Dividing a large image into smaller overlapping blocks for parallel processing

[Riaan van den Dool]

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... at gmail.com<javascript:>>: 
>
>
> > 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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