def _add_grayscale_layer(self, data, name=None, visible=False): ''' adds a grayscale layer to the layerstack :param data: numpy array (2D) containing the data :param name: name of layer :param visible: bool determining whether this layer should be set to visible :return: ''' #assert len(data.shape) == 2 a, data_shape = createDataSource(data, True) self.editor.dataShape = list(data_shape) new_layer = GrayscaleLayer(a) new_layer.visible = visible if name is not None: new_layer.name = name self.layerstack.append(new_layer)
def _add_grayscale_layer(self, data, name=None, visible=False): ''' adds a grayscale layer to the layerstack :param data: numpy array (2D) containing the data :param name: name of layer :param visible: bool determining whether this layer should be set to visible :return: ''' #assert len(data.shape) == 2 a, data_shape = createDataSource(data, True) self.editor.dataShape = list(data_shape) new_layer = GrayscaleLayer(a) new_layer.visible = visible if name is not None: new_layer.name = name self.layerstack.append(new_layer)
def setupLayers(self): layers = [] op = self.topLevelOperatorView ravelerLabelsSlot = op.RavelerLabels if ravelerLabelsSlot.ready(): colortable = [] for _ in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) ravelerLabelLayer = ColortableLayer(LazyflowSource(ravelerLabelsSlot), colortable, direct=True) ravelerLabelLayer.name = "Raveler Labels" ravelerLabelLayer.visible = False ravelerLabelLayer.opacity = 0.4 layers.append(ravelerLabelLayer) def addFragmentSegmentationLayers(mslot, name): if mslot.ready(): for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label colortable = map(QColor.rgba, self._fragmentColors) fragSegLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) fragSegLayer.name = "{} #{} ({})".format( name, index, raveler_label ) fragSegLayer.visible = False fragSegLayer.opacity = 1.0 layers.append(fragSegLayer) addFragmentSegmentationLayers( op.FragmentedBodies, "Saved Fragments" ) addFragmentSegmentationLayers( op.RelabeledFragments, "Relabeled Fragments" ) addFragmentSegmentationLayers( op.FilteredFragmentedBodies, "CC-Filtered Fragments" ) addFragmentSegmentationLayers( op.WatershedFilledBodies, "Watershed-filled Fragments" ) mslot = op.EditedRavelerBodies for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label # 0=Black, 1=Transparent colortable = [QColor(0, 0, 0).rgba(), QColor(0, 0, 0, 0).rgba()] bodyMaskLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) bodyMaskLayer.name = "Raveler Body Mask #{} ({})".format( index, raveler_label ) bodyMaskLayer.visible = False bodyMaskLayer.opacity = 1.0 layers.append(bodyMaskLayer) finalSegSlot = op.FinalSegmentation if finalSegSlot.ready(): colortable = [] for _ in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) finalLayer = ColortableLayer(LazyflowSource(finalSegSlot), colortable, direct=True) finalLayer.name = "Final Segmentation" finalLayer.visible = False finalLayer.opacity = 0.4 layers.append(finalLayer) inputSlot = op.InputData if inputSlot.ready(): layer = GrayscaleLayer( LazyflowSource(inputSlot) ) layer.name = "WS Input" layer.visible = False layer.opacity = 1.0 layers.append(layer) #raw data rawSlot = self.topLevelOperatorView.RawData rawLayer = None if rawSlot.ready(): raw5D = self.topLevelOperatorView.RawData.value rawLayer = GrayscaleLayer(ArraySource(raw5D), direct=True) #rawLayer = GrayscaleLayer( LazyflowSource(rawSlot) ) rawLayer.name = "raw" rawLayer.visible = True rawLayer.opacity = 1.0 rawLayer.shortcutRegistration = ( "g", ShortcutManager.ActionInfo( "Postprocessing", "Raw Data to Top", "Raw Data to Top", partial(self._toggleRawDataPosition, rawLayer), self.viewerControlWidget(), rawLayer ) ) layers.append(rawLayer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView ravelerLabelsSlot = op.RavelerLabels if ravelerLabelsSlot.ready(): colortable = [] for _ in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) ravelerLabelLayer = ColortableLayer(LazyflowSource(ravelerLabelsSlot), colortable, direct=True) ravelerLabelLayer.name = "Raveler Labels" ravelerLabelLayer.visible = False ravelerLabelLayer.opacity = 0.4 layers.append(ravelerLabelLayer) supervoxelsSlot = op.Supervoxels if supervoxelsSlot.ready(): colortable = [] for i in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) supervoxelsLayer = ColortableLayer(LazyflowSource(supervoxelsSlot), colortable) supervoxelsLayer.name = "Input Supervoxels" supervoxelsLayer.visible = False supervoxelsLayer.opacity = 1.0 layers.append(supervoxelsLayer) def addFragmentSegmentationLayers(mslot, name): if mslot.ready(): for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label colortable = [] for i in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) colortable[0] = QColor(0,0,0,0).rgba() fragSegLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) fragSegLayer.name = "{} #{} ({})".format( name, index, raveler_label ) fragSegLayer.visible = False fragSegLayer.opacity = 1.0 layers.append(fragSegLayer) addFragmentSegmentationLayers( op.MaskedSupervoxels, "Masked Supervoxels" ) addFragmentSegmentationLayers( op.FilteredMaskedSupervoxels, "Filtered Masked Supervoxels" ) addFragmentSegmentationLayers( op.HoleFilledSupervoxels, "Hole Filled Supervoxels" ) addFragmentSegmentationLayers( op.RelabeledSupervoxels, "Relabeled Supervoxels" ) mslot = op.EditedRavelerBodies for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label # 0=Black, 1=Transparent colortable = [QColor(0, 0, 0).rgba(), QColor(0, 0, 0, 0).rgba()] bodyMaskLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) bodyMaskLayer.name = "Raveler Body Mask #{} ({})".format( index, raveler_label ) bodyMaskLayer.visible = False bodyMaskLayer.opacity = 1.0 layers.append(bodyMaskLayer) finalSegSlot = op.FinalSupervoxels if finalSegSlot.ready(): colortable = [] for _ in range(256): r,g,b = numpy.random.randint(0,255), numpy.random.randint(0,255), numpy.random.randint(0,255) colortable.append(QColor(r,g,b).rgba()) finalLayer = ColortableLayer(LazyflowSource(finalSegSlot), colortable) finalLayer.name = "Final Supervoxels" finalLayer.visible = False finalLayer.opacity = 0.4 layers.append(finalLayer) inputSlot = op.InputData if inputSlot.ready(): layer = GrayscaleLayer( LazyflowSource(inputSlot) ) layer.name = "WS Input" layer.visible = False layer.opacity = 1.0 layers.append(layer) #raw data rawSlot = self.topLevelOperatorView.RawData if rawSlot.ready(): raw5D = self.topLevelOperatorView.RawData.value layer = GrayscaleLayer(ArraySource(raw5D), direct=True) #layer = GrayscaleLayer( LazyflowSource(rawSlot) ) layer.name = "raw" layer.visible = True layer.opacity = 1.0 layers.append(layer) return layers
def setupLayers(self): layers = [] op = self.topLevelOperatorView ravelerLabelsSlot = op.RavelerLabels if ravelerLabelsSlot.ready(): colortable = [] for _ in range(256): r, g, b = numpy.random.randint(0, 255), numpy.random.randint( 0, 255), numpy.random.randint(0, 255) colortable.append(QColor(r, g, b).rgba()) ravelerLabelLayer = ColortableLayer( LazyflowSource(ravelerLabelsSlot), colortable, direct=True) ravelerLabelLayer.name = "Raveler Labels" ravelerLabelLayer.visible = False ravelerLabelLayer.opacity = 0.4 layers.append(ravelerLabelLayer) supervoxelsSlot = op.Supervoxels if supervoxelsSlot.ready(): colortable = [] for i in range(256): r, g, b = numpy.random.randint(0, 255), numpy.random.randint( 0, 255), numpy.random.randint(0, 255) colortable.append(QColor(r, g, b).rgba()) supervoxelsLayer = ColortableLayer(LazyflowSource(supervoxelsSlot), colortable) supervoxelsLayer.name = "Input Supervoxels" supervoxelsLayer.visible = False supervoxelsLayer.opacity = 1.0 layers.append(supervoxelsLayer) def addFragmentSegmentationLayers(mslot, name): if mslot.ready(): for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label colortable = [] for i in range(256): r, g, b = numpy.random.randint( 0, 255), numpy.random.randint( 0, 255), numpy.random.randint(0, 255) colortable.append(QColor(r, g, b).rgba()) colortable[0] = QColor(0, 0, 0, 0).rgba() fragSegLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) fragSegLayer.name = "{} #{} ({})".format( name, index, raveler_label) fragSegLayer.visible = False fragSegLayer.opacity = 1.0 layers.append(fragSegLayer) addFragmentSegmentationLayers(op.MaskedSupervoxels, "Masked Supervoxels") addFragmentSegmentationLayers(op.FilteredMaskedSupervoxels, "Filtered Masked Supervoxels") addFragmentSegmentationLayers(op.HoleFilledSupervoxels, "Hole Filled Supervoxels") addFragmentSegmentationLayers(op.RelabeledSupervoxels, "Relabeled Supervoxels") mslot = op.EditedRavelerBodies for index, slot in enumerate(mslot): if slot.ready(): raveler_label = slot.meta.selected_label # 0=Black, 1=Transparent colortable = [ QColor(0, 0, 0).rgba(), QColor(0, 0, 0, 0).rgba() ] bodyMaskLayer = ColortableLayer(LazyflowSource(slot), colortable, direct=True) bodyMaskLayer.name = "Raveler Body Mask #{} ({})".format( index, raveler_label) bodyMaskLayer.visible = False bodyMaskLayer.opacity = 1.0 layers.append(bodyMaskLayer) finalSegSlot = op.FinalSupervoxels if finalSegSlot.ready(): colortable = [] for _ in range(256): r, g, b = numpy.random.randint(0, 255), numpy.random.randint( 0, 255), numpy.random.randint(0, 255) colortable.append(QColor(r, g, b).rgba()) finalLayer = ColortableLayer(LazyflowSource(finalSegSlot), colortable) finalLayer.name = "Final Supervoxels" finalLayer.visible = False finalLayer.opacity = 0.4 layers.append(finalLayer) inputSlot = op.InputData if inputSlot.ready(): layer = GrayscaleLayer(LazyflowSource(inputSlot)) layer.name = "WS Input" layer.visible = False layer.opacity = 1.0 layers.append(layer) #raw data rawSlot = self.topLevelOperatorView.RawData if rawSlot.ready(): raw5D = self.topLevelOperatorView.RawData.value layer = GrayscaleLayer(ArraySource(raw5D), direct=True) #layer = GrayscaleLayer( LazyflowSource(rawSlot) ) layer.name = "raw" layer.visible = True layer.opacity = 1.0 layers.append(layer) return layers
def showStuff(raw_name, pred_viewer1, pred_viewer2, cutout_name, one_extra = None): # display the raw and annotations for cremi challenge data raw = vigra.impex.readHDF5(indir+datasets[raw_name], "data", order = 'C') # raw_old = vigra.readHDF5(indir+datasets["raw_bad"], "data", order = 'C') defect_prediction_128 = vigra.impex.readHDF5(indir+datasets[pred_viewer2], "data", order = 'C') defect_prediction_150 = vigra.impex.readHDF5(indir+datasets[pred_viewer1], "data", order = 'C') cutout_from_150_pred = vigra.impex.readHDF5(indir+datasets[cutout_name], "data", order = 'C') #################################################################################################################### # only used for fast testing stuff #change_one = vigra.readHDF5(indir+datasets["segmentation_on_equalized_image"], "data", order = 'C') #pdb.set_trace() #defect_prediction_150[1,:,:] = change_one[0,:,:,0] #################################################################################################################### # defect_prediction_150 = gt[..., 0] cutout = numpy.asarray(cutout_from_150_pred) rawdata = numpy.asarray(raw) # rawdata_old = numpy.asarray(raw_old) # op5ify # shape5d = rawdata.shape shape5d = (1,)+rawdata.shape+(1,) print shape5d, rawdata.shape, rawdata.dtype app = QApplication([]) v = Viewer() direct = False # layer for raw data rawdata = numpy.reshape(rawdata, shape5d) rawsource = ArraySource(rawdata) v.dataShape = shape5d lraw = GrayscaleLayer(rawsource, direct=direct) lraw.visible = True lraw.name = "raw" v.layerstack.append(lraw) # layer for cutout regions from raw data cutout = numpy.reshape(cutout, shape5d) cutoutsource = ArraySource(cutout) lcutout = GrayscaleLayer(cutoutsource, direct = direct) lcutout.visible = False lcutout.name = "cut_out" v.layerstack.append(lcutout) # layer for first prediction result defect_prediction_128 = numpy.reshape(defect_prediction_128, shape5d) synsource = ArraySource(defect_prediction_128) ct = create_random_16bit() ct[0] = 0 lsyn = ColortableLayer(synsource, ct) lsyn.name = pred_viewer2 lsyn.visible = False v.layerstack.append(lsyn) # layer for second prediction result segm = numpy.reshape(defect_prediction_150, shape5d) segsource = ArraySource(segm) ct = create_random_16bit() ct[0] = 0 lseg = ColortableLayer(segsource, ct) lseg.name = pred_viewer1 lseg.visible = False v.layerstack.append(lseg) if one_extra is None: v.showMaximized() app.exec_() if one_extra is not None: # layer for third prediction result extra_prediction = vigra.readHDF5(indir+datasets[one_extra], "data", order = 'C') extra_pred_reshaped = numpy.reshape(extra_prediction, shape5d) segsource = ArraySource(extra_pred_reshaped) ct = create_random_16bit() ct[0] = 0 # ct = create_default_16bit() lseg = ColortableLayer(segsource, ct) lseg.name = one_extra lseg.visible = False v.layerstack.append(lseg) v.showMaximized() app.exec_()
def showStuff(raw_name, pred_viewer1, pred_viewer2, cutout_name, one_extra=None): # display the raw and annotations for cremi challenge data raw = vigra.impex.readHDF5(indir + datasets[raw_name], "data", order='C') # raw_old = vigra.readHDF5(indir+datasets["raw_bad"], "data", order = 'C') defect_prediction_128 = vigra.impex.readHDF5(indir + datasets[pred_viewer2], "data", order='C') defect_prediction_150 = vigra.impex.readHDF5(indir + datasets[pred_viewer1], "data", order='C') cutout_from_150_pred = vigra.impex.readHDF5(indir + datasets[cutout_name], "data", order='C') #################################################################################################################### # only used for fast testing stuff #change_one = vigra.readHDF5(indir+datasets["segmentation_on_equalized_image"], "data", order = 'C') #pdb.set_trace() #defect_prediction_150[1,:,:] = change_one[0,:,:,0] #################################################################################################################### # defect_prediction_150 = gt[..., 0] cutout = numpy.asarray(cutout_from_150_pred) rawdata = numpy.asarray(raw) # rawdata_old = numpy.asarray(raw_old) # op5ify # shape5d = rawdata.shape shape5d = (1, ) + rawdata.shape + (1, ) print shape5d, rawdata.shape, rawdata.dtype app = QApplication([]) v = Viewer() direct = False # layer for raw data rawdata = numpy.reshape(rawdata, shape5d) rawsource = ArraySource(rawdata) v.dataShape = shape5d lraw = GrayscaleLayer(rawsource, direct=direct) lraw.visible = True lraw.name = "raw" v.layerstack.append(lraw) # layer for cutout regions from raw data cutout = numpy.reshape(cutout, shape5d) cutoutsource = ArraySource(cutout) lcutout = GrayscaleLayer(cutoutsource, direct=direct) lcutout.visible = False lcutout.name = "cut_out" v.layerstack.append(lcutout) # layer for first prediction result defect_prediction_128 = numpy.reshape(defect_prediction_128, shape5d) synsource = ArraySource(defect_prediction_128) ct = create_random_16bit() ct[0] = 0 lsyn = ColortableLayer(synsource, ct) lsyn.name = pred_viewer2 lsyn.visible = False v.layerstack.append(lsyn) # layer for second prediction result segm = numpy.reshape(defect_prediction_150, shape5d) segsource = ArraySource(segm) ct = create_random_16bit() ct[0] = 0 lseg = ColortableLayer(segsource, ct) lseg.name = pred_viewer1 lseg.visible = False v.layerstack.append(lseg) if one_extra is None: v.showMaximized() app.exec_() if one_extra is not None: # layer for third prediction result extra_prediction = vigra.readHDF5(indir + datasets[one_extra], "data", order='C') extra_pred_reshaped = numpy.reshape(extra_prediction, shape5d) segsource = ArraySource(extra_pred_reshaped) ct = create_random_16bit() ct[0] = 0 # ct = create_default_16bit() lseg = ColortableLayer(segsource, ct) lseg.name = one_extra lseg.visible = False v.layerstack.append(lseg) v.showMaximized() app.exec_()