You also might want to look into joblib which makes it very easy to do parallel computations. This is used frequently in sklearn to speedup code.

http://pythonhosted.org/joblib/


On Saturday, August 31, 2013 3:26:51 PM UTC-4, Riaan van den Dool wrote:
Thanks

On Saturday, August 31, 2013 8:17:25 PM UTC+2, Johannes Schönberger wrote:
Some hints:

 - pad image with skimage.util.pad, which allows a large number of padding methods
 - spawn a pool of processes using Python's multiprocessing package in the standard library
 - use shared memory to provide read access to complete image
 - define slices of image blocks and add them to a processing queue

Am 31.08.2013 um 20:05 schrieb Riaan van den Dool <riaan...@gmail.com>:

> The blockproc function's signature provides a useful starting point, thanks.
> http://www.mathworks.com/help/images/ref/blockproc.html
>
> I will have to think about how to do the parallel execution from the function.
>
> Blockproc provides two 'padding' methods: replicate and symmetric. I guess what I need could be called margin, or overlap perhaps.
>
> For the margin case it might make sense that such a function merely returns an array of block definitions, rather than blocks of pixel data. But this would not be so applicable for the replicate and symmetric cases I think.
>
> R
>
>  
>
> On Saturday, August 31, 2013 6:49:31 PM UTC+2, Johannes Schönberger wrote:
> Hi Riaan,
>
> Unfortunately we do not have (at least I do not know of) a function similar to Matlab's `blockproc`. Such feature would be a great addition to skimage!
>
> Regards, Johannes
>
> Am 31.08.2013 um 16:04 schrieb Riaan van den Dool <riaan...@gmail.com>:
>
> > Hi guys
> >
> > I would like to use scikit-image to process large images, for example (5696, 13500).
> >
> > In the interest of speed I need to divide the image into smaller sub-images with the possibility of processing these in parallel.
> >
> > If I define the sub-images so that neighbouring sub-images overlap then edge effects should not be a problem for the algorithm operating on each sub-image.
> >
> > This is probably a specific case of the more general border/edge-effect handling issue as addressed by the mode parameter here:
> > http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.filters.convolve.html
> >
> > My questions:
> >         • Is there already a image-division function/strategy implemented in scikit-image?
> >         • Is this something that might be included in future if an implementation is available?
> >         • Please share any references to articles or code that deals with this.
> > Riaan
> >
> >
> >  
> >
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