def onNewImage(self, dataItemImage):
dataItemImage.Classification.setModuleMgr(self)
#create featureM
dataItemImage.module["Classification"]["featureM"] = numpy.zeros(dataItemImage.shape[0:-1] + (self.featureMgr.totalFeatureSize,),'float32')
#clear features and training
self.classificationMgr.clearFeaturesAndTrainingForImage(dataItemImage)
#handle obsolete file formats:
if dataItemImage.Classification["labels"] is not None:
labels = dataItemImage.Classification["labels"]
ov = overlayMgr.OverlayItem(labels._data, alpha = 1.0, colorTable = labels.getColorTab(), autoAdd = True, autoVisible = True, autoAlphaChannel = False, linkColorTable = True)
dataItemImage.overlayMgr["Classification/Labels"] = ov
if len(self.dataMgr.module["Classification"]["labelDescriptions"]) == 0:
for d in labels.descriptions:
self.dataMgr.module["Classification"]["labelDescriptions"].append(d)
dataItemImage.Classification["labels"] = None
#create LabelOverlay
if dataItemImage.overlayMgr["Classification/Labels"] is None:
data = numpy.zeros(dataItemImage.shape[0:-1]+(1,),'uint8')
ov = overlayMgr.OverlayItem(data, color = 0, alpha = 1.0, colorTable = self.dataMgr.module["Classification"]["labelDescriptions"].getColorTab(), autoAdd = True, autoVisible = True, linkColorTable = True)
dataItemImage.overlayMgr["Classification/Labels"] = ov
#be able to show the labels in 3D
dataItemImage.overlayMgr["Classification/Labels"].displayable3D = True
dataItemImage.overlayMgr["Classification/Labels"].backgroundClasses = set([0])
dataItemImage.overlayMgr["Classification/Labels"].smooth3D = False
#add the raw data
if dataItemImage.overlayMgr["Raw Data"] is not None:
dataItemImage.Classification.addOverlayRef(dataItemImage.overlayMgr["Raw Data"].getRef())
if dataItemImage.Classification["prediction"] is not None:
prediction = dataItemImage.Classification["prediction"]
for index, descr in enumerate(self.dataMgr.module["Classification"]["labelDescriptions"]):
ov = overlayMgr.OverlayItem(DataAccessor(prediction[:,:,:,:,index:index+1], channels = False), color = long(descr.color), alpha = 0.4, colorTable = None, autoAdd = True, autoVisible = True, min = 0, max = 1.0)
ov.setColorGetter(descr.getColor, descr)
dataItemImage.overlayMgr["Classification/Prediction/" + descr.name] = ov
margin = activeLearning.computeEnsembleMargin(prediction[:,:,:,:,:])
ov = overlayMgr.OverlayItem(DataAccessor(margin), alpha = 1.0, color = long(65535)<<16, colorTable = None, autoAdd = True, autoVisible = True, min = 0, max = 1.0)
dataItemImage.overlayMgr["Classification/Uncertainty"] = ov
dataItemImage.Classification["prediction"] = None
# if self._dataVol.uncertainty is not None:
# #create Overlay for uncertainty:
# ov = overlayMgr.OverlayItem(self._dataVol.uncertainty, color = QtGui.QColor(255, 0, 0), alpha = 1.0, colorTable = None, autoAdd = True, autoVisible = False)
# self.overlayMgr["Classification/Uncertainty"] = ov
#calculate features for the image
featureProcess = featureMgr.FeatureThread(self.featureMgr, self.dataMgr, [dataItemImage])
featureProcess.start()
featureProcess.wait()
def generateOverlays(self, activeImage = None):
for itemindex, activeItem in enumerate(self.dataMgr):
prediction = self._prediction
descriptions = self.dataMgr.module["Classification"]["labelDescriptions"]
classifiers = self.dataMgr.module["Classification"]["classificationMgr"].classifiers
if prediction is not None and prediction[itemindex] is not None:
foregrounds = []
for p_i, p_num in enumerate(classifiers[0].unique_vals):
#create Overlay for _prediction:
ov = overlayMgr.OverlayItem(prediction[itemindex][:,:,:,:,p_i], color = long(descriptions[p_num-1].color), alpha = 0.4, colorTable = None, autoAdd = True, autoVisible = True, min = 0, max = 1)
ov.setColorGetter(descriptions[p_num-1].getColor, descriptions[p_num-1])
activeItem.overlayMgr["Classification/Prediction/" + descriptions[p_num-1].name] = ov
ov = activeItem.overlayMgr["Classification/Prediction/" + descriptions[p_num-1].name]
ov.setColorGetter(descriptions[p_num-1].getColor, descriptions[p_num-1])
foregrounds.append(ov)
import ilastik.core.overlays.thresholdOverlay as tho
if len(foregrounds) > 1:
if activeItem.overlayMgr["Classification/Segmentation"] is None:
ov = tho.ThresholdOverlay(foregrounds, [], autoAdd = True, autoVisible = True)
activeItem.overlayMgr["Classification/Segmentation"] = ov
else:
ov = activeItem.overlayMgr["Classification/Segmentation"]
ov.setForegrounds(foregrounds)
all = range(len(descriptions))
if len(classifiers) > 0:
not_predicted = numpy.setdiff1d(all, classifiers[0].unique_vals - 1)
if len(not_predicted) > 0:
print not_predicted
for p_i, p_num in enumerate(not_predicted):
if activeItem.overlayMgr["Classification/Prediction/" + descriptions[p_num].name] is not None:
print "clearing prediction for unused Label ", p_num
activeItem.overlayMgr["Classification/Prediction/" + descriptions[p_num].name][:,:,:,:,:] = 0
margin = activeLearning.computeEnsembleMargin(prediction[itemindex][:,:,:,:,:])
#create Overlay for uncertainty:
ov = overlayMgr.OverlayItem(margin, color = long(65535 << 16), alpha = 1.0, colorTable = None, autoAdd = True, autoVisible = False, min = 0, max = 1)
activeItem.overlayMgr["Classification/Uncertainty"] = ov
else:
print "Prediction for item ", itemindex, "is None, not generating Overlays"
