EuroSciPy sprint

[Tony Yu]

On Thu, Aug 9, 2012 at 1:31 AM, Stéfan van der Walt <stefan at sun.ac.za>wrote:

> Hi, everyone
>
> We'll be sprinting at EuroSciPy, and would like to solicit ideas.
> Currently, the sprint description reads as follows.  I'd love to hear
> other suggestions!
>
> """
>
> scikits-image is a collection of algorithms for image processing. See
> http://skimage.org for more detail.
>
> Ideas for the sprint:
>
> - Add text, anti-aliasing to the draw module
> - Integrate Tony's visualization tools
> - Add binary features (BRIEF, BRISK, FREAK)
> - Update the user guide
> - Better video loading
> - Hough transform parameter space explorer
>
> Outstanding challenges:
>
> - Blurring kernel estimation: http://bit.ly/Nril3u
> -
> http://stackoverflow.com/questions/10196198/how-to-remove-convexity-defects-in-sudoku-square/11366549#11366549
>
> And write up this one as an example:
>
> -
> http://stackoverflow.com/questions/8686926/python-image-processing-help-needed-for-corner-detection-in-preferably-pil-or/9173430#9173430
> """
>
> Regards
> Stéfan
>


It's depressing to see so many suggestions repeated from our last sprint.
That said, we did get a number of great contributions (many weren't on our
original list).

It sounds like there might be a *3rd* implementation of a an extensible
image viewer, so I won't be offended if you punt on the Qt-MPL viewer PR.
If you do tackle it, I think a good exercise would be to have someone
implement a new plugin for the viewer. (Maybe something as simple as
adapting parts of skivi.)

In case there are people at the sprint looking for simple problems to
tackle, I've also added a number of issues labeled quickfix, that *should*
in theory be fairly easy to implement. (Although the decision on the
correct implementation may or may not be so easy, but that's why it's nice
to have people in one room).

To get specific on updating the User Guide: I think a "Getting Started"
page would be great. Something that takes the user through a common
workflow; e.g.:

1) load an image
2) try using a feature detector or transform.
3) realize that it didn't perform ideally. Now try smoothing or denoising
(or something else).
4) successfully find feature.
5) save feature as some sort of data file.

One more item to the list:
- `imread_collection` should work for any plugin that implements `imread`.
(I guess I'll add this as an issue).

In regards to the Hough transform parameter space explorer, I have a
not-so-elegant implementation here:
https://github.com/tonysyu/scikits-image/blob/qtmpl-houghplugin/viewer_examples/plugins/hough_transform_simple.py

Cheers,
-Tony
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