def setUp(self):
segimg = segImage()
rawimg = np.indices(segimg.shape).sum(0).astype(np.float32)
rawimg = rawimg.view(vigra.VigraArray)
rawimg.axistags = vigra.defaultAxistags('txyzc')
feats = {"Standard Object Features":
{"Count":{}, "RegionCenter":{}, "Coord<Principal<Kurtosis>>":{}, "Coord<Minimum>":{}, "Coord<Maximum>":{}}
}
g = Graph()
self.featsop = OpRegionFeatures(graph=g)
self.featsop.LabelVolume.setValue(segimg)
self.featsop.RawVolume.setValue( rawimg )
self.featsop.Features.setValue(feats)
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.op = OpObjectTrain(graph=g)
self.op.Features.resize(1)
self.op.Features[0].connect(self._opRegFeatsAdaptOutput.Output)
self.op.SelectedFeatures.setValue(feats)
self.op.FixClassifier.setValue(False)
self.op.ForestCount.setValue(self.nRandomForests)
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 setUp(self):
segimg = segImage()
binimg = (segimg > 0).astype(np.uint8)
labels = {0: np.array([0, 1, 2]), 1: np.array([0, 1, 1, 2])}
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": {},
"RegionCenter": {},
"Coord<Principal<Kurtosis>>": {},
"Coord<Maximum>": {},
"Mean": {},
"Mean in neighborhood": {
"margin": (30, 30, 1)
},
}
}
sel_features = {
"Standard Object Features": {
"Count": {},
"Mean": {},
"Mean in neighborhood": {
"margin": (30, 30, 1)
},
"Variance": {},
}
}
self.extrOp = OpObjectExtraction(graph=g)
self.extrOp.BinaryImage.setValue(binimg)
self.extrOp.RawImage.setValue(rawimg)
self.extrOp.Features.setValue(features)
assert self.extrOp.RegionFeatures.ready()
feats = self.extrOp.RegionFeatures([0, 1]).wait()
assert len(feats) == rawimg.shape[0]
for key in features["Standard Object Features"]:
assert key in list(feats[0]["Standard Object Features"].keys())
self.trainop = OpObjectTrain(graph=g)
self.trainop.Features.resize(1)
self.trainop.Features.connect(self.extrOp.RegionFeatures)
self.trainop.SelectedFeatures.setValue(sel_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)
assert self.trainop.Classifier.ready()