def test_load_single_file_with_list(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph, operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
fileNameString = os.path.pathsep.join(self.file_names)
info = DatasetInfo(filepath=fileNameString)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData = reader.Image[0][...].wait()
print('imgData', reader.Image.meta.axistags, reader.Image.meta.original_axistags)
# Check raw images
assert imgData.shape == self.imgData3Dct.shape, (imgData.shape, self.imgData3Dct.shape)
numpy.testing.assert_array_equal(imgData, self.imgData3Dct)
def testBasic3DstacksFromFileList(self):
for ext, fileNames in list(self.imgFileLists2D.items()):
fileNameString = os.path.pathsep.join(fileNames)
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph, operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo(filepath=fileNameString)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3D = reader.Image[0][...].wait()
# Check raw images
assert imgData3D.shape == self.imgData3D.shape, (imgData3D.shape, self.imgData3D.shape)
# skip this if image was saved compressed:
if any(x.strip('.') in ext.lower() for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(ext))
continue
numpy.testing.assert_array_equal(imgData3D, self.imgData3D)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
for i, dataFile in enumerate(self.SAMPLE_DATA):
# Add a file
info = DatasetInfo()
info.filePath = dataFile
opDataSelection.DatasetGroup.resize(i + 1)
opDataSelection.DatasetGroup[i][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
workflow = self.workflow
# New project
shell.createAndLoadNewProject(projFilePath)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
featureGui = workflow.featureSelectionApplet.gui
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
for i, dataFile in enumerate(self.SAMPLE_DATA):
# Add a file
info = DatasetInfo()
info.filePath = dataFile
opDataSelection.DatasetGroup.resize(i + 1)
opDataSelection.DatasetGroup[i][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.FeatureIds.setValue(
OpPixelFeaturesPresmoothed.DefaultFeatureIds)
opFeatures.Scales.setValue([0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0])
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath)
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.FeatureIds.setValue(
OpPixelFeaturesPresmoothed.DefaultFeatureIds)
opFeatures.Scales.setValue([0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0])
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False]])
opFeatures.SelectionMatrix.setValue(selections)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
opDataSelection.DatasetGroup[0][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def test_fake_data_source(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph,
operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = self.testRawDataFileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
# Use *fake* data source
info.realDataSource = False
info.axistags = vigra.defaultAxistags('tczyx')
info.laneShape = self.imgData.shape
info.laneDtype = self.imgData.dtype
reader.Dataset.setValues([info])
# Verify that now data selection operator returns fake data
# with expected shape and type
imgData = reader.Image[0][...].wait()
assert imgData.shape == self.imgData.shape
assert imgData.dtype == self.imgData.dtype
expected_fake_data = numpy.zeros(info.laneShape, dtype=info.laneDtype)
numpy.testing.assert_array_equal(imgData, expected_fake_data)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath)
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.FeatureIds.setValue( OpPixelFeaturesPresmoothed.DefaultFeatureIds )
opFeatures.Scales.setValue( [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0] )
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False],
[True, True, True, True, True, True, False]] )
opFeatures.SelectionMatrix.setValue(selections)
def _get_template_dataset_infos(self, input_axes=None):
"""
Sometimes the default settings for an input file are not suitable (e.g. the axistags need to be changed).
We assume the LAST non-batch input in the workflow has settings that will work for all batch processing inputs.
Here, we get the DatasetInfo objects from that lane and store them as 'templates' to modify for all batch-processing files.
"""
template_infos = {}
# If there isn't an available dataset to use as a template
if len(self.dataSelectionApplet.topLevelOperator.DatasetGroup) == 0:
num_roles = len(self.dataSelectionApplet.topLevelOperator.DatasetRoles.value)
for role_index in range(num_roles):
template_infos[role_index] = DatasetInfo()
template_infos[role_index].axistags = vigra.defaultAxistags(input_axes)
return template_infos
# Use the LAST non-batch input file as our 'template' for DatasetInfo settings (e.g. axistags)
template_lane = len(self.dataSelectionApplet.topLevelOperator.DatasetGroup) - 1
opDataSelectionTemplateView = self.dataSelectionApplet.topLevelOperator.getLane(template_lane)
for role_index, info_slot in enumerate(opDataSelectionTemplateView.DatasetGroup):
if info_slot.ready():
template_infos[role_index] = info_slot.value
else:
template_infos[role_index] = DatasetInfo()
if input_axes:
# Support the --input_axes arg to override input axis order, same as DataSelection applet.
template_infos[role_index].axistags = vigra.defaultAxistags(input_axes)
return template_infos
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath)
workflow = shell.projectManager.workflow
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
for i, dataFile in enumerate(self.SAMPLE_DATA):
# Add a file
info = DatasetInfo()
info.filePath = dataFile
opDataSelection.Dataset.resize(i+1)
opDataSelection.Dataset[i].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.FeatureIds.setValue( OpPixelFeaturesPresmoothed.DefaultFeatureIds )
opFeatures.Scales.setValue( [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0] )
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def testWeirdAxisInfos(self):
"""
If we add a dataset that has the channel axis in the wrong place,
the operator should automatically transpose it to be last.
"""
weirdAxisFilename = os.path.join(self.workingDir, 'WeirdAxes.npy')
expected_data = numpy.random.random( (3,100,100) )
numpy.save(weirdAxisFilename, expected_data)
info = DatasetInfo()
info.filePath = weirdAxisFilename
info.axistags = vigra.defaultAxistags('cxy')
graph = Graph()
op = OpDataSelectionGroup(graph=graph, forceAxisOrder=False)
op.WorkingDirectory.setValue( self.workingDir )
op.DatasetRoles.setValue( ['RoleA'] )
op.DatasetGroup.resize( 1 )
op.DatasetGroup[0].setValue( info )
assert op.ImageGroup[0].ready()
data_from_op = op.ImageGroup[0][:].wait()
assert data_from_op.dtype == expected_data.dtype
assert data_from_op.shape == expected_data.shape, (data_from_op.shape, expected_data.shape)
assert (data_from_op == expected_data).all()
# op.Image is a synonym for op.ImageGroup[0]
assert op.Image.ready()
assert (op.Image[:].wait() == expected_data).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def debug_with_new(shell, workflow):
"""
(Function for debug and testing.)
"""
projFilePath = "/magnetic/test_watershed_project.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
#info.filePath = '/magnetic/gigacube.h5'
#info.filePath = '/magnetic/synapse_small.npy'
info.filePath = '/magnetic/synapse_small.npy_results.h5'
#info.filePath = '/magnetic/singleslice.h5'
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Select the watershed drawer
shell.setSelectedAppletDrawer(1)
# Save the project
shell.onSaveProjectActionTriggered()
def debug_with_new(shell):
"""
(Function for debug and testing.)
"""
#projFilePath = "/magnetic/synapse_debug_data/object_prediction.ilp"
projFilePath = "/magnetic/stuart_object_predictions.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
rawInfo = DatasetInfo()
#rawInfo.filePath = '/magnetic/synapse_debug_data/block256.h5/cube'
#rawInfo.filePath = '/magnetic/synapse_small_4d.h5/volume/data'
rawInfo.filePath = '/magnetic/validation_slices_20_40_3200_4000_1200_2000.h5/volume/data'
opRawDataSelection = workflow.rawDataSelectionApplet.topLevelOperator
opRawDataSelection.Dataset.resize(1)
opRawDataSelection.Dataset[0].setValue(rawInfo)
predictionInfo = DatasetInfo()
#predictionInfo.filePath = '/magnetic/synapse_debug_data/block256_spots_predictions.h5/cube'
#predictionInfo.filePath = '/magnetic/synapse_small_4d_synapse_predictions.h5/volume/data'
predictionInfo.filePath = '/magnetic/validation_slices_20_40_3200_4000_1200_2000_pred.h5/volume/data'
opPredDataSelection = workflow.predictionSelectionApplet.topLevelOperator
opPredDataSelection.Dataset.resize(1)
opPredDataSelection.Dataset[0].setValue(predictionInfo)
# Select the feature drawer
shell.setSelectedAppletDrawer(2)
def debug_with_new(shell, workflow):
"""
(Function for debug and testing.)
"""
projFilePath = "/magnetic/test_watershed_project.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
#info.filePath = '/magnetic/gigacube.h5'
#info.filePath = '/magnetic/synapse_small.npy'
info.filePath = '/magnetic/synapse_small.npy_results.h5'
#info.filePath = '/magnetic/singleslice.h5'
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Select the watershed drawer
shell.setSelectedAppletDrawer(1)
# Save the project
shell.onSaveProjectActionTriggered()
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
for i, dataFile in enumerate(self.SAMPLE_DATA):
# Add a file
info = DatasetInfo()
info.filePath = dataFile
opDataSelection.DatasetGroup.resize(i+1)
opDataSelection.DatasetGroup[i][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
def create_new_tst_project(cls):
# Instantiate 'shell'
shell = HeadlessShell( )
# Create a blank project file and load it.
newProjectFilePath = cls.PROJECT_FILE
newProjectFile = ProjectManager.createBlankProjectFile(newProjectFilePath, PixelClassificationWorkflow, [])
newProjectFile.close()
shell.openProjectFile(newProjectFilePath)
workflow = shell.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = cls.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
opDataSelection.DatasetGroup[0][0].setValue(info)
# Set some features
ScalesList = [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0]
FeatureIds = [ 'GaussianSmoothing',
'LaplacianOfGaussian',
'StructureTensorEigenvalues',
'HessianOfGaussianEigenvalues',
'GaussianGradientMagnitude',
'DifferenceOfGaussians' ]
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.Scales.setValue( ScalesList )
opFeatures.FeatureIds.setValue( FeatureIds )
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
# Add some labels directly to the operator
opPixelClass = workflow.pcApplet.topLevelOperator
opPixelClass.LabelNames.setValue(['Label 1', 'Label 2'])
slicing1 = sl[0:1,0:10,0:10,0:1,0:1]
labels1 = 1 * numpy.ones(slicing2shape(slicing1), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing1] = labels1
slicing2 = sl[0:1,0:10,10:20,0:1,0:1]
labels2 = 2 * numpy.ones(slicing2shape(slicing2), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing2] = labels2
# Save and close
shell.projectManager.saveProject()
del shell
def testCreateExportDirectory(self):
"""
Test that the batch operator can create the export directory if it doesn't exist yet.
"""
# Start by writing some test data to disk.
self.testData = numpy.random.random((1, 10, 10, 10, 1))
numpy.save(self.testDataFileName, self.testData)
cwd = os.getcwd()
info = DatasetInfo()
info.filePath = os.path.join(cwd, 'NpyTestData.npy')
graph = Graph()
opBatchIo = OpBatchIo(graph=graph)
opInput = OpInputDataReader(graph=graph)
opInput.FilePath.setValue(info.filePath)
# Our test "processing pipeline" is just a smoothing operator.
opSmooth = OpGaussianSmoothing(graph=graph)
opSmooth.Input.connect(opInput.Output)
opSmooth.sigma.setValue(3.0)
exportDir = os.path.join(cwd, 'exported_data')
opBatchIo.ExportDirectory.setValue(exportDir)
opBatchIo.Suffix.setValue('_smoothed')
opBatchIo.Format.setValue(ExportFormat.H5)
opBatchIo.DatasetPath.setValue(info.filePath)
internalPath = 'path/to/data'
opBatchIo.InternalPath.setValue(internalPath)
opBatchIo.ImageToExport.connect(opSmooth.Output)
dirty = opBatchIo.Dirty.value
assert dirty == True
outputPath = opBatchIo.OutputDataPath.value
assert outputPath == os.path.join(exportDir, 'NpyTestData_smoothed.h5',
internalPath)
result = opBatchIo.ExportResult.value
assert result
dirty = opBatchIo.Dirty.value
assert dirty == False
# Check the file
smoothedPath = PathComponents(outputPath).externalPath
with h5py.File(smoothedPath, 'r') as f:
assert internalPath in f
assert f[internalPath].shape == self.testData.shape
try:
os.remove(smoothedPath)
os.rmdir(exportDir)
except:
pass
def create_new_tst_project(cls):
# Instantiate 'shell'
shell = HeadlessShell()
# Create a blank project file and load it.
newProjectFilePath = cls.PROJECT_FILE
newProjectFile = ProjectManager.createBlankProjectFile(
newProjectFilePath, PixelClassificationWorkflow, [])
newProjectFile.close()
shell.openProjectFile(newProjectFilePath)
workflow = shell.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = cls.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
opDataSelection.DatasetGroup[0][0].setValue(info)
# Set some features
ScalesList = [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0]
FeatureIds = [
'GaussianSmoothing', 'LaplacianOfGaussian',
'StructureTensorEigenvalues', 'HessianOfGaussianEigenvalues',
'GaussianGradientMagnitude', 'DifferenceOfGaussians'
]
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.Scales.setValue(ScalesList)
opFeatures.FeatureIds.setValue(FeatureIds)
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
# Add some labels directly to the operator
opPixelClass = workflow.pcApplet.topLevelOperator
slicing1 = sl[0:1, 0:10, 0:10, 0:1, 0:1]
labels1 = 1 * numpy.ones(slicing2shape(slicing1), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing1] = labels1
slicing2 = sl[0:1, 0:10, 10:20, 0:1, 0:1]
labels2 = 2 * numpy.ones(slicing2shape(slicing2), dtype=numpy.uint8)
opPixelClass.LabelInputs[0][slicing2] = labels2
# Save and close
shell.projectManager.saveProject()
del shell
def testCreateExportDirectory(self):
"""
Test that the batch operator can create the export directory if it doesn't exist yet.
"""
# Start by writing some test data to disk.
self.testData = numpy.random.random((1,10,10,10,1))
numpy.save(self.testDataFileName, self.testData)
cwd = os.getcwd()
info = DatasetInfo()
info.filePath = os.path.join(cwd, 'NpyTestData.npy')
graph = Graph()
opBatchIo = OpBatchIo(graph=graph)
opInput = OpInputDataReader(graph=graph)
opInput.FilePath.setValue( info.filePath )
# Our test "processing pipeline" is just a smoothing operator.
opSmooth = OpGaussianSmoothing(graph=graph)
opSmooth.Input.connect( opInput.Output )
opSmooth.sigma.setValue(3.0)
exportDir = os.path.join(cwd, 'exported_data')
opBatchIo.ExportDirectory.setValue( exportDir )
opBatchIo.Suffix.setValue( '_smoothed' )
opBatchIo.Format.setValue( ExportFormat.H5 )
opBatchIo.DatasetPath.setValue( info.filePath )
opBatchIo.WorkingDirectory.setValue( cwd )
internalPath = 'path/to/data'
opBatchIo.InternalPath.setValue( internalPath )
opBatchIo.ImageToExport.connect( opSmooth.Output )
dirty = opBatchIo.Dirty.value
assert dirty == True
outputPath = opBatchIo.OutputDataPath.value
assert outputPath == os.path.join(exportDir, 'NpyTestData_smoothed.h5', internalPath)
result = opBatchIo.ExportResult.value
assert result
dirty = opBatchIo.Dirty.value
assert dirty == False
# Check the file
smoothedPath = PathComponents(outputPath).externalPath
with h5py.File(smoothedPath, 'r') as f:
assert internalPath in f
assert f[internalPath].shape == self.testData.shape
try:
os.remove(smoothedPath)
os.rmdir(exportDir)
except:
pass
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
opDataSelection.DatasetGroup[0][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array(
[
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
]
)
opFeatures.SelectionMatrix.setValue(selections)
workflow = self.shell.projectManager.workflow
countingClassApplet = workflow.countingApplet
gui = countingClassApplet.getMultiLaneGui()
opCount = countingClassApplet.topLevelOperator
opCount.opTrain.Sigma.setValue(self.COUNTING_SIGMA)
# Select the labeling drawer
self.shell.setSelectedAppletDrawer(COUNTING_APPLET_INDEX)
# Turn off the huds and so we can capture the raw image
viewMenu = gui.currentGui().menus()[0]
viewMenu.actionToggleAllHuds.trigger()
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def basicImpl(self):
cwd = os.getcwd()
info = DatasetInfo()
info.filePath = os.path.join(cwd, self.testDataFileName)
graph = Graph()
opBatchIo = OpBatchIo(graph=graph)
opInput = OpInputDataReader(graph=graph)
opInput.FilePath.setValue( info.filePath )
# Our test "processing pipeline" is just a smoothing operator.
opSmooth = OpGaussianSmoothing(graph=graph)
opSmooth.Input.connect( opInput.Output )
opSmooth.sigma.setValue(3.0)
opBatchIo.ExportDirectory.setValue( '' )
opBatchIo.Suffix.setValue( '_smoothed' )
opBatchIo.Format.setValue( ExportFormat.H5 )
opBatchIo.DatasetPath.setValue( info.filePath )
opBatchIo.WorkingDirectory.setValue( cwd )
internalPath = 'path/to/data'
opBatchIo.InternalPath.setValue( internalPath )
opBatchIo.ImageToExport.connect( opSmooth.Output )
dirty = opBatchIo.Dirty.value
assert dirty == True
outputPath = opBatchIo.OutputDataPath.value
assert outputPath == os.path.join(cwd, 'NpyTestData_smoothed.h5/' + internalPath)
result = opBatchIo.ExportResult.value
assert result
dirty = opBatchIo.Dirty.value
assert dirty == False
# Check the file
smoothedPath = os.path.join(cwd, 'NpyTestData_smoothed.h5')
with h5py.File(smoothedPath, 'r') as f:
assert internalPath in f
assert f[internalPath].shape == self.expectedDataShape
assert (f[internalPath][:] == opSmooth.Output[:].wait()).all()
try:
os.remove(smoothedPath)
except:
pass
# Check the exported image
assert ( opBatchIo.ExportedImage[:].wait() == opSmooth.Output[:].wait() ).all()
def testBasic3DWrongAxes(self):
"""Test if 3D file with intentionally wrong axes is rejected """
for fileName in self.imgFileNames3D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
info.axistags = vigra.defaultAxistags('tzyc')
try:
reader.Dataset.setValues([info])
assert False, "Should have thrown an exception!"
except DatasetConstraintError:
pass
except:
assert False, "Should have thrown a DatasetConstraintError!"
def loadProject(shell, workflow):
if not os.path.exists(projectFilename):
shell.createAndLoadNewProject(projectFilename)
else:
shell.openProjectFile(projectFilename)
workflow.setCarvingGraphFile(carvingGraphFilename)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = carvingGraphFilename + "/graph/raw"
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
shell.setSelectedAppletDrawer(2)
def impl():
projFilePath = self.PROJECT_FILE
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = self.SAMPLE_DATA
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
opDataSelection.DatasetGroup[0][0].setValue(info)
# Set some features
opFeatures = workflow.featureSelectionApplet.topLevelOperator
opFeatures.FeatureIds.setValue( OpPixelFeaturesPresmoothed.DefaultFeatureIds )
opFeatures.Scales.setValue( [0.3, 0.7, 1, 1.6, 3.5, 5.0, 10.0] )
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
selections = numpy.array( [[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
#shell.setSelectedAppletDrawer(5)
workflow = self.shell.projectManager.workflow
countingClassApplet = workflow.countingApplet
gui = countingClassApplet.getMultiLaneGui()
opCount = countingClassApplet.topLevelOperator
# Select the labeling drawer
self.shell.setSelectedAppletDrawer(3)
# Turn off the huds and so we can capture the raw image
viewMenu = gui.currentGui().menus()[0]
viewMenu.actionToggleAllHuds.trigger()
# Save and close
shell.projectManager.saveProject()
shell.ensureNoCurrentProject(assertClean=True)
def generateBatchPredictions(workflow, batchInputPaths, batchExportDir, batchOutputSuffix, exportedDatasetName):
"""
Compute the predictions for each of the specified batch input files,
and export them to corresponding h5 files.
"""
batchInputPaths = convertStacksToH5(batchInputPaths)
batchInputInfos = []
for p in batchInputPaths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
batchInputInfos.append(info)
# Configure batch input operator
opBatchInputs = workflow.batchInputApplet.topLevelOperator
opBatchInputs.Dataset.setValues( batchInputInfos )
# Configure batch export operator
opBatchResults = workflow.batchResultsApplet.topLevelOperator
opBatchResults.ExportDirectory.setValue(batchExportDir)
opBatchResults.Format.setValue(ExportFormat.H5)
opBatchResults.Suffix.setValue(batchOutputSuffix)
opBatchResults.InternalPath.setValue(exportedDatasetName)
opBatchResults.SelectedSlices.setValue([30])
logger.info( "Exporting data to " + opBatchResults.OutputDataPath[0].value )
# Set up progress display handling (just logging for now)
currentProgress = [None]
def handleProgress(percentComplete):
if currentProgress[0] != percentComplete:
currentProgress[0] = percentComplete
logger.info("Batch job: {}% complete.".format(percentComplete))
progressSignal = opBatchResults.ProgressSignal[0].value
progressSignal.subscribe( handleProgress )
# Make it happen!
result = opBatchResults.ExportResult[0].value
return result
def generateBatchPredictions(workflow, batchInputPaths, batchExportDir, batchOutputSuffix, exportedDatasetName):
"""
Compute the predictions for each of the specified batch input files,
and export them to corresponding h5 files.
"""
batchInputPaths = convertStacksToH5(batchInputPaths)
batchInputInfos = []
for p in batchInputPaths:
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
# Convert all paths to absolute
# (otherwise they are relative to the project file, which probably isn't what the user meant)
comp = PathComponents(p)
comp.externalPath = os.path.abspath(comp.externalPath)
info.filePath = comp.totalPath()
batchInputInfos.append(info)
# Configure batch input operator
opBatchInputs = workflow.batchInputApplet.topLevelOperator
opBatchInputs.Dataset.setValues( batchInputInfos )
# Configure batch export operator
opBatchResults = workflow.batchResultsApplet.topLevelOperator
opBatchResults.ExportDirectory.setValue(batchExportDir)
opBatchResults.Format.setValue(ExportFormat.H5)
opBatchResults.Suffix.setValue(batchOutputSuffix)
opBatchResults.InternalPath.setValue(exportedDatasetName)
opBatchResults.SelectedSlices.setValue([30])
logger.info( "Exporting data to " + opBatchResults.OutputDataPath[0].value )
# Set up progress display handling (just logging for now)
currentProgress = [None]
def handleProgress(percentComplete):
if currentProgress[0] != percentComplete:
currentProgress[0] = percentComplete
logger.info("Batch job: {}% complete.".format(percentComplete))
progressSignal = opBatchResults.ProgressSignal[0].value
progressSignal.subscribe( handleProgress )
# Make it happen!
result = opBatchResults.ExportResult[0].value
return result
def basicImpl(self):
cwd = os.getcwd()
info = DatasetInfo()
info.filePath = os.path.join(cwd, 'NpyTestData.npy')
graph = Graph()
opBatchIo = OpBatchIo(graph=graph)
opInput = OpInputDataReader(graph=graph)
opInput.FilePath.setValue(info.filePath)
# Our test "processing pipeline" is just a smoothing operator.
opSmooth = OpGaussianSmoothing(graph=graph)
opSmooth.Input.connect(opInput.Output)
opSmooth.sigma.setValue(3.0)
opBatchIo.ExportDirectory.setValue('')
opBatchIo.Suffix.setValue('_smoothed')
opBatchIo.Format.setValue(ExportFormat.H5)
opBatchIo.DatasetPath.setValue(info.filePath)
internalPath = 'path/to/data'
opBatchIo.InternalPath.setValue(internalPath)
opBatchIo.ImageToExport.connect(opSmooth.Output)
dirty = opBatchIo.Dirty.value
assert dirty == True
outputPath = opBatchIo.OutputDataPath.value
assert outputPath == os.path.join(
cwd, 'NpyTestData_smoothed.h5/' + internalPath)
result = opBatchIo.ExportResult.value
assert result
dirty = opBatchIo.Dirty.value
assert dirty == False
# Check the file
smoothedPath = os.path.join(cwd, 'NpyTestData_smoothed.h5')
with h5py.File(smoothedPath, 'r') as f:
assert internalPath in f
assert f[internalPath].shape == self.expectedDataShape
try:
os.remove(smoothedPath)
except:
pass
def impl():
projFilePath = self.project_file
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add our input files:
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
info_raw = DatasetInfo()
info_raw.filePath = self.sample_data_raw
opDataSelection.DatasetGroup[0][0].setValue(info_raw)
# Save
shell.projectManager.saveProject()
def impl():
projFilePath = self.project_file
shell = self.shell
# New project
shell.createAndLoadNewProject(projFilePath, self.workflowClass())
workflow = shell.projectManager.workflow
# Add our input files:
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.DatasetGroup.resize(1)
info_raw = DatasetInfo()
info_raw.filePath = self.sample_data_raw
opDataSelection.DatasetGroup[0][0].setValue(info_raw)
# Save
shell.projectManager.saveProject()
def testBasic3DWrongAxes(self):
"""Test if 3D file with intentionally wrong axes is rejected """
for fileName in self.imgFileNames3D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph, operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
info.axistags = vigra.defaultAxistags('tzyc')
try:
reader.Dataset.setValues([info])
assert False, "Should have thrown an exception!"
except DatasetConstraintError:
pass
except:
assert False, "Should have thrown a DatasetConstraintError!"
def test_real_data_source(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph,
operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = self.testRawDataFileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
#Use real data source
info.realDataSource = True
reader.Dataset.setValues([info])
# Read the test file using the data selection operator and verify the contents
imgData = reader.Image[0][...].wait()
assert imgData.shape == self.imgData.shape
numpy.testing.assert_array_equal(imgData, self.imgData)
def testBasic2D(self):
"""Test if plane 2d files are loaded correctly"""
for fileName in self.imgFileNames2D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection,
graph=graph,
operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData2D = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData2D.shape == self.imgData2D.shape
# skip this if image was saved compressed:
if any(x in fileName.lower() for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(
fileName))
continue
numpy.testing.assert_array_equal(imgData2D, self.imgData2D)
def _run_export_with_empty_batch_lane(self, role_input_paths, batch_lane_index, template_infos, progress_callback):
"""
Configure the fresh batch lane with the given input files, and export the results.
"""
assert role_input_paths[0], "At least one file must be provided for each dataset (the first role)."
opDataSelectionBatchLaneView = self.dataSelectionApplet.topLevelOperator.getLane( batch_lane_index )
# Apply new settings for each role
for role_index, path_for_role in enumerate(role_input_paths):
if not path_for_role:
continue
if template_infos[role_index]:
info = copy.copy(template_infos[role_index])
else:
info = DatasetInfo()
# Override the template settings with the current filepath.
default_info = DataSelectionApplet.create_default_headless_dataset_info(path_for_role)
info.filePath = default_info.filePath
info.location = default_info.location
info.nickname = default_info.nickname
# Apply to the data selection operator
opDataSelectionBatchLaneView.DatasetGroup[role_index].setValue(info)
# Make sure nothing went wrong
opDataExportBatchlaneView = self.dataExportApplet.topLevelOperator.getLane( batch_lane_index )
assert opDataExportBatchlaneView.ImageToExport.ready()
assert opDataExportBatchlaneView.ExportPath.ready()
# New lanes were added.
# Give the workflow a chance to restore anything that was unecessarily invalidated (e.g. classifiers)
self.workflow.handleNewLanesAdded()
# Call customization hook
self.dataExportApplet.prepare_lane_for_export(batch_lane_index)
# Finally, run the export
logger.info("Exporting to {}".format( opDataExportBatchlaneView.ExportPath.value ))
opDataExportBatchlaneView.progressSignal.subscribe(progress_callback)
opDataExportBatchlaneView.run_export()
# Call customization hook
self.dataExportApplet.post_process_lane_export(batch_lane_index)
def testBasic3DcStackFromGlobString(self):
"""Test if stacked 2d 3-channel files are loaded correctly"""
# For some reason vigra saves 2D+c data compressed in gifs, so skip!
self.compressedExtensions.append('.gif')
for fileName in self.imgFileNameGlobs2Dc:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3Dc = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData3Dc.shape == self.imgData3Dc.shape
# skip this if image was saved compressed:
if any(x in fileName.lower() for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(
fileName))
continue
numpy.testing.assert_array_equal(imgData3Dc, self.imgData3Dc)
def testProjectLocalData(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
# Create a list of dataset infos . . .
datasetInfos = []
# From project
info = DatasetInfo()
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = "This string should be ignored..."
info._datasetId = 'dataset1' # (Cheating a bit here...)
info.invertColors = False
info.convertToGrayscale = False
datasetInfos.append(info)
reader.Dataset.setValues(datasetInfos)
projectInternalData = reader.Image[0][...].wait()
assert projectInternalData.shape == self.imgData3Dc.shape
assert (projectInternalData == self.imgData3Dc).all()
def testBasic3D(self):
"""Test if plane 2d files are loaded correctly"""
for fileName in self.imgFileNames3D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3D = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData3D.shape == self.imgData3D.shape
# skip this if image was saved compressed:
numpy.testing.assert_array_equal(imgData3D, self.imgData3D)
def debug_with_new(shell, workflow):
"""
(Function for debug and testing.)
"""
projFilePath = "/magnetic/test_project.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = '/magnetic/gigacube.h5'
#info.filePath = '/magnetic/synapse_small.npy'
#info.filePath = '/magnetic/singleslice.h5'
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
import numpy
featureGui = workflow.featureSelectionApplet.gui
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
# selections = numpy.array( [[True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True]] )
selections = numpy.array(
[[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]])
opFeatures.SelectionMatrix.setValue(selections)
# Select the feature drawer
shell.setSelectedAppletDrawer(2)
# Save the project
shell.onSaveProjectActionTriggered()
def debug_with_new(shell, workflow):
"""
(Function for debug and testing.)
"""
projFilePath = "/magnetic/test_project.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
# Add a file
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
info = DatasetInfo()
info.filePath = '/magnetic/gigacube.h5'
#info.filePath = '/magnetic/synapse_small.npy'
#info.filePath = '/magnetic/singleslice.h5'
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(info)
# Set some features
import numpy
featureGui = workflow.featureSelectionApplet.gui
opFeatures = workflow.featureSelectionApplet.topLevelOperator
# sigma: 0.3 0.7 1.0 1.6 3.5 5.0 10.0
# selections = numpy.array( [[True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True],
# [True, True, True, True, True, True, True]] )
selections = numpy.array( [[True, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False],
[False, False, False, False, False, False, False]] )
opFeatures.SelectionMatrix.setValue(selections)
# Select the feature drawer
shell.setSelectedAppletDrawer(2)
# Save the project
shell.onSaveProjectActionTriggered()
def test(self):
"""
Make sure that the dataset roles work the way we expect them to.
"""
infoA = DatasetInfo()
infoA.filePath = self.group1Data[0][0]
infoC = DatasetInfo()
infoC.filePath = self.group1Data[1][0]
graph = Graph()
op = OpDataSelectionGroup( graph=graph )
op.WorkingDirectory.setValue( self.workingDir )
op.DatasetRoles.setValue( ['RoleA', 'RoleB', 'RoleC'] )
op.DatasetGroup.resize( 3 )
op.DatasetGroup[0].setValue( infoA )
# Leave RoleB blank -- datasets other than the first are optional
op.DatasetGroup[2].setValue( infoC )
assert op.ImageGroup[0].ready()
assert op.ImageGroup[2].ready()
expectedDataA = self.group1Data[0][1]
dataFromOpA = op.ImageGroup[0][:].wait()
assert dataFromOpA.dtype == expectedDataA.dtype
assert dataFromOpA.shape == expectedDataA.shape
assert (dataFromOpA == expectedDataA).all()
expectedDataC = self.group1Data[0][1]
dataFromOpC = op.ImageGroup[0][:].wait()
assert dataFromOpC.dtype == expectedDataC.dtype
assert dataFromOpC.shape == expectedDataC.shape
assert (dataFromOpC == expectedDataC).all()
assert op.Image.ready()
assert (op.Image[:].wait() == expectedDataA).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def _test_stack_along(self, name, extension, sequence_axis, expected):
fileName = os.path.join(self.tmpdir, f'{name}{extension}')
graph = lazyflow.graph.Graph()
reader = OpDataSelection(graph=graph, forceAxisOrder=False)
reader.WorkingDirectory.setValue(os.getcwd())
info = DatasetInfo(fileName, sequence_axis=sequence_axis)
reader.Dataset.setValue(info)
read = reader.Image[...].wait()
assert numpy.allclose(
read, expected), f'{name}: {read.shape}, {expected.shape}'
def testBasic3DcStackFromGlobString(self):
"""Test if stacked 2d 3-channel files are loaded correctly"""
# For some reason vigra saves 2D+c data compressed in gifs, so skip!
self.compressedExtensions.append('.gif')
for fileName in self.imgFileNameGlobs2Dc:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph, operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3Dc = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData3Dc.shape == self.imgData3Dc.shape, (imgData3Dc.shape, self.imgData3Dc.shape)
# skip this if image was saved compressed:
if any(x in fileName.lower() for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(fileName))
continue
numpy.testing.assert_array_equal(imgData3Dc, self.imgData3Dc)
def testProjectLocalData(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper( OpDataSelection, graph=graph )
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue( os.getcwd() )
reader.ProjectDataGroup.setValue( 'DataSelection/local_data' )
# Create a list of dataset infos . . .
datasetInfos = []
# From project
info = DatasetInfo()
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = "This string should be ignored..."
info._datasetId = 'dataset1' # (Cheating a bit here...)
info.invertColors = False
info.convertToGrayscale = False
datasetInfos.append(info)
reader.Dataset.setValues(datasetInfos)
projectInternalData = reader.Image[0][...].wait()
assert projectInternalData.shape == self.pngData.shape
assert (projectInternalData == self.pngData).all()
def testBasic3D(self):
"""Test if plane 2d files are loaded correctly"""
for fileName in self.imgFileNames3D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph, operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3D = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData3D.shape == self.imgData3D.shape, (imgData3D.shape, self.imgData3D.shape)
# skip this if image was saved compressed:
numpy.testing.assert_array_equal(imgData3D, self.imgData3D)
def testBasic3DstacksFromFileList(self):
for ext, fileNames in self.imgFileLists2D.items():
fileNameString = os.path.pathsep.join(fileNames)
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo(filepath=fileNameString)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData3D = reader.Image[0][...].wait()
# Check raw images
assert imgData3D.shape == self.imgData3D.shape
# skip this if image was saved compressed:
if any(
x.strip('.') in ext.lower()
for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(
ext))
continue
numpy.testing.assert_array_equal(imgData3D, self.imgData3D)
def testBasic2D(self):
"""Test if plane 2d files are loaded correctly"""
for fileName in self.imgFileNames2D:
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = fileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData2D = reader.Image[0][...].wait()
# Check the file name output
assert reader.ImageName[0].value == fileName
# Check raw images
assert imgData2D.shape == self.imgData2D.shape
# skip this if image was saved compressed:
if any(x in fileName.lower() for x in self.compressedExtensions):
print("Skipping raw comparison for compressed data: {}".format(fileName))
continue
numpy.testing.assert_array_equal(imgData2D, self.imgData2D)
def test_load_single_file_with_list(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection,
graph=graph,
operator_kwargs={'forceAxisOrder': False})
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
fileNameString = os.path.pathsep.join(self.file_names)
info = DatasetInfo(filepath=fileNameString)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData = reader.Image[0][...].wait()
print('imgData', reader.Image.meta.axistags,
reader.Image.meta.original_axistags)
# Check raw images
assert imgData.shape == self.imgData3Dct.shape, (
imgData.shape, self.imgData3Dct.shape)
numpy.testing.assert_array_equal(imgData, self.imgData3Dct)
def test_load_single_file_with_glob(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo(filepath=self.glob_string)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData = reader.Image[0][...].wait()
# Check raw images
assert imgData.shape == self.imgData3Dct.shape
numpy.testing.assert_array_equal(imgData, self.imgData3Dct)
def testNoChannelAxis(self):
"""
If we add a dataset that is missing a channel axis altogether,
the operator should automatically append a channel axis.
"""
noChannelFilename = os.path.join(self.workingDir, 'NoChannelAxis.npy')
noChannelData = numpy.random.random( (100,100) )
numpy.save(noChannelFilename, noChannelData)
info = DatasetInfo()
info.filePath = noChannelFilename
info.axistags = vigra.defaultAxistags('xy')
graph = Graph()
op = OpDataSelectionGroup( graph=graph )
op.WorkingDirectory.setValue( self.workingDir )
op.DatasetRoles.setValue( ['RoleA'] )
op.DatasetGroup.resize( 1 )
op.DatasetGroup[0].setValue( info )
assert op.ImageGroup[0].ready()
# Note that we expect a channel axis to be appended to the data.
expected_data = noChannelData[:,:,numpy.newaxis]
data_from_op = op.ImageGroup[0][:].wait()
assert data_from_op.dtype == expected_data.dtype
assert data_from_op.shape == expected_data.shape
assert (data_from_op == expected_data).all()
# op.Image is a synonym for op.ImageGroup[0]
assert op.Image.ready()
assert (op.Image[:].wait() == expected_data).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def testWeirdAxisInfos(self):
"""
If we add a dataset that has the channel axis in the wrong place,
the operator should automatically transpose it to be last.
"""
weirdAxisFilename = os.path.join(self.workingDir, 'WeirdAxes.npy')
weirdAxisData = numpy.random.random( (3,100,100) )
numpy.save(weirdAxisFilename, weirdAxisData)
info = DatasetInfo()
info.filePath = weirdAxisFilename
info.axistags = vigra.defaultAxistags('cxy')
graph = Graph()
op = OpDataSelectionGroup( graph=graph )
op.WorkingDirectory.setValue( self.workingDir )
op.DatasetRoles.setValue( ['RoleA'] )
op.DatasetGroup.resize( 1 )
op.DatasetGroup[0].setValue( info )
assert op.ImageGroup[0].ready()
# Note that we expect the channel axis to be transposed to be last.
expected_data = weirdAxisData.transpose( 1,2,0 )
data_from_op = op.ImageGroup[0][:].wait()
assert data_from_op.dtype == expected_data.dtype
assert data_from_op.shape == expected_data.shape
assert (data_from_op == expected_data).all()
# op.Image is a synonym for op.ImageGroup[0]
assert op.Image.ready()
assert (op.Image[:].wait() == expected_data).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def testNoChannelAxis(self):
"""
If we add a dataset that is missing a channel axis altogether,
the operator should automatically append a channel axis.
"""
noChannelFilename = os.path.join(self.workingDir, 'NoChannelAxis.npy')
noChannelData = numpy.random.random((100, 100))
numpy.save(noChannelFilename, noChannelData)
info = DatasetInfo()
info.filePath = noChannelFilename
info.axistags = vigra.defaultAxistags('xy')
graph = Graph()
op = OpDataSelectionGroup(graph=graph)
op.WorkingDirectory.setValue(self.workingDir)
op.DatasetRoles.setValue(['RoleA'])
op.DatasetGroup.resize(1)
op.DatasetGroup[0].setValue(info)
assert op.ImageGroup[0].ready()
# Note that we expect a channel axis to be appended to the data.
expected_data = noChannelData[:, :, numpy.newaxis]
data_from_op = op.ImageGroup[0][:].wait()
assert data_from_op.dtype == expected_data.dtype
assert data_from_op.shape == expected_data.shape
assert (data_from_op == expected_data).all()
# op.Image is a synonym for op.ImageGroup[0]
assert op.Image.ready()
assert (op.Image[:].wait() == expected_data).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def debug_with_new(shell):
"""
(Function for debug and testing.)
"""
projFilePath = "/magnetic/test_project.ilp"
# New project
shell.createAndLoadNewProject(projFilePath)
workflow = shell.projectManager.workflow
from ilastik.applets.dataSelection.opDataSelection import DatasetInfo
rawInfo = DatasetInfo()
rawInfo.filePath = '/magnetic/synapse_small.npy'
opDataSelection = workflow.rawDataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(rawInfo)
binaryInfo = DatasetInfo()
binaryInfo.filePath = '/magnetic/synapse_small_binary.npy'
opDataSelection = workflow.dataSelectionApplet.topLevelOperator
opDataSelection.Dataset.resize(1)
opDataSelection.Dataset[0].setValue(binaryInfo)
def _get_template_dataset_infos(self, input_axes=None, sequence_axis=None):
"""
Sometimes the default settings for an input file are not suitable (e.g. the axistags need to be changed).
We assume the LAST non-batch input in the workflow has settings that will work for all batch processing inputs.
Here, we get the DatasetInfo objects from that lane and store them as 'templates' to modify for all batch-
processing files.
"""
template_infos = {}
# If there isn't an available dataset to use as a template
if len(self.dataSelectionApplet.topLevelOperator.DatasetGroup) == 0:
num_roles = len(
self.dataSelectionApplet.topLevelOperator.DatasetRoles.value)
for role_index in range(num_roles):
template_infos[role_index] = DatasetInfo()
if input_axes:
template_infos[
role_index].axistags = vigra.defaultAxistags(
input_axes)
if sequence_axis:
template_infos[role_index].sequenceAxis = sequence_axis
return template_infos
# Use the LAST non-batch input file as our 'template' for DatasetInfo settings (e.g. axistags)
template_lane = len(
self.dataSelectionApplet.topLevelOperator.DatasetGroup) - 1
opDataSelectionTemplateView = self.dataSelectionApplet.topLevelOperator.getLane(
template_lane)
for role_index, info_slot in enumerate(
opDataSelectionTemplateView.DatasetGroup):
if info_slot.ready():
template_infos[role_index] = info_slot.value
else:
template_infos[role_index] = DatasetInfo()
if input_axes:
# Support the --input_axes arg to override input axis order, same as DataSelection applet.
template_infos[role_index].axistags = vigra.defaultAxistags(
input_axes)
if sequence_axis:
template_infos[role_index].sequenceAxis = sequence_axis
return template_infos
def test(self):
"""
Make sure that the dataset roles work the way we expect them to.
"""
infoA = DatasetInfo()
infoA.filePath = self.group1Data[0][0]
infoC = DatasetInfo()
infoC.filePath = self.group1Data[1][0]
graph = Graph()
op = OpDataSelectionGroup(graph=graph)
op.WorkingDirectory.setValue(self.workingDir)
op.DatasetRoles.setValue(['RoleA', 'RoleB', 'RoleC'])
op.DatasetGroup.resize(3)
op.DatasetGroup[0].setValue(infoA)
# Leave RoleB blank -- datasets other than the first are optional
op.DatasetGroup[2].setValue(infoC)
assert op.ImageGroup[0].ready()
assert op.ImageGroup[2].ready()
expectedDataA = self.group1Data[0][1]
dataFromOpA = op.ImageGroup[0][:].wait()
assert dataFromOpA.dtype == expectedDataA.dtype
assert dataFromOpA.shape == expectedDataA.shape
assert (dataFromOpA == expectedDataA).all()
expectedDataC = self.group1Data[0][1]
dataFromOpC = op.ImageGroup[0][:].wait()
assert dataFromOpC.dtype == expectedDataC.dtype
assert dataFromOpC.shape == expectedDataC.shape
assert (dataFromOpC == expectedDataC).all()
assert op.Image.ready()
assert (op.Image[:].wait() == expectedDataA).all()
# Ensure that files opened by the inner operators are closed before we exit.
op.DatasetGroup.resize(0)
def test_load_single_file_with_glob(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
info = DatasetInfo(filepath=self.glob_string)
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
reader.Dataset.setValues([info])
# Read the test files using the data selection operator and verify the contents
imgData = reader.Image[0][...].wait()
# Check raw images
assert imgData.shape == self.imgData3Dct.shape
numpy.testing.assert_array_equal(imgData, self.imgData3Dct)
def test(self):
infoA = DatasetInfo()
infoA.filePath = self.group1Data[0][0]
infoC = DatasetInfo()
infoC.filePath = self.group1Data[1][0]
graph = Graph()
op = OpDataSelectionGroup(graph=graph)
op.WorkingDirectory.setValue(self.workingDir)
op.DatasetRoles.setValue(['RoleA', 'RoleB', 'RoleC'])
op.DatasetGroup.resize(3)
op.DatasetGroup[0].setValue(infoA)
# Leave RoleB blank -- datasets other than the first are optional
op.DatasetGroup[2].setValue(infoC)
assert op.ImageGroup[0].ready()
assert op.ImageGroup[2].ready()
expectedDataA = self.group1Data[0][1]
dataFromOpA = op.ImageGroup[0][:].wait()
assert dataFromOpA.dtype == expectedDataA.dtype
assert dataFromOpA.shape == expectedDataA.shape
assert (dataFromOpA == expectedDataA).all()
expectedDataC = self.group1Data[0][1]
dataFromOpC = op.ImageGroup[0][:].wait()
assert dataFromOpC.dtype == expectedDataC.dtype
assert dataFromOpC.shape == expectedDataC.shape
assert (dataFromOpC == expectedDataC).all()
assert op.Image.ready()
assert (op.Image[:].wait() == expectedDataA).all()
def testBasic(self):
graph = lazyflow.graph.Graph()
reader = OperatorWrapper(OpDataSelection, graph=graph)
reader.ProjectFile.setValue(self.projectFile)
reader.WorkingDirectory.setValue(os.getcwd())
reader.ProjectDataGroup.setValue('DataSelection/local_data')
# Create a list of dataset infos . . .
datasetInfos = []
# npy
info = DatasetInfo()
# Will be read from the filesystem since the data won't be found in the project file.
info.location = DatasetInfo.Location.ProjectInternal
info.filePath = self.testNpyFileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
datasetInfos.append(info)
# png
info = DatasetInfo()
info.location = DatasetInfo.Location.FileSystem
info.filePath = self.testPngFileName
info.internalPath = ""
info.invertColors = False
info.convertToGrayscale = False
datasetInfos.append(info)
reader.Dataset.setValues(datasetInfos)
# Read the test files using the data selection operator and verify the contents
npyData = reader.Image[0][...].wait()
pngData = reader.Image[1][...].wait()
# Check the file name output
print reader.ImageName[0].value
assert reader.ImageName[0].value == self.testNpyFileName
assert reader.ImageName[1].value == self.testPngFileName
# Check raw images
assert npyData.shape == (10, 11, 1)
for x in range(npyData.shape[0]):
for y in range(npyData.shape[1]):
assert npyData[x, y, 0] == x + y
assert pngData.shape == (100, 200, 3)
for x in range(pngData.shape[0]):
for y in range(pngData.shape[1]):
for c in range(pngData.shape[2]):
assert pngData[x, y, c] == (x + y) % 256
def _run_export_with_empty_batch_lane(self, role_input_datas,
batch_lane_index, template_infos,
progress_callback, export_to_array):
"""
Configure the fresh batch lane with the given input files, and export the results.
role_input_datas: A list of str or DatasetInfo, one item for each dataset-role.
(For example, a workflow might have two roles: Raw Data and Binary Segmentation.)
batch_lane_index: The lane index used as the batch export lane.
template_infos: A dict of DatasetInfo objects.
Settings like axistags, etc. that cannot be automatically inferred
from the filepath will be copied from these template objects.
(See explanation in _get_template_dataset_infos(), above.)
progress_callback: Export progress for the current lane is reported via this callback.
"""
assert role_input_datas[
0], "At least one file must be provided for each dataset (the first role)."
opDataSelectionBatchLaneView = self.dataSelectionApplet.topLevelOperator.getLane(
batch_lane_index)
# Apply new settings for each role
for role_index, data_for_role in enumerate(role_input_datas):
if not data_for_role:
continue
if isinstance(data_for_role, DatasetInfo):
# Caller provided a pre-configured DatasetInfo instead of a just a path
info = data_for_role
else:
# Copy the template info, but override filepath, etc.
template_info = template_infos[role_index]
info = DatasetInfo.from_file_path(template_info, data_for_role)
# Force real data source when in headless mode.
# If raw data doesn't exist in headless mode, we use fake data reader
# (datasetInfo.realDataSource = False). Now we need to ensure that
# the flag is set to True for new image lanes.
info.realDataSource = True
# Apply to the data selection operator
opDataSelectionBatchLaneView.DatasetGroup[role_index].setValue(
info)
# Make sure nothing went wrong
opDataExportBatchlaneView = self.dataExportApplet.topLevelOperator.getLane(
batch_lane_index)
# New lanes were added.
# Give the workflow a chance to restore anything that was unecessarily invalidated (e.g. classifiers)
self.workflow().handleNewLanesAdded()
assert opDataExportBatchlaneView.ImageToExport.ready()
assert opDataExportBatchlaneView.ExportPath.ready()
# Call customization hook
self.dataExportApplet.prepare_lane_for_export(batch_lane_index)
# Finally, run the export
opDataExportBatchlaneView.progressSignal.subscribe(progress_callback)
if export_to_array:
logger.info("Exporting to in-memory array.")
result = opDataExportBatchlaneView.run_export_to_array()
else:
logger.info("Exporting to {}".format(
opDataExportBatchlaneView.ExportPath.value))
opDataExportBatchlaneView.run_export()
result = opDataExportBatchlaneView.ExportPath.value
# Call customization hook
self.dataExportApplet.post_process_lane_export(batch_lane_index)
return result
