[vtkusers] segfault with MarchingCubes

[Clément D.]

Great David, thanks again.
The SetNumberOfScalarComponents(3) was indeed a residual of a
not-careful-enough copy-paste from another part of the code.
Everything's working now, and thanks for the optimisation tips !


2017-07-10 14:48 GMT+02:00 David Gobbi <david.gobbi at gmail.com>:

> Hi Clément,
>
> The "number of scalar components" should be 1 for your data:
>
>   dataImporter.SetNumberOfScalarComponents(1)
>
> There are a couple places where you can improve efficiency:
>
>   # directly create an array of the desired type
>   npa=np.zeros((150,150,150), np.uint8)
>
>   # directly pass the array to VTK (without tostring())
>   dataImporter.CopyImportVoidPointer(npa, npa.size)
>
> Cheers,
>  - David
>
>
> On Mon, Jul 10, 2017 at 2:29 AM, Clément D. <clement.douarre at gmail.com>
> wrote:
>
>> Hello all,
>>
>> I am trying to use the Marching Cubes algorithm to show a 3D surface of a
>> 3D image given by a Python (numpy array).
>> I thought I had it all mapped out, things were going great with arrays of
>> small size (generated and rendered 3D isosurfaces), but now that I'm
>> working with arrays of slightly bigger size, the programm crashes with
>> Python error 11 SIGSEGV which means seg fault. What worries me a bit is
>> that the array I'm using is not that big, 150*150*150, and I have a fairly
>> recent computer who shouldn't have any problem handling it.
>>
>> Code MWE is below (in Python)
>>
>> =========================================== Code snippet
>> =======================
>>
>> import numpy as np
>> import vtk
>>
>> # Creating a numpy array representing a 3D image
>> npa=np.zeros((150,150,150)).astype(np.uint8)
>> npa[100:102,100:102,100:102]=255
>>
>> # Importing it as  object vtk (based on vtk example
>> http://www.vtk.org/Wiki/VTK/Examples/Python/vtkWithNumpy)
>> dataImporter = vtk.vtkImageImport()
>> dataString = npa.tostring()
>> [hh, ww, dd] = npa.shape
>> dataImporter.CopyImportVoidPointer(dataString, len(dataString))
>> dataImporter.SetDataScalarTypeToUnsignedChar()
>> dataImporter.SetNumberOfScalarComponents(3)
>> dataImporter.SetDataExtent(0, dd - 1, 0, ww - 1, 0, hh - 1)
>> dataImporter.SetWholeExtent(0, dd - 1, 0, ww - 1, 0, hh - 1)
>> dataImporter.Update()
>> vol = dataImporter.GetOutputPort()
>> # (print(dataImporter.GetOutput().GetClassName()) yields "vtk.ImageData")
>>
>> # Use MarchingCubes to generate iso-surface
>> surface = vtk.vtkMarchingCubes();
>> surface.SetInputConnection(vol);
>> surface.ComputeNormalsOn();
>> surface.ComputeScalarsOn();
>> isoValue = 128
>> surface.SetValue(0, isoValue);
>>
>> #This line causes segfault if npa is "too big"
>> surface.Update();
>>
>> # .... rest of pipeline
>> # decimator = vtk.vtkDecimatePro(); ....
>> # smoother = vtk.vtkSmoothPolyDataFilter(); ....
>> # connectivityFilter = vtk.vtkPolyDataConnectivityFilter(); ....
>> # mesh = vtk.vtkPolyData(); ....
>> # mapper = vtk.vtkPolyDataMapper(); ....
>> # actor = vtk.vtkActor(); ....
>>
>>
>> =========================================== End of code snippet
>> =======================
>>
>>
>>
>> So I have a couple of questions here :
>> 1/ Is this a problem of input for MarchingCubes being too big ? Or is
>> this segfault indicating something else ?
>> - arguments for the former : Reducing the size of npa in this code makes
>> it work
>> - arguments for the latter : This code I'm showing is a MWE for my code
>> which is more complicated, and in my code, this code portion fails with a
>> large array, but succeeds if I reduce in size *another* array which has
>> nothing to do with MarchingCubes() (again, that other array is not that
>> big, so I don't think I'm reaching the limits of Python memory or anything
>> like that...)
>>
>> 2/ Am I doing the whole import numpy thing correctly ? I've based myself
>> on the vtk example, but I am unsure of whether it is the recommended way
>> for my case or if it was case specific
>>
>>
>> Thanks everyone,
>> Clément
>>
>
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