[SciPy-User] 2D slice of transformed data
Thu Mar 24 07:16:13 CDT 2011
if I understood correctly, you are foremost interested in visualizing
the data after applying the respective pixel transforms. Could you
not simply use the OpenGL rotate, translate and scale operations ? --
then it could be done literally instantaneously.
There is already code for this in the viewer modules in my Priithon project.
For such large data it would be good to have a video card with 1GB (if
not 2GB) memory, which is now rather cheap (one to a few 100 $) to
buy these days.
I'm not sure but it might even be feasible, once the user has
confirmed that a given transform parameter set is optimal, to read the
transformed pixel values back from the graphics card -- if you really
want that; but I would probably suggest to just store the parameters
to the image data header, and take those into account for all further
visualizations and other image processing you might be doing.
On Thu, Mar 24, 2011 at 2:22 AM, Isaiah Norton <firstname.lastname@example.org> wrote:
> Hi Chris,
> It's not strictly Python, but VTK and ITK are the heavy-iron for this sort
> of thing (py wrappings available). There are several tools built on these
> libraries to provide user-friendly 3D/4D registration, visualization, etc.
> GoFigure2: http://gofigure2.sourceforge.net/
> - very microscopy oriented. 4D support. linux/mac/win
> V3D: http://penglab.janelia.org/proj/v3d/V3D/About_V3D.html
> - also 4D and triplatform.
> - mostly written in Python glue for vtk/itk.
> If you want to build something custom in Python, check out MayaVi - it uses
> VTK under the hood so the transforms will be handled fast in C++, but has
> nice pythonic tvtk syntax and native numpy support.
> On Wed, Mar 23, 2011 at 6:00 PM, Chris Weisiger <email@example.com>
>> In preface, I'm not remotely an expert at array manipulation here. I'm an
>> experienced programmer, but not an experienced *scientific* programmer. I'm
>> sure what I want to do is possible, and I'm pretty certain it's even
>> possible to do efficiently, but figuring out the actual implementation is
>> giving me fits.
>> I have two four-dimensional arrays of data: time, Z, Y, X. These represent
>> microscopy data taken of the same sample with two different cameras. Their
>> views don't quite match up if you overlay them, so we have a
>> three-dimensional transform to align one array with the other. That
>> transformation consists of X, Y, and Z translations (shifts), rotation about
>> the Z axis, and equal scaling in X and Y -- thus, the transformation has 5
>> parameters. I can perform the transformation on the data without difficulty
>> with ndimage.affine_transform, but because we typically have hundreds of
>> millions of pixels in one array, it takes a moderately long time. A
>> representative array would be 30x50x512x512 or thereabouts.
>> I'm writing a program to allow users to adjust the transformation and see
>> how well-aligned the data looks from several perspectives. In addition to
>> the traditional XY view, we also want to show XZ and YZ views, as well as
>> kymographs (e.g. TX, TY, TZ views). Thus, I need to be able to show 2D
>> slices of the transformed data in a timely fashion. These slices are always
>> perpendicular to two axes (e.g. an XY slice passing through T = 0, Z = 20,
>> or a TZ slice passing through X = 256, Y = 256), never diagonal. It seems
>> like the fast way to do this would be to take each pixel in the desired
>> slice, apply the reverse transform, and figure out where in the original
>> data it came from. But I'm having trouble figuring out how to efficiently do
>> I could construct a 3D array with shape (length of axis 1), (length of
>> axis 2), (4), such that each position in the array is a 4-tuple of the
>> coordinates of the pixel in the desired slice. For example, if doing a YX
>> slice at T = 10, Z = 20, the array would look like [[[10, 20, 0, 0], [10,
>> 20, 1, 0], [10, 20, 2, 0], ...], [[10, 20, 0, 1], 10, 20, 1, 1], ...]]. Then
>> perhaps there'd be some way to efficiently apply the inverse transform to
>> each coordinate tuple, then using ndimage.map_coordinates to turn those into
>> pixel data. But I haven't managed to figure that out yet.
>> By any chance is this already solved? If not, any suggestions / assistance
>> would be wonderful.
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