def __init__(self, rag, ndim=3, axis=2):
self.rag = rag
self.labels = rag.labels
self.featureExtractor = graphs.gridRagFeatureExtractor(self.rag, self.labels)
self.featureExtractor.labels = self.labels
self.featureExtractor.graph = self.rag
self.shape = self.labels.shape
self.blocking2d = Blocking2d(self.shape[0:2], (40,40))
self.ndim = ndim
self.axis = axis
assert len(self.shape) == ndim
self.nSlices = 1
if ndim == 3:
self.nSlices = self.shape[axis]
self.sliceDict = None
# qt gui
self.win = QtGui.QMainWindow()
self.win.setWindowTitle('pyqtgraph example: ImageItem')
self.win.show()
self.win.resize(800, 600)
self.cw = QtGui.QWidget()
self.cw.setMouseTracking(True)
self.win.setCentralWidget(self.cw)
self.layout = QtGui.QHBoxLayout()
self.layout2 = QtGui.QVBoxLayout()
self.cw.setLayout(self.layout)
self.gv = pg.GraphicsLayoutWidget()
self.gv.scene()
self.gv.setSizePolicy(QtGui.QSizePolicy.Preferred, QtGui.QSizePolicy.Expanding)
self.gv.setMouseTracking(True)
self.layerCtrl = BvLayerCtrl()
self.layout.addLayout(self.layout2)
self.layout.addWidget(self.layerCtrl)
self.layout2.addWidget(self.gv,3)
self.viewBox = BvGridViewBox(blocking2d=self.blocking2d)
#self.viewBox.setMouseTracking(True)
self.gv.addItem(self.viewBox)
self.viewBox.setAspectLocked(True)
self.edgeClickLabels = dict()
def scrolled(d):
if d>0:
d=5
else :
d=-5
if self.ndim == 3:
newSlice = min(self.nSlices-1, self.currentSlice - d)
newSlice = max(0, newSlice)
self.currentSlice = newSlice
with vigra.Timer("scroll"):
self.setZ(self.currentSlice)
self.viewBox.sigScrolled.connect(scrolled)
self.ctrlWidget = DownCtrl()
self.ctrlWidget.setSizePolicy(QtGui.QSizePolicy.Preferred, QtGui.QSizePolicy.Minimum)
self.layout2.insertStretch(1,0)
self.layout2.addWidget(self.ctrlWidget)
self.ctrlWidget.modeSelectorComboBox.currentIndexChanged.connect(self.updatePens)
self.ctrlWidget.brushSizeSlider.sliderMoved.connect(self.changeLineSize)
self.ctrlWidget.saveLabelsButton.clicked.connect(self.onClickedSaveLabels)
self.ctrlWidget.loadLabelsButton.clicked.connect(self.onClickedLoadLabels)
self.ctrlWidget.computeFeaturesButton.clicked.connect(self.onClickedComputeFeatures)
self.ctrlWidget.saveFeaturesButton.clicked.connect(self.onClickedSaveFeatures)
self.ctrlWidget.loadFeaturesButton.clicked.connect(self.onClickedLoadFeatures)
self.ctrlWidget.featureGradientWidget.sigGradientChanged.connect(self.onGradientChanged)
self.ctrlWidget.trainRfButton.clicked.connect(self.onClickedTrainRf)
self.ctrlWidget.predictProbsButton.clicked.connect(self.onClickedPredictProbs)
self.ctrlWidget.predictMcButton.clicked.connect(self.onClickedMulticut)
self.ctrlWidget.saveRfButton.clicked.connect(self.onClickedSaveRf)
self.ctrlWidget.loadRfButton.clicked.connect(self.onClickedLoadRf)
self.layerCtrl.sigFeatureSelected.connect(self.onFeatureSelected)
self.imgItem = pg.ImageItem(border='w')
self.viewBox.addItem(self.imgItem)
self.dataDict = dict()
self.curves = []
self.allCurves = None
self.currentSlice = 0
self.pathHint = None
self.currentFeatures = None
self.featureMinMax = None
self.currentFi = 0
self.rf = None
self.probs = None
self.nCurves = 0
self.ScrollLock = threading.Lock()
self.scrollingIsLocked = False
def makeFeat(rag, raw, labels, show=True):
featureExtractor = graphs.gridRagFeatureExtractor(rag, labels)
featureExtractor.labels = labels
featureExtractor.graph = rag
geoFeat = featureExtractor.geometricFeatures()
topoFeat = featureExtractor.topologicalFeatures()
features = [geoFeat, topoFeat]
# ward facs
wardness = numpy.array([0.0, 0.1, 0.15, 0.25, 0.5, 1.0], dtype="float32")
accFeat = featureExtractor.accumulatedFeatures(raw)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.filters.gaussianGradientMagnitude(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.filters.laplacianOfGaussian(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.gaussianSmoothing(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
# st ev
for s in [2.0, 3.0, 4.0]:
res = stEv(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
mean = accFeat[:, 0]
ucm = featureExtractor.ucmTransformFeatures(mean[:, None], wardness)
features.extend([accFeat, ucm])
if False:
for x in range(ucm.shape[1]):
rag.showEdgeFeature(raw, ucm[:, x])
vigra.show()
# hessian ev
for s in [1.0, 3.0, 4.0, 6.0, 8.0]:
img = hessianEv2(raw, s, 2.0)
# vigra.imshow(img)
# vigra.show()
accFeat = featureExtractor.accumulatedFeatures(img)
mean = accFeat[:, 0]
ucm = featureExtractor.ucmTransformFeatures(mean[:, None], wardness)
features.extend([accFeat, ucm])
if False:
for x in range(ucm.shape[1]):
print "x", x
rag.showEdgeFeature(raw, ucm[:, x])
vigra.show()
# break
#
features = numpy.concatenate(features, axis=1)
return features
def __init__(self, rag, ndim=3, axis=2):
self.rag = rag
self.labels = rag.labels
self.featureExtractor = graphs.gridRagFeatureExtractor(
self.rag, self.labels)
self.featureExtractor.labels = self.labels
self.featureExtractor.graph = self.rag
self.shape = self.labels.shape
self.blocking2d = Blocking2d(self.shape[0:2], (40, 40))
self.ndim = ndim
self.axis = axis
assert len(self.shape) == ndim
self.nSlices = 1
if ndim == 3:
self.nSlices = self.shape[axis]
self.sliceDict = None
# qt gui
self.win = QtGui.QMainWindow()
self.win.setWindowTitle('pyqtgraph example: ImageItem')
self.win.show()
self.win.resize(800, 600)
self.cw = QtGui.QWidget()
self.cw.setMouseTracking(True)
self.win.setCentralWidget(self.cw)
self.layout = QtGui.QHBoxLayout()
self.layout2 = QtGui.QVBoxLayout()
self.cw.setLayout(self.layout)
self.gv = pg.GraphicsLayoutWidget()
self.gv.scene()
self.gv.setSizePolicy(QtGui.QSizePolicy.Preferred,
QtGui.QSizePolicy.Expanding)
self.gv.setMouseTracking(True)
self.layerCtrl = BvLayerCtrl()
self.layout.addLayout(self.layout2)
self.layout.addWidget(self.layerCtrl)
self.layout2.addWidget(self.gv, 3)
self.viewBox = BvGridViewBox(blocking2d=self.blocking2d)
#self.viewBox.setMouseTracking(True)
self.gv.addItem(self.viewBox)
self.viewBox.setAspectLocked(True)
self.edgeClickLabels = dict()
def scrolled(d):
if d > 0:
d = 5
else:
d = -5
if self.ndim == 3:
newSlice = min(self.nSlices - 1, self.currentSlice - d)
newSlice = max(0, newSlice)
self.currentSlice = newSlice
with vigra.Timer("scroll"):
self.setZ(self.currentSlice)
self.viewBox.sigScrolled.connect(scrolled)
self.ctrlWidget = DownCtrl()
self.ctrlWidget.setSizePolicy(QtGui.QSizePolicy.Preferred,
QtGui.QSizePolicy.Minimum)
self.layout2.insertStretch(1, 0)
self.layout2.addWidget(self.ctrlWidget)
self.ctrlWidget.modeSelectorComboBox.currentIndexChanged.connect(
self.updatePens)
self.ctrlWidget.brushSizeSlider.sliderMoved.connect(
self.changeLineSize)
self.ctrlWidget.saveLabelsButton.clicked.connect(
self.onClickedSaveLabels)
self.ctrlWidget.loadLabelsButton.clicked.connect(
self.onClickedLoadLabels)
self.ctrlWidget.computeFeaturesButton.clicked.connect(
self.onClickedComputeFeatures)
self.ctrlWidget.saveFeaturesButton.clicked.connect(
self.onClickedSaveFeatures)
self.ctrlWidget.loadFeaturesButton.clicked.connect(
self.onClickedLoadFeatures)
self.ctrlWidget.featureGradientWidget.sigGradientChanged.connect(
self.onGradientChanged)
self.ctrlWidget.trainRfButton.clicked.connect(self.onClickedTrainRf)
self.ctrlWidget.predictProbsButton.clicked.connect(
self.onClickedPredictProbs)
self.ctrlWidget.predictMcButton.clicked.connect(self.onClickedMulticut)
self.ctrlWidget.saveRfButton.clicked.connect(self.onClickedSaveRf)
self.ctrlWidget.loadRfButton.clicked.connect(self.onClickedLoadRf)
self.layerCtrl.sigFeatureSelected.connect(self.onFeatureSelected)
self.imgItem = pg.ImageItem(border='w')
self.viewBox.addItem(self.imgItem)
self.dataDict = dict()
self.curves = []
self.allCurves = None
self.currentSlice = 0
self.pathHint = None
self.currentFeatures = None
self.featureMinMax = None
self.currentFi = 0
self.rf = None
self.probs = None
self.nCurves = 0
self.ScrollLock = threading.Lock()
self.scrollingIsLocked = False
# parameter:
imPath = ('holyRegion.h5', 'im') # input image path
labPath = ('segMaskOnly.h5', 'data') # labeled image path
# load volume
labels = vigra.impex.readHDF5(*labPath).astype(np.uint32)#[:,:,0:20]
volume = vigra.impex.readHDF5(*imPath)#[:,:,0:20]
volume = volume.astype('float32')
gridGraph = graphs.gridGraph(labels.shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
featureExtractor = graphs.gridRagFeatureExtractor(rag, labels)
accSimpel = featureExtractor.accumulatedFeaturesSimple
with vigra.Timer("acc raw"):
miMa = float(volume.min()),float(volume.max())
accFeatRaw = featureExtractor.accumulatedFeatures(volume,miMa[0],miMa[1])
with vigra.Timer("acc grad"):
grad = vigra.filters.gaussianGradientMagnitude(volume,1.0)
accFeatGrad = accSimpel(grad)
from matplotlib.widgets import Slider, Button, RadioButtons
# parameter:
imPath = ('holyRegion.h5', 'im') # input image path
labPath = ('segMaskOnly.h5', 'data') # labeled image path
# load volume
labels = vigra.impex.readHDF5(*labPath).astype(np.uint32) #[:,:,0:20]
volume = vigra.impex.readHDF5(*imPath) #[:,:,0:20]
volume = volume.astype('float32')
gridGraph = graphs.gridGraph(labels.shape)
rag = graphs.regionAdjacencyGraph(gridGraph, labels)
featureExtractor = graphs.gridRagFeatureExtractor(rag, labels)
accSimpel = featureExtractor.accumulatedFeaturesSimple
with vigra.Timer("acc raw"):
miMa = float(volume.min()), float(volume.max())
accFeatRaw = featureExtractor.accumulatedFeatures(volume, miMa[0], miMa[1])
with vigra.Timer("acc grad"):
grad = vigra.filters.gaussianGradientMagnitude(volume, 1.0)
accFeatGrad = accSimpel(grad)
with vigra.Timer("acc hessian"):
grad = vigra.filters.hessianOfGaussianEigenvalues(volume, 1.0)[:, :, 0]
accFeatHess = accSimpel(grad)
gui = vigra.graphs.TinyEdgeLabelGui(rag=rag,
def makeFeat(rag, raw, labels, show=True):
featureExtractor = graphs.gridRagFeatureExtractor(rag, labels)
featureExtractor.labels = labels
featureExtractor.graph = rag
geoFeat = featureExtractor.geometricFeatures()
topoFeat = featureExtractor.topologicalFeatures()
features = [geoFeat, topoFeat]
# ward facs
wardness = numpy.array([0.0, 0.1, 0.15, 0.25, 0.5, 1.0], dtype='float32')
accFeat = featureExtractor.accumulatedFeatures(raw)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.filters.gaussianGradientMagnitude(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.filters.laplacianOfGaussian(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
for s in [2.0, 3.0, 4.0]:
res = vigra.gaussianSmoothing(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
features.append(accFeat)
# st ev
for s in [2.0, 3.0, 4.0]:
res = stEv(raw, s)
accFeat = featureExtractor.accumulatedFeatures(res)
mean = accFeat[:, 0]
ucm = featureExtractor.ucmTransformFeatures(mean[:, None], wardness)
features.extend([accFeat, ucm])
if False:
for x in range(ucm.shape[1]):
rag.showEdgeFeature(raw, ucm[:, x])
vigra.show()
# hessian ev
for s in [1.0, 3.0, 4.0, 6.0, 8.0]:
img = hessianEv2(raw, s, 2.0)
#vigra.imshow(img)
#vigra.show()
accFeat = featureExtractor.accumulatedFeatures(img)
mean = accFeat[:, 0]
ucm = featureExtractor.ucmTransformFeatures(mean[:, None], wardness)
features.extend([accFeat, ucm])
if False:
for x in range(ucm.shape[1]):
print "x", x
rag.showEdgeFeature(raw, ucm[:, x])
vigra.show()
#break
#
features = numpy.concatenate(features, axis=1)
return features
