Why would I need the GPU to do parallel operations? I thought most
modern processors have vector units. I just don't know if there's a
way to have my code use them.
I tried a quick test with pyopengl (as quick as can be done in that
crazy api), but I found adding two textures of the same size as ain
took nearly as long as numpy (maybe twice as fast but I need it to be
more like 10x faster). I can send you the code if you like, but it
may be getting a bit out of scope for this forum.
Thanks,
Brendan
On 16-Sep-08, at 10:04 AM, David Huard wrote:
Brendan,
Not sure if I understand correctly what you want, but ...
Numpy vector operations are performed in C, so there will be an
iteration over the array elements.
For parallel operations over all pixels, you'd need a package that
talks to your GPU, such as pyGPU.
I've never tried it and if you do, please report your experience,
I'd be very interested to hear about it.
HTH,
David
On Tue, Sep 16, 2008 at 4:50 AM, Stéfan van der Walt
<[EMAIL PROTECTED]> wrote:
Hi Brendan
2008/9/16 brendan simons <[EMAIL PROTECTED]>:
> #interpolate the green pixels from the bayer filter image ain
> g = greenMask * ain
> gi = g[:-2, 1:-1].astype('uint16')
> gi += g[2:, 1:-1]
> gi += g[1:-1, :-2]
> gi += g[1:-1, 2:]
> gi /= 4
> gi += g[1:-1, 1:-1]
> return gi
I may be completely off base here, but you should be able to do this
*very* quickly using your GPU, or even just using OpenGL. Otherwise,
coding it up in ctypes is easy as well (I can send you a code snippet,
if you need).
> I do something similar for red and blue, then stack the
interpolated red,
> green and blue integers into an array of 24 bit integers and blit
to the
> screen.
>
> I was hoping that none of the lines would have to iterate over
pixels, and
> would instead do the adds and multiplies as single operations.
Perhaps numpy
> has to iterate when copying a subset of an array? Is there a
faster array
> "crop" ? Any hints as to how I might code this part up using
ctypes?
Have you tried formulating this as a convolution, and using
scipy.signal's 2-d convolve or fftconvolve?
Cheers
Stéfan
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