def Execute(self):
if self.Surface == None:
self.PrintError('Error: No input surface.')
# feature edges are used to find any holes in the surface.
fedges = vtk.vtkFeatureEdges()
fedges.BoundaryEdgesOn()
fedges.FeatureEdgesOff()
fedges.ManifoldEdgesOff()
fedges.SetInputData(self.Surface)
fedges.Update()
ofedges = fedges.GetOutput()
# if numEdges is not 0, then there are holes which need to be capped
numEdges = ofedges.GetNumberOfPoints()
if numEdges != 0:
tempcapper = vmtksurfacecapper.vmtkSurfaceCapper()
tempcapper.Surface = self.Surface
tempcapper.Interactive = 0
tempcapper.Execute()
networkSurface = tempcapper.Surface
else:
networkSurface = self.Surface
# randomly select one cell to delete so that there is an opening for
# vmtkNetworkExtraction to use.
numCells = self.Surface.GetNumberOfCells()
cellToDelete = random.randrange(0, numCells-1)
networkSurface.BuildLinks()
networkSurface.DeleteCell(cellToDelete)
networkSurface.RemoveDeletedCells()
# extract the network of approximated centerlines
net = vmtknetworkextraction.vmtkNetworkExtraction()
net.Surface = networkSurface
net.AdvancementRatio = 1.001
net.Execute()
network = net.Network
convert = vmtkcenterlinestonumpy.vmtkCenterlinesToNumpy()
convert.Centerlines = network
convert.LogOn = False
convert.Execute()
ad = convert.ArrayDict
cellDataTopology = ad['CellData']['Topology']
# the network topology identifies an the input segment with the "0" id.
# since we artificially created this segment, we don't want to use the
# ends of the segment as source/target points of the centerline calculation
nodeIndexToIgnore = np.where(cellDataTopology[:,0] == 0)[0][0]
keepCellConnectivityList = []
pointIdxToKeep = np.array([])
# we remove the cell, points, and point data which are associated with the
# segment we want to ignore
for loopIdx, cellConnectivityList in enumerate(ad['CellData']['CellPointIds']):
if loopIdx == nodeIndexToIgnore:
removeCellStartIdx = cellConnectivityList[0]
removeCellEndIdx = cellConnectivityList[-1]
removeCellLength = cellConnectivityList.size
if (removeCellEndIdx + 1) - removeCellStartIdx != removeCellLength:
raise(ValueError)
continue
else:
rescaledCellConnectivity = np.subtract(cellConnectivityList, removeCellLength, where=cellConnectivityList >= removeCellLength)
keepCellConnectivityList.append(rescaledCellConnectivity)
pointIdxToKeep = np.concatenate((pointIdxToKeep, cellConnectivityList)).astype(np.int)
newPoints = ad['Points'][pointIdxToKeep]
newRadius = ad['PointData']['Radius'][pointIdxToKeep]
# precompute the delaunay tessellation for the whole surface.
tessalation = vmtkdelaunayvoronoi.vmtkDelaunayVoronoi()
tessalation.Surface = networkSurface
tessalation.Execute()
self.DelaunayTessellation = tessalation.DelaunayTessellation
self.VoronoiDiagram = tessalation.VoronoiDiagram
self.PoleIds = tessalation.PoleIds
# vtk objects cannot be serialized in python. Instead of converting the inputs to numpy arrays and having
# to reconstruct the vtk object each time the loop executes (a slow process), we can just pass in the
# memory address of the data objects as a string, and use the vtk python bindings to create a python name
# referring to the data residing at that memory address. This works because joblib executes each loop
# iteration in a fork of the original process, providing access to the original memory space.
# However, the process does not work for return arguments, since the original process will not have access to
# the memory space of the fork. To return results we use the vmtkCenterlinesToNumpy converter.
networkSurfaceMemoryAddress = networkSurface.__this__
delaunayMemoryAddress = tessalation.DelaunayTessellation.__this__
voronoiMemoryAddress = tessalation.VoronoiDiagram.__this__
poleIdsMemoryAddress = tessalation.PoleIds.__this__
# When using Joblib Under Windows, it is important to protect the main loop of code to avoid recursive spawning
# of subprocesses. Since we cannot guarantee no code will run outside of “if __name__ == ‘__main__’” blocks
# (only imports and definitions), we make Joblib execute each loop iteration serially on windows.
# On unix-like os's (linux, Mac) we execute each loop iteration independently on as many cores as the system has.
if (sys.platform == 'win32') or (sys.platform == 'win64') or (sys.platform == 'cygwin'):
self.PrintLog('Centerlines extraction on windows computer will execute serially.')
self.PrintLog('To speed up execution, please run vmtk on unix-like operating system')
numParallelJobs = 1
else:
numParallelJobs = -1
self.PrintLog('Computing Centerlines ...')
# note about the verbose function: while Joblib can print a progress bar output (set verbose = 20),
# it does not implement a callback function as of version 0.11, so we cannot report progress to the user
# if we are redirecting standard out with the self.PrintLog method.
outlist = Parallel(n_jobs=numParallelJobs, backend='multiprocessing', verbose=0)(
delayed(_compute_centerlines_network)(networkSurfaceMemoryAddress,
delaunayMemoryAddress,
voronoiMemoryAddress,
poleIdsMemoryAddress,
cell,
newPoints) for cell in keepCellConnectivityList)
out = []
for item in outlist:
npConvert = vmtknumpytocenterlines.vmtkNumpyToCenterlines()
npConvert.ArrayDict = item
npConvert.LogOn = 0
npConvert.Execute()
out.append(npConvert.Centerlines)
# Append each segment's polydata into a single polydata object
centerlineAppender = vtk.vtkAppendPolyData()
for data in out:
centerlineAppender.AddInputData(data)
centerlineAppender.Update()
# clean and strip the output centerlines so that redundant points are merged and tracts are combined
centerlineCleaner = vtk.vtkCleanPolyData()
centerlineCleaner.SetInputData(centerlineAppender.GetOutput())
centerlineCleaner.Update()
centerlineStripper = vtk.vtkStripper()
centerlineStripper.SetInputData(centerlineCleaner.GetOutput())
centerlineStripper.JoinContiguousSegmentsOn()
centerlineStripper.Update()
self.Centerlines = centerlineStripper.GetOutput()
def test_methods_with_default_params(aorta_surface_open_ends, method, paramid, compare_surfaces, write_surface):
name = __name__ + '_test_methods_with_default_params_' + paramid + '.vtp'
capper = surfacecapper.vmtkSurfaceCapper()
capper.Surface = aorta_surface_open_ends
capper.Method = method
capper.Interactive = 0
capper.Execute()
write_surface(capper.Surface)
assert compare_surfaces(capper.Surface, name) == True
def Execute(self):
if self.Surface == None:
self.PrintError('Error: No Input Surface.')
# Step 0: Check if the surface has any unfilled holes in it. if it does, cap them.
fedges = vtk.vtkFeatureEdges()
fedges.BoundaryEdgesOn()
fedges.FeatureEdgesOff()
fedges.ManifoldEdgesOff()
fedges.SetInputData(self.Surface)
fedges.Update()
ofedges = fedges.GetOutput()
# if the following is not == 0, then the surface contains unfilled holes.
numEdges = ofedges.GetNumberOfPoints()
if numEdges >= 1:
self.PrintLog('Capping unclosed holes in surface.')
tempcapper = vmtksurfacecapper.vmtkSurfaceCapper()
tempcapper.Surface = self.Surface
tempcapper.LogOn = 0
tempcapper.Interactive = 0
tempcapper.Execute()
self.Surface = tempcapper.Surface
# Step 1: Convert the input surface into an image mask of unsigned char type and spacing = PolyDataToImageDataSpacing
# Where voxels lying inside the surface are set to 255 and voxels outside the image are set to value 0.
# since we are creating a new image container from nothing, calculate the origin, extent, and dimensions for the
# vtkImageDataObject from the surface parameters.
self.PrintLog('Converting Surface to Image Mask')
binaryImageFilter = vmtksurfacetobinaryimage.vmtkSurfaceToBinaryImage()
binaryImageFilter.Surface = self.Surface
binaryImageFilter.InsideValue = 255
binaryImageFilter.LogOn = 0
binaryImageFilter.OutsideValue = 0
binaryImageFilter.PolyDataToImageDataSpacing = self.PolyDataToImageDataSpacing
binaryImageFilter.Execute()
# Step 2: Feed into the vtkvmtkMedialCurveFilter
# This takes the binary image and computes the average outward flux of the image. This is then
# used to compute the skeleton image. It returns a binary image where values of 1 are skeleton points
# and values of 0 are outside the skeleton. The execution speed of this algorithm is fairly sensetive to
# the extent of the input image.
self.PrintLog('Extracting Centerline Skeleton from Image Mask...')
medialCurveFilter = vtkvmtk.vtkvmtkMedialCurveFilter()
medialCurveFilter.SetInputData(binaryImageFilter.Image)
medialCurveFilter.SetThreshold(self.Threshold)
medialCurveFilter.SetSigma(self.Sigma)
medialCurveFilter.Update()
self.Image = medialCurveFilter.GetOutput()
def Execute(self):
if self.Surface == None:
self.PrintError('Error: No Input Surface.')
# Step 0: Check if the surface has any unfilled holes in it. if it does, cap them.
fedges = vtk.vtkFeatureEdges()
fedges.BoundaryEdgesOn()
fedges.FeatureEdgesOff()
fedges.ManifoldEdgesOff()
fedges.SetInputData(self.Surface)
fedges.Update()
ofedges = fedges.GetOutput()
# if the following is not == 0, then the surface contains unfilled holes.
numEdges = ofedges.GetNumberOfPoints()
if numEdges >= 1:
self.PrintLog('Capping unclosed holes in surface.')
tempcapper = vmtksurfacecapper.vmtkSurfaceCapper()
tempcapper.Surface = self.Surface
tempcapper.LogOn = 0
tempcapper.Interactive = 0
tempcapper.Execute()
self.Surface = tempcapper.Surface
# Step 1: Convert the input surface into an image mask of unsigned char type and spacing = PolyDataToImageDataSpacing
# Where voxels lying inside the surface are set to 255 and voxels outside the image are set to value 0.
# since we are creating a new image container from nothing, calculate the origin, extent, and dimensions for the
# vtkImageDataObject from the surface parameters.
self.PrintLog('Converting Surface to Image Mask')
binaryImageFilter = vmtksurfacetobinaryimage.vmtkSurfaceToBinaryImage()
binaryImageFilter.Surface = self.Surface
binaryImageFilter.InsideValue = 255
binaryImageFilter.LogOn = 0
binaryImageFilter.OutsideValue = 0
binaryImageFilter.PolyDataToImageDataSpacing = self.PolyDataToImageDataSpacing
binaryImageFilter.Execute()
# Step 2: Feed into the vtkvmtkMedialCurveFilter
# This takes the binary image and computes the average outward flux of the image. This is then
# used to compute the skeleton image. It returns a binary image where values of 1 are skeleton points
# and values of 0 are outside the skeleton. The execution speed of this algorithm is fairly sensetive to
# the extent of the input image.
self.PrintLog('Extracting Centerline Skeleton from Image Mask...')
medialCurveFilter = vtkvmtk.vtkvmtkMedialCurveFilter()
medialCurveFilter.SetInputData(binaryImageFilter.Image)
medialCurveFilter.SetThreshold(self.Threshold)
medialCurveFilter.SetSigma(self.Sigma)
medialCurveFilter.Update()
self.Image = medialCurveFilter.GetOutput()
def test_smooth_method_with_changing_params(aorta_surface_open_ends, constraint, rings,
paramid, compare_surfaces, write_surface):
name = __name__ + '_test_smooth_method_with_changing_params_' + paramid + '.vtp'
capper = surfacecapper.vmtkSurfaceCapper()
capper.Surface = aorta_surface_open_ends
capper.Method = 'smooth'
capper.ConstraintFactor = constraint
capper.NumberOfRings = rings
capper.Interactive = 0
capper.Execute()
write_surface(capper.Surface)
assert compare_surfaces(capper.Surface, name) == True
def surface_capper(input_file, output_file):
'''
将网格变得端口封住
'''
reader = sr.vmtkSurfaceReader()
reader.InputFileName = input_file
reader.Execute()
capper = sc.vmtkSurfaceCapper()
capper.Surface = reader.Surface
capper.Execute()
writer = sw.vmtkSurfaceWriter()
writer.OutputFileName = output_file
writer.Surface = capper.Surface
writer.Mode = 'ascii'
writer.Execute()
def Execute(self):
if self.Surface == None:
self.PrintError('Error: No input surface.')
# feature edges are used to find any holes in the surface.
fedges = vtk.vtkFeatureEdges()
fedges.BoundaryEdgesOn()
fedges.FeatureEdgesOff()
fedges.ManifoldEdgesOff()
fedges.SetInputData(self.Surface)
fedges.Update()
ofedges = fedges.GetOutput()
# if numEdges is not 0, then there are holes which need to be capped
numEdges = ofedges.GetNumberOfPoints()
if numEdges != 0:
tempcapper = vmtksurfacecapper.vmtkSurfaceCapper()
tempcapper.Surface = self.Surface
tempcapper.Interactive = 0
tempcapper.Execute()
networkSurface = tempcapper.Surface
else:
networkSurface = self.Surface
# randomly select one cell to delete so that there is an opening for
# vmtkNetworkExtraction to use.
numCells = networkSurface.GetNumberOfCells()
cellToDelete = random.randrange(0, numCells-1)
networkSurface.BuildLinks()
networkSurface.DeleteCell(cellToDelete)
networkSurface.RemoveDeletedCells()
# extract the network of approximated centerlines
net = vmtknetworkextraction.vmtkNetworkExtraction()
net.Surface = networkSurface
net.AdvancementRatio = 1.001
net.Execute()
network = net.Network
convert = vmtkcenterlinestonumpy.vmtkCenterlinesToNumpy()
convert.Centerlines = network
convert.LogOn = False
convert.Execute()
ad = convert.ArrayDict
cellDataTopology = ad['CellData']['Topology']
# the network topology identifies an the input segment with the "0" id.
# since we artificially created this segment, we don't want to use the
# ends of the segment as source/target points of the centerline calculation
nodeIndexToIgnore = np.where(cellDataTopology[:,0] == 0)[0][0]
keepCellConnectivityList = []
pointIdxToKeep = np.array([])
removeCellLength = 0
# we remove the cell, points, and point data which are associated with the
# segment we want to ignore
for loopIdx, cellConnectivityList in enumerate(ad['CellData']['CellPointIds']):
if loopIdx == nodeIndexToIgnore:
removeCellStartIdx = cellConnectivityList[0]
removeCellEndIdx = cellConnectivityList[-1]
removeCellLength = cellConnectivityList.size
if (removeCellEndIdx + 1) - removeCellStartIdx != removeCellLength:
raise(ValueError)
continue
else:
rescaledCellConnectivity = np.subtract(cellConnectivityList, removeCellLength, where=cellConnectivityList >= removeCellLength)
keepCellConnectivityList.append(rescaledCellConnectivity)
pointIdxToKeep = np.concatenate((pointIdxToKeep, cellConnectivityList)).astype(np.int)
newPoints = ad['Points'][pointIdxToKeep]
newRadius = ad['PointData']['Radius'][pointIdxToKeep]
# precompute the delaunay tessellation for the whole surface.
tessalation = vmtkdelaunayvoronoi.vmtkDelaunayVoronoi()
tessalation.Surface = networkSurface
tessalation.Execute()
self.DelaunayTessellation = tessalation.DelaunayTessellation
self.VoronoiDiagram = tessalation.VoronoiDiagram
self.PoleIds = tessalation.PoleIds
# vtk objects cannot be serialized in python. Instead of converting the inputs to numpy arrays and having
# to reconstruct the vtk object each time the loop executes (a slow process), we can just pass in the
# memory address of the data objects as a string, and use the vtk python bindings to create a python name
# referring to the data residing at that memory address. This works because joblib executes each loop
# iteration in a fork of the original process, providing access to the original memory space.
# However, the process does not work for return arguments, since the original process will not have access to
# the memory space of the fork. To return results we use the vmtkCenterlinesToNumpy converter.
networkSurfaceMemoryAddress = networkSurface.__this__
delaunayMemoryAddress = tessalation.DelaunayTessellation.__this__
voronoiMemoryAddress = tessalation.VoronoiDiagram.__this__
poleIdsMemoryAddress = tessalation.PoleIds.__this__
# When using Joblib Under Windows, it is important to protect the main loop of code to avoid recursive spawning
# of subprocesses. Since we cannot guarantee no code will run outside of “if __name__ == ‘__main__’” blocks
# (only imports and definitions), we make Joblib execute each loop iteration serially on windows.
# On unix-like os's (linux, Mac) we execute each loop iteration independently on as many cores as the system has.
if (sys.platform == 'win32') or (sys.platform == 'win64') or (sys.platform == 'cygwin'):
self.PrintLog('Centerlines extraction on windows computer will execute serially.')
self.PrintLog('To speed up execution, please run vmtk on unix-like operating system')
numParallelJobs = 1
else:
numParallelJobs = -1
self.PrintLog('Computing Centerlines ...')
# note about the verbose function: while Joblib can print a progress bar output (set verbose = 20),
# it does not implement a callback function as of version 0.11, so we cannot report progress to the user
# if we are redirecting standard out with the self.PrintLog method.
outlist = Parallel(n_jobs=numParallelJobs, backend='multiprocessing', verbose=0)(
delayed(_compute_centerlines_network)(networkSurfaceMemoryAddress,
delaunayMemoryAddress,
voronoiMemoryAddress,
poleIdsMemoryAddress,
cell,
newPoints) for cell in keepCellConnectivityList)
out = []
for item in outlist:
npConvert = vmtknumpytocenterlines.vmtkNumpyToCenterlines()
npConvert.ArrayDict = item
npConvert.LogOn = 0
npConvert.Execute()
out.append(npConvert.Centerlines)
# Append each segment's polydata into a single polydata object
centerlineAppender = vtk.vtkAppendPolyData()
for data in out:
centerlineAppender.AddInputData(data)
centerlineAppender.Update()
# clean and strip the output centerlines so that redundant points are merged and tracts are combined
centerlineCleaner = vtk.vtkCleanPolyData()
centerlineCleaner.SetInputData(centerlineAppender.GetOutput())
centerlineCleaner.Update()
centerlineStripper = vtk.vtkStripper()
centerlineStripper.SetInputData(centerlineCleaner.GetOutput())
centerlineStripper.JoinContiguousSegmentsOn()
centerlineStripper.Update()
self.Centerlines = centerlineStripper.GetOutput()