Hi Burlen, Utkarsh, Thanks much for your detailed answers, and the caveat. A developer here just told me that vtkImageGaussianSmooth might not be what I need. I am trying to achive a non-pixellated look in a rendering of a slice from a volume.
Right now I read in the volume and choose a slice representation to look at a selected plane. The datatype remains uniform grid. As per the image intensities in the voume, it looks a bit pixellated. Changing shading between flat/Gouraud makes no difference, I think because its not a surface. If I read in the volume and then use the slice filter to extract a slice. The output slice is polygonal mesh datatype. I choose a surface representation to look at that slice and there is a nice smoother look across the pixels. (Changing shading between flat/Gouraud doesn't seem to make a difference here either.) The developer here thinks that even if I pre-smooth my volume with vtkImageGaussianSmooth the uniform grid slice representation will look a little pixellated. Is there a way to get the nice smoothness of the polygonal mesh surface look on my uniform grid slice representation ? My script will just be a little more complicated if I need to call the slice filter, so I'd rather not if this can be handled at the representation level. thanks kate > Hi Kate, > > It shouldn't be too hard for you to expose the vtkImageGaussianSmooth in > PV. I have one caveat for you re: using vtkImageGaussianSmooth in > parallel: when it runs it will trigger a complete re-execution of your > entire pipeline including the reader.This happens because > vtkImageGaussianSmooth increases the update extents which, unless > something has changed in the recent version of PV, would be satisfied by > the reader re-executing. this could cause you some performance related > issues especially for large data where I/O becomes a significant > bottleneck. On the other hand, this re-execution would happen only when > running in parallel and if you have small data it might not be too bad. > > I encountered this issue a while back. with our data size the additional > I/O was unacceptable. i wrote a parallel ghost cell generator for image > data to prevent the pipeline re-execution for operations like kernel > convolution that require ghost cells. The plugin includes a kernel > convolution filter as well. To use it you'd have to build and load our > plugin and add the "image ghosts" filter right before the "kernel > convolution" filter. Our kernel convolution filter also has a CUDA > implementation that's about 20x faster than the cpu version (note: CUDA > stuff is still being developed, still some issue when update extent is > too large to fit on the gpu). I'm attaching a link to the latest release > of the plugin in case you could use it. It requires a parallel build of > PV. http://www.hpcvis.com/SciVisToolKit/SciVisToolKit-12.02.tar.gz > > Burlen > > On 03/14/2012 10:16 AM, fiss...@pitt.edu wrote: >> Hi, >> >> I didn't see a paraview plugin for the vtkImageGaussianSmooth.cxx >> filter >> in the paraview Filters menus or the filters.xml file. >> Is there some equivalent ? >> >> If not: am I right that I will need to write a little paraview plugin >> wrapper in >> order to be able to use vtkImageGaussianSmooth in a pvpython script ? >> Any reason other than somewhat ugly parameter specification or lack >> of interest/time that there isn't a plugin ? >> >> thanks >> kate >> >> _______________________________________________ >> Powered by www.kitware.com >> >> Visit other Kitware open-source projects at >> http://www.kitware.com/opensource/opensource.html >> >> Please keep messages on-topic and check the ParaView Wiki at: >> http://paraview.org/Wiki/ParaView >> >> Follow this link to subscribe/unsubscribe: >> http://www.paraview.org/mailman/listinfo/paraview > > > _______________________________________________ Powered by www.kitware.com Visit other Kitware open-source projects at http://www.kitware.com/opensource/opensource.html Please keep messages on-topic and check the ParaView Wiki at: http://paraview.org/Wiki/ParaView Follow this link to subscribe/unsubscribe: http://www.paraview.org/mailman/listinfo/paraview