def test(self):
# Define the files we'll be making
testProjectName = 'test_project.ilp'
# Clean up: Remove the test data files we created last time (just in case)
for f in [testProjectName]:
try:
os.remove(f)
except:
pass
# Create an empty project
with h5py.File(testProjectName) as testProject:
testProject.create_dataset("ilastikVersion", data="1.0.0")
# Create an operator to work with and give it some input
g = Graph()
op = OpMockPixelClassifier(graph=g)
operatorToSave = op
serializer = PixelClassificationSerializer(operatorToSave, 'PixelClassificationTest')
op.LabelInputs.resize( 1 )
# Create some labels
labeldata = numpy.zeros(op.dataShape)
labeldata[:,:,0:5,:,:] = 1
labeldata[:,:,50:60,:] = 2
# Slice them into our operator
op.LabelInputs[0][0:1, 0:10, 0:5, 0:100, 0:1] = labeldata[:,:,0:5,:,:]
op.LabelInputs[0][0:1, 0:10, 50:60, 0:100, 0:1] = labeldata[:,:,50:60,:,:]
# change label names and colors
op.LabelNames.setValue( ["Label1", "Label2"] )
op.LabelColors.setValue( [(255,30,30), (30,255,30)] )
op.PmapColors.setValue( [(255,30,30), (30,255,30)] )
# Simulate the predictions changing by setting the prediction output dirty
op.PredictionProbabilities[0].setDirty(slice(None))
# Serialize!
serializer.serializeToHdf5(testProject, testProjectName)
# Deserialize into a fresh operator
operatorToLoad = OpMockPixelClassifier(graph=g)
deserializer = PixelClassificationSerializer(operatorToLoad, 'PixelClassificationTest')
deserializer.deserializeFromHdf5(testProject, testProjectName)
# Did the data go in and out of the file without problems?
assert len(operatorToLoad.LabelImages) == 1
assert (operatorToSave.LabelImages[0][...].wait() == operatorToLoad.LabelImages[0][...].wait()).all()
assert (operatorToSave.LabelImages[0][...].wait() == labeldata[...]).all()
assert operatorToSave.LabelNames.value == operatorToLoad.LabelNames.value
assert (numpy.array(operatorToSave.LabelColors.value) == numpy.array(operatorToLoad.LabelColors.value)).all()
assert (numpy.array(operatorToSave.PmapColors.value) == numpy.array(operatorToLoad.PmapColors.value)).all()
os.remove(testProjectName)
def test(self):
# Define the files we'll be making
testProjectName = 'test_project.ilp'
# Clean up: Remove the test data files we created last time (just in case)
for f in [testProjectName]:
try:
os.remove(f)
except:
pass
# Create an empty project
with h5py.File(testProjectName) as testProject:
testProject.create_dataset("ilastikVersion", data=0.6)
# Create an operator to work with and give it some input
g = Graph()
op = OpMockPixelClassifier(graph=g)
operatorToSave = op
serializer = PixelClassificationSerializer(operatorToSave, 'PixelClassificationTest')
op.LabelInputs.resize( 1 )
# Create some labels
labeldata = numpy.zeros(op.dataShape)
labeldata[:,:,0:5,:,:] = 1
labeldata[:,:,50:60,:] = 2
# Slice them into our operator
op.LabelInputs[0][0:1, 0:10, 0:5, 0:100, 0:1] = labeldata[:,:,0:5,:,:]
op.LabelInputs[0][0:1, 0:10, 50:60, 0:100, 0:1] = labeldata[:,:,50:60,:,:]
# Simulate the predictions changing by setting the prediction output dirty
op.PredictionProbabilities[0].setDirty(slice(None))
# Enable prediction storage
serializer.predictionStorageEnabled = True
# Serialize!
serializer.serializeToHdf5(testProject, testProjectName)
# Check that the prediction data was written to the file
assert (testProject['PixelClassificationTest/Predictions/predictions0000'][...] == op.PredictionProbabilities[0][...].wait()).all()
# Deserialize into a fresh operator
operatorToLoad = OpMockPixelClassifier(graph=g)
deserializer = PixelClassificationSerializer(operatorToLoad, 'PixelClassificationTest')
deserializer.deserializeFromHdf5(testProject, testProjectName)
# Did the data go in and out of the file without problems?
assert len(operatorToLoad.LabelImages) == 1
assert (operatorToSave.LabelImages[0][...].wait() == operatorToLoad.LabelImages[0][...].wait()).all()
assert (operatorToSave.LabelImages[0][...].wait() == labeldata[...]).all()
os.remove(testProjectName)
def __init__(self, workflow, projectFileGroupName):
self._topLevelOperator = OpIIBoostPixelClassification(parent=workflow)
def on_classifier_changed(slot, roi):
if self._topLevelOperator.classifier_cache.Output.ready() and \
self._topLevelOperator.classifier_cache.fixAtCurrent.value is True and \
self._topLevelOperator.classifier_cache.Output.value is None:
# When the classifier is deleted (e.g. because the number of features has changed,
# then notify the workflow. (Export applet should be disabled.)
self.appletStateUpdateRequested()
self._topLevelOperator.classifier_cache.Output.notifyDirty(
on_classifier_changed)
super(IIBoostPixelClassificationApplet,
self).__init__("Synapse Detection")
# We can use the ordinary pixel classification serializer class.
self._serializableItems = [
PixelClassificationSerializer(self._topLevelOperator,
projectFileGroupName)
]
self._gui = None
# GUI needs access to the serializer to enable/disable prediction storage
self.predictionSerializer = self._serializableItems[0]
# FIXME: For now, we can directly connect the progress signal from the classifier training operator
# directly to the applet's overall progress signal, because it's the only thing we report progress for at the moment.
# If we start reporting progress for multiple tasks that might occur simultaneously,
# we'll need to aggregate the progress updates.
self._topLevelOperator.opTrain.progressSignal.subscribe(
self.progressSignal)