[SciPy-user] The IO library and image file formats -- compare with with PIL
Stéfan van der Walt
stefan at sun.ac.za
Mon Apr 21 03:38:35 EDT 2008
On 21/04/2008, Zachary Pincus <zachary.pincus at yale.edu> wrote:
> > I would really try and avoid the forking route, if we could. Each
> > extra dependency (i.e. libpng, libjpeg etc.) is a potential build
> > problem, and PIL already comes packaged everywhere. My changes can
> > easily be included in SciPy, rather than in PIL. Could we do the same
> > for yours? Then we could rather build scipy.image (Travis' and
> > Robert's colour-space codes can be incorporated there, as well?) on
> > top of the PIL.
>
>
> Nothing should be built "on top" of PIL, or any other image IO
> library, IMO. Just build things to work with numpy arrays (or things
> that have an array interface, so can be converted by numpy), and let
> the user decide what package is best for getting bits into and out of
> files on disk. Any explicit PIL dependencies should be really
> discouraged, because of that library's continued unsuitability for
> dealing with scientifically-relevant file formats and data types.
I agree with you, but we still need to provide the user with an easy
way to access images on disk (SciPy comes pretty much batteries
included).
> My thought was thus to take the pure-python file-sniffing
> part of PIL and marry it to numpy tools for taking in byte sequences
> and interpreting them as necessary. This would be have no library
> dependencies, and really wouldn't be a "fork" of PIL so much as using
> a small amount of non-broken PIL file-format-reading code that's there
> and abandoning the awkward/broken byte-IO and memory-model. I can't
> promise I have any time to work on this -- but I'll look into it,
> maybe -- and if anyone else wants to look into it as well, I'm happy
> to provide some code to start with.
How would this code be used in practice? I'm just trying to form a
mental image of how the parts fit together.
> A major difficulty with ndimage, beyond the hairy C-code, is the
> spline-interpolation model that nearly everything is built on. While
> it's technically a nice infrastructure, it's quite dissimilar from
> what a lot of people (well, especially me) are used to with regard to
> how image resampling systems are generally constructed. So that makes
> it a lot harder to hack on or track down and fix bugs. I don't really
> have a good suggestion for addressing this, though, because the spline
> model is really quite nice when it works.
I have the article on which this is based (I think?). It is
"Splines: A Perfect Fit for Signal/Image Processing" by Michael Unser
(http://citeseer.ist.psu.edu/unser99splines.html)
The irony is that we have something like three or four spline
implementations in SciPy! We should re-factor that into a standard
location, but as you can imagine it is no small task.
Regards
Stéfan
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