimg.axistags = vigra.defaultAxistags('txyzc')
g = Graph()
features = {"Standard Object Features": {"Count": {}}}
self.featsop = OpRegionFeatures(graph=g)
self.featsop.LabelVolume.setValue(segimg)
self.featsop.RawVolume.setValue(rawimg)
self.featsop.Features.setValue(features)
self.assertTrue(
self.featsop.Output.ready(),
"The output of operator {} was not ready after connections took place."
.format(self.featsop))
self._opRegFeatsAdaptOutput = OpAdaptTimeListRoi(graph=g)
self._opRegFeatsAdaptOutput.Input.connect(self.featsop.Output)
self.assertTrue(
self._opRegFeatsAdaptOutput.Output.ready(),
"The output of operator {} was not ready after connections took place."
.format(self._opRegFeatsAdaptOutput))
self.trainop = OpObjectTrain(graph=g)
self.trainop.Features.resize(1)
self.trainop.Features[0].connect(self._opRegFeatsAdaptOutput.Output)
self.trainop.SelectedFeatures.setValue(features)
self.trainop.LabelsCount.setValue(2)
self.trainop.Labels.resize(1)
self.trainop.Labels.setValues([labels])
self.trainop.FixClassifier.setValue(False)
self.trainop.ForestCount.setValue(1)
self.assertTrue(
self.trainop.Classifier.ready(),
"The output of operator {} was not ready after connections took place."
.format(self.trainop))
self.op = OpObjectPredict(graph=g)
self.op.Classifier.connect(self.trainop.Classifier)
self.op.Features.connect(self._opRegFeatsAdaptOutput.Output)
self.op.SelectedFeatures.setValue(features)
self.op.LabelsCount.connect(self.trainop.LabelsCount)
self.assertTrue(
self.op.Predictions.ready(),
"The output of operator {} was not ready after connections took place."
.format(self.op))
def __init__(self, block_roi, halo_padding, *args, **kwargs):
super(self.__class__, self).__init__(*args, **kwargs)
self.block_roi = block_roi # In global coordinates
self._halo_padding = halo_padding
self._opBinarySubRegion = OpSubRegion(parent=self)
self._opBinarySubRegion.Input.connect(self.BinaryImage)
self._opRawSubRegion = OpSubRegion(parent=self)
self._opRawSubRegion.Input.connect(self.RawImage)
self._opExtract = OpObjectExtraction(parent=self)
self._opExtract.BinaryImage.connect(self._opBinarySubRegion.Output)
self._opExtract.RawImage.connect(self._opRawSubRegion.Output)
self._opExtract.Features.connect(self.SelectedFeatures)
self.BlockwiseRegionFeatures.connect(
self._opExtract.BlockwiseRegionFeatures)
self._opPredict = OpObjectPredict(parent=self)
self._opPredict.Features.connect(self._opExtract.RegionFeatures)
self._opPredict.SelectedFeatures.connect(self.SelectedFeatures)
self._opPredict.Classifier.connect(self.Classifier)
self._opPredict.LabelsCount.connect(self.LabelsCount)
self._opPredictionImage = OpRelabelSegmentation(parent=self)
self._opPredictionImage.Image.connect(self._opExtract.LabelImage)
self._opPredictionImage.Features.connect(
self._opExtract.RegionFeatures)
self._opPredictionImage.ObjectMap.connect(self._opPredict.Predictions)
def __init__(self, block_roi, halo_padding, *args, **kwargs):
super(self.__class__, self).__init__(*args, **kwargs)
self.block_roi = block_roi # In global coordinates
self._halo_padding = halo_padding
self._opBinarySubRegion = OpSubRegion(parent=self)
self._opBinarySubRegion.Input.connect(self.BinaryImage)
self._opRawSubRegion = OpSubRegion(parent=self)
self._opRawSubRegion.Input.connect(self.RawImage)
self._opExtract = OpObjectExtraction(parent=self)
self._opExtract.BinaryImage.connect(self._opBinarySubRegion.Output)
self._opExtract.RawImage.connect(self._opRawSubRegion.Output)
self._opExtract.Features.connect(self.SelectedFeatures)
self.BlockwiseRegionFeatures.connect(
self._opExtract.BlockwiseRegionFeatures)
self._opExtract._opRegFeats._opCache.name = "blockwise-regionfeats-cache"
self._opPredict = OpObjectPredict(parent=self)
self._opPredict.Features.connect(self._opExtract.RegionFeatures)
self._opPredict.SelectedFeatures.connect(self.SelectedFeatures)
self._opPredict.Classifier.connect(self.Classifier)
self._opPredict.LabelsCount.connect(self.LabelsCount)
self.ObjectwisePredictions.connect(self._opPredict.Predictions)
self._opPredictionImage = OpRelabelSegmentation(parent=self)
self._opPredictionImage.Image.connect(self._opExtract.LabelImage)
self._opPredictionImage.Features.connect(
self._opExtract.RegionFeatures)
self._opPredictionImage.ObjectMap.connect(self._opPredict.Predictions)
self._opPredictionCache = OpArrayCache(parent=self)
self._opPredictionCache.Input.connect(self._opPredictionImage.Output)
self._opProbabilityChannelsToImage = OpMultiRelabelSegmentation(
parent=self)
self._opProbabilityChannelsToImage.Image.connect(
self._opExtract.LabelImage)
self._opProbabilityChannelsToImage.ObjectMaps.connect(
self._opPredict.ProbabilityChannels)
self._opProbabilityChannelsToImage.Features.connect(
self._opExtract.RegionFeatures)
self._opProbabilityChannelStacker = OpMultiArrayStacker(parent=self)
self._opProbabilityChannelStacker.Images.connect(
self._opProbabilityChannelsToImage.Output)
self._opProbabilityChannelStacker.AxisFlag.setValue('c')
self._opProbabilityCache = OpArrayCache(parent=self)
self._opProbabilityCache.Input.connect(
self._opProbabilityChannelStacker.Output)
class TestOpObjectPredict(unittest.TestCase):
def setUp(self):
segimg = segImage()
labels = {0 : np.array([0, 1, 2]),
1 : np.array([0, 0, 0, 0,])}
rawimg = np.indices(segimg.shape).sum(0).astype(np.float32)
rawimg = rawimg.view(vigra.VigraArray)
rawimg.axistags = vigra.defaultAxistags('txyzc')
g = Graph()
features = {"Standard Object Features": {"Count":{}}}
self.featsop = OpRegionFeatures(graph=g)
self.featsop.LabelVolume.setValue(segimg)
self.featsop.RawVolume.setValue( rawimg )
self.featsop.Features.setValue(features)
self.assertTrue(self.featsop.Output.ready(), "The output of operator {} was not ready after connections took place.".format(self.featsop))
self._opRegFeatsAdaptOutput = OpAdaptTimeListRoi(graph=g)
self._opRegFeatsAdaptOutput.Input.connect(self.featsop.Output)
self.assertTrue(self._opRegFeatsAdaptOutput.Output.ready(), "The output of operator {} was not ready after connections took place.".format(self._opRegFeatsAdaptOutput))
self.trainop = OpObjectTrain(graph=g)
self.trainop.Features.resize(1)
self.trainop.Features[0].connect(self._opRegFeatsAdaptOutput.Output)
self.trainop.SelectedFeatures.setValue(features)
self.trainop.LabelsCount.setValue(2)
self.trainop.Labels.resize(1)
self.trainop.Labels.setValues([labels])
self.trainop.FixClassifier.setValue(False)
self.trainop.ForestCount.setValue(1)
self.assertTrue(self.trainop.Classifier.ready(), "The output of operator {} was not ready after connections took place.".format(self.trainop))
self.op = OpObjectPredict(graph=g)
self.op.Classifier.connect(self.trainop.Classifier)
self.op.Features.connect(self._opRegFeatsAdaptOutput.Output)
self.op.SelectedFeatures.setValue(features)
self.op.LabelsCount.connect( self.trainop.LabelsCount )
self.assertTrue(self.op.Predictions.ready(), "The output of operator {} was not ready after connections took place.".format(self.op))
def test_predict(self):
###
# test whether prediction works correctly
# label 1 is 'big object', label 2 is 'small object'
###
preds = self.op.Predictions([0, 1]).wait()
self.assertTrue(np.all(preds[0] == np.array([0, 1, 2])))
self.assertTrue(np.all(preds[1] == np.array([0, 1, 1, 2])))
def test_probabilities(self):
###
# test whether the probability channel slots and the total probability slot return the same values
###
probs = self.op.Probabilities([0, 1]).wait()
probChannel0Time0 = self.op.ProbabilityChannels[0][0].wait()
probChannel1Time0 = self.op.ProbabilityChannels[1][0].wait()
probChannel0Time1 = self.op.ProbabilityChannels[0][1].wait()
probChannel1Time1 = self.op.ProbabilityChannels[1][1].wait()
probChannel0Time01 = self.op.ProbabilityChannels[0]([0, 1]).wait()
self.assertTrue( np.all(probChannel0Time0[0]==probs[0][:, 0]) )
self.assertTrue( np.all(probChannel1Time0[0]==probs[0][:, 1]) )
self.assertTrue( np.all(probChannel0Time1[1]==probs[1][:, 0]) )
self.assertTrue( np.all(probChannel1Time1[1]==probs[1][:, 1]) )
self.assertTrue( np.all(probChannel0Time01[0]==probs[0][:, 0]) )
self.assertTrue( np.all(probChannel0Time01[1]==probs[1][:, 0]) )