def testBasic(self):
features = numpy.indices( (100,100) ).astype(numpy.float32) + 0.5
features = numpy.rollaxis(features, 0, 3)
features = vigra.taggedView(features, 'xyc')
labels = numpy.zeros( (100,100,1), dtype=numpy.uint8 )
labels = vigra.taggedView(labels, 'xyc')
labels[10,10] = 1
labels[10,11] = 1
labels[20,20] = 2
labels[20,21] = 2
graph = Graph()
opFeatureMatrixCache = OpFeatureMatrixCache(graph=graph)
opFeatureMatrixCache.FeatureImage.setValue(features)
opFeatureMatrixCache.LabelImage.setValue(labels)
opFeatureMatrixCache.LabelImage.setDirty( numpy.s_[10:11, 10:12] )
opFeatureMatrixCache.LabelImage.setDirty( numpy.s_[20:21, 20:22] )
opFeatureMatrixCache.LabelImage.setDirty( numpy.s_[30:31, 30:32] )
opTrain = OpTrainClassifierFromFeatureVectors( graph=graph )
opTrain.ClassifierFactory.setValue( ParallelVigraRfLazyflowClassifierFactory(100) )
opTrain.MaxLabel.setValue(2)
opTrain.LabelAndFeatureMatrix.connect( opFeatureMatrixCache.LabelAndFeatureMatrix )
assert opTrain.Classifier.ready()
trained_classifier = opTrain.Classifier.value
# This isn't much of a test at the moment...
assert isinstance( trained_classifier, ParallelVigraRfLazyflowClassifier ), \
"classifier is of the wrong type: {}".format(type(trained_classifier))
def testBasic(self):
features = numpy.indices((100, 100)).astype(numpy.float32) + 0.5
features = numpy.rollaxis(features, 0, 3)
features = vigra.taggedView(features, "xyc")
labels = numpy.zeros((100, 100, 1), dtype=numpy.uint8)
labels = vigra.taggedView(labels, "xyc")
labels[10, 10] = 1
labels[10, 11] = 1
labels[20, 20] = 2
labels[20, 21] = 2
graph = Graph()
# Use a cache for the labels so we can control the ideal_blockshape
# This ensures that the blockwise behavior is tested, even though we're
# testing with tiny data that would normally fall into a single block.
opLabelCache = OpBlockedArrayCache(graph=graph)
opLabelCache.BlockShape.setValue((10, 10, 1))
opLabelCache.Input.setValue(labels)
opFeatureMatrixCache = OpFeatureMatrixCache(graph=graph)
opFeatureMatrixCache.LabelImage.connect(opLabelCache.Output)
opFeatureMatrixCache.FeatureImage.setValue(features)
labels_and_features = opFeatureMatrixCache.LabelAndFeatureMatrix.value
assert labels_and_features.shape == (
0, 3), "Empty feature matrix has wrong shape: {}".format(
labels_and_features.shape)
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[10:11, 10:12])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[20:21, 20:22])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[30:31, 30:32])
labels_and_features = opFeatureMatrixCache.LabelAndFeatureMatrix.value
assert labels_and_features.shape == (4, 3)
assert (labels_and_features[:, 0] == 1).sum() == 2
assert (labels_and_features[:, 0] == 2).sum() == 2
# Can't check for equality because feature blocks can be in a random order.
# Just check that all features are present, regardless of order.
for feature_vec in [[10.5, 10.5], [10.5, 11.5], [20.5, 20.5],
[20.5, 21.5]]:
assert feature_vec in labels_and_features[:, 1:]
def _getMatrixOp(self, graph):
features = numpy.indices((100, 100)).astype(numpy.float32) + 0.5
features = numpy.rollaxis(features, 0, 3)
features = vigra.taggedView(features, "xyc")
labels = numpy.zeros((100, 100, 1), dtype=numpy.uint8)
labels = vigra.taggedView(labels, "xyc")
labels[10, 10] = 1
labels[10, 11] = 1
labels[20, 20] = 2
labels[20, 21] = 2
opFeatureMatrixCache = OpFeatureMatrixCache(graph=graph)
opFeatureMatrixCache.FeatureImage.setValue(features)
opFeatureMatrixCache.LabelImage.setValue(labels)
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[10:11, 10:12])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[20:21, 20:22])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[30:31, 30:32])
return opFeatureMatrixCache
def testBasic(self):
features = numpy.indices((100, 100)).astype(numpy.float) + 0.5
features = numpy.rollaxis(features, 0, 3)
features = vigra.taggedView(features, 'xyc')
labels = numpy.zeros((100, 100, 1), dtype=numpy.uint8)
labels = vigra.taggedView(labels, 'xyc')
labels[10, 10] = 1
labels[10, 11] = 1
labels[20, 20] = 2
labels[20, 21] = 2
graph = Graph()
opFeatureMatrixCache = OpFeatureMatrixCache(graph=graph)
opFeatureMatrixCache.FeatureImage.setValue(features)
opFeatureMatrixCache.LabelImage.setValue(labels)
opFeatureMatrixCache.NonZeroLabelBlocks.setValue(0)
labels_and_features = opFeatureMatrixCache.LabelAndFeatureMatrix.value
assert labels_and_features.shape == (
0, 3), "Empty feature matrix has wrong shape: {}".format(
labels_and_features.shape)
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[10:11, 10:12])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[20:21, 20:22])
opFeatureMatrixCache.LabelImage.setDirty(numpy.s_[30:31, 30:32])
labels_and_features = opFeatureMatrixCache.LabelAndFeatureMatrix.value
assert labels_and_features.shape == (4, 3)
assert (labels_and_features[:, 0] == 1).sum() == 2
assert (labels_and_features[:, 0] == 2).sum() == 2
# Can't check for equality because feature blocks can be in a random order.
# Just check that all features are present, regardless of order.
for feature_vec in [[10.5, 10.5], [10.5, 11.5], [20.5, 20.5],
[20.5, 21.5]]:
assert feature_vec in labels_and_features[:, 1:]