Dividing a large image into smaller overlapping blocks for parallel processing

Riaan van den Dool riaanvddool at gmail.com
Sat Aug 31 15:26:51 EDT 2013


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