def _get_available_classifier_factories(self):
# FIXME: Replace this logic with a proper plugin mechanism
from lazyflow.classifiers import VigraRfLazyflowClassifierFactory, SklearnLazyflowClassifierFactory, \
ParallelVigraRfLazyflowClassifierFactory, VigraRfPixelwiseClassifierFactory,\
LazyflowVectorwiseClassifierFactoryABC, LazyflowPixelwiseClassifierFactoryABC
classifiers = OrderedDict()
classifiers["Parallel Random Forest (VIGRA)"] = ParallelVigraRfLazyflowClassifierFactory(100)
try:
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.lda import LDA
from sklearn.qda import QDA
from sklearn.svm import SVC, NuSVC
classifiers["Random Forest (scikit-learn)"] = SklearnLazyflowClassifierFactory( RandomForestClassifier, 100 )
classifiers["Gaussian Naive Bayes (scikit-learn)"] = SklearnLazyflowClassifierFactory( GaussianNB )
classifiers["AdaBoost (scikit-learn)"] = SklearnLazyflowClassifierFactory( AdaBoostClassifier, n_estimators=100 )
classifiers["Single Decision Tree (scikit-learn)"] = SklearnLazyflowClassifierFactory( DecisionTreeClassifier, max_depth=5 )
classifiers["K-Neighbors (scikit-learn)"] = SklearnLazyflowClassifierFactory( KNeighborsClassifier )
classifiers["LDA (scikit-learn)"] = SklearnLazyflowClassifierFactory( LDA )
classifiers["QDA (scikit-learn)"] = SklearnLazyflowClassifierFactory( QDA )
classifiers["SVM C-Support (scikit-learn)"] = SklearnLazyflowClassifierFactory( SVC, probability=True )
classifiers["SVM Nu-Support (scikit-learn)"] = SklearnLazyflowClassifierFactory( NuSVC, probability=True )
except ImportError:
import warnings
warnings.warn("Couldn't import sklearn. Scikit-learn classifiers not available.")
# Debug classifiers
classifiers["Parallel Random Forest with Variable Importance (VIGRA)"] = ParallelVigraRfLazyflowClassifierFactory(100, variable_importance_enabled=True)
classifiers["(debug) Single-threaded Random Forest (VIGRA)"] = VigraRfLazyflowClassifierFactory(100)
classifiers["(debug) Pixelwise Random Forest (VIGRA)"] = VigraRfPixelwiseClassifierFactory(100)
return classifiers
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()
opTrain = OpTrainPixelwiseClassifierBlocked(graph=graph)
opTrain.ClassifierFactory.setValue(
VigraRfPixelwiseClassifierFactory(10))
opTrain.Images.resize(1)
opTrain.Labels.resize(1)
opTrain.nonzeroLabelBlocks.resize(1)
opTrain.Images[0].setValue(features)
opTrain.Labels[0].setValue(labels)
nonzero_slicings = [
numpy.s_[10:20, 10:20, 0:1], numpy.s_[20:30, 20:30, 0:1]
]
opTrain.nonzeroLabelBlocks[0].setValue(nonzero_slicings)
opTrain.MaxLabel.setValue(2)
opTrain.Labels[0].setDirty(numpy.s_[10:11, 10:12])
opTrain.Labels[0].setDirty(numpy.s_[20:21, 20:22])
opTrain.Labels[0].setDirty(numpy.s_[30:31, 30:32])
trained_classifier = opTrain.Classifier.value
# This isn't much of a test at the moment...
assert isinstance(trained_classifier, VigraRfPixelwiseClassifier)
# Try predicting while we're at it...
opPredict = OpPixelwiseClassifierPredict(graph=graph)
opPredict.Image.connect(opTrain.Images[0])
opPredict.LabelsCount.connect(opTrain.MaxLabel)
opPredict.Classifier.connect(opTrain.Classifier)
predictions = opPredict.PMaps[:].wait()
assert predictions.shape == features.shape[:-1] + (
2, ) # We used 2 input labels above.