本文整理汇总了Python中volumina.api.GrayscaleLayer.ref_object方法的典型用法代码示例。如果您正苦于以下问题：Python GrayscaleLayer.ref_object方法的具体用法？Python GrayscaleLayer.ref_object怎么用？Python GrayscaleLayer.ref_object使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类volumina.api.GrayscaleLayer的用法示例。
在下文中一共展示了GrayscaleLayer.ref_object方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: initGraph
# 需要导入模块: from volumina.api import GrayscaleLayer [as 别名]
# 或者: from volumina.api.GrayscaleLayer import ref_object [as 别名]
def initGraph(self):
shape = self.inputProvider.outputs["Output"].shape
srcs = []
minMax = []
normalize = []
print "* Data has shape=%r" % (shape,)
#create a layer for each channel of the input:
slicer=OpMultiArraySlicer2(self.g)
slicer.inputs["Input"].connect(self.inputProvider.outputs["Output"])
slicer.inputs["AxisFlag"].setValue('c')
nchannels = shape[-1]
for ich in xrange(nchannels):
data=slicer.outputs['Slices'][ich][:].allocate().wait()
#find the minimum and maximum value for normalization
mm = (numpy.min(data), numpy.max(data))
print " - channel %d: min=%r, max=%r" % (ich, mm[0], mm[1])
minMax.append(mm)
if self._normalize_data:
normalize.append(mm)
else:
normalize.append((0,255))
layersrc = LazyflowSource(slicer.outputs['Slices'][ich], priority = 100)
layersrc.setObjectName("raw data channel=%d" % ich)
srcs.append(layersrc)
#FIXME: we shouldn't merge channels automatically, but for now it's prettier
layer1 = None
if nchannels == 1:
layer1 = GrayscaleLayer(srcs[0], range=minMax[0], normalize=normalize[0])
print " - showing raw data as grayscale"
elif nchannels==2:
layer1 = RGBALayer(red = srcs[0], normalizeR=normalize[0],
green = srcs[1], normalizeG=normalize[1], range=minMax[0:2]+[(0,255), (0,255)])
print " - showing channel 1 as red, channel 2 as green"
elif nchannels==3:
layer1 = RGBALayer(red = srcs[0], normalizeR=normalize[0],
green = srcs[1], normalizeG=normalize[1],
blue = srcs[2], normalizeB=normalize[2],
range = minMax[0:3])
print " - showing channel 1 as red, channel 2 as green, channel 3 as blue"
else:
print "only 1,2 or 3 channels supported so far"
return
print
layer1.name = "Input data"
layer1.ref_object = None
self.layerstack.append(layer1)
opImageList = Op5ToMulti(self.g)
opImageList.inputs["Input0"].connect(self.inputProvider.outputs["Output"])
#init the features operator
opPF = OpPixelFeaturesPresmoothed(self.g)
opPF.inputs["Input"].connect(opImageList.outputs["Outputs"])
opPF.inputs["Scales"].setValue(self.featScalesList)
self.opPF=opPF
#Caches the features
opFeatureCache = OpBlockedArrayCache(self.g)
opFeatureCache.inputs["innerBlockShape"].setValue((1,32,32,32,16))
opFeatureCache.inputs["outerBlockShape"].setValue((1,128,128,128,64))
opFeatureCache.inputs["Input"].connect(opPF.outputs["Output"])
opFeatureCache.inputs["fixAtCurrent"].setValue(False)
self.opFeatureCache=opFeatureCache
self.initLabels()
self.dataReadyToView.emit()示例2: initGraph
# 需要导入模块: from volumina.api import GrayscaleLayer [as 别名]
# 或者: from volumina.api.GrayscaleLayer import ref_object [as 别名]
def initGraph(self):
shape = self.inputProvider.outputs["Output"].shape
srcs = []
minMax = []
print "* Data has shape=%r" % (shape,)
#create a layer for each channel of the input:
slicer=OpMultiArraySlicer2(self.g)
slicer.inputs["Input"].connect(self.inputProvider.outputs["Output"])
slicer.inputs["AxisFlag"].setValue('c')
nchannels = shape[-1]
for ich in xrange(nchannels):
if self._normalize_data:
data=slicer.outputs['Slices'][ich][:].allocate().wait()
#find the minimum and maximum value for normalization
mm = (numpy.min(data), numpy.max(data))
print " - channel %d: min=%r, max=%r" % (ich, mm[0], mm[1])
minMax.append(mm)
else:
minMax.append(None)
layersrc = LazyflowSource(slicer.outputs['Slices'][ich], priority = 100)
layersrc.setObjectName("raw data channel=%d" % ich)
srcs.append(layersrc)
#FIXME: we shouldn't merge channels automatically, but for now it's prettier
layer1 = None
if nchannels == 1:
layer1 = GrayscaleLayer(srcs[0], normalize=minMax[0])
layer1.set_range(0,minMax[0])
print " - showing raw data as grayscale"
elif nchannels==2:
layer1 = RGBALayer(red = srcs[0], normalizeR=minMax[0],
green = srcs[1], normalizeG=minMax[1])
layer1.set_range(0, minMax[0])
layer1.set_range(1, minMax[1])
print " - showing channel 1 as red, channel 2 as green"
elif nchannels==3:
layer1 = RGBALayer(red = srcs[0], normalizeR=minMax[0],
green = srcs[1], normalizeG=minMax[1],
blue = srcs[2], normalizeB=minMax[2])
layer1.set_range(0, minMax[0])
layer1.set_range(1, minMax[1])
layer1.set_range(2, minMax[2])
print " - showing channel 1 as red, channel 2 as green, channel 3 as blue"
else:
print "only 1,2 or 3 channels supported so far"
return
print
layer1.name = "Input data"
layer1.ref_object = None
self.layerstack.append(layer1)
self.workflow = PixelClassificationLazyflow( self.g, self.featScalesList, self.inputProvider.outputs["Output"])
self.initLabels()
self.startClassification()
self.dataReadyToView.emit()