ndarray subclasses

[Josh Warner]

Indeed, marching cubes has anisotropic support presently. 

`random_walker` also does, though I think my original API for it is poor 
(only supports anisotropic `depth`, rather than true arbitrary anisotropy 
via `sampling`). I have an improvement/generalization PR for that in the 
works.

I believe the most recent SLIC improvements also added a `sampling` 
parameter.


On Friday, September 20, 2013 6:55:58 AM UTC-5, Almar Klein wrote:
>
> On Thu, Sep 19, 2013 at 12:03 PM, Almar Klein <almar... at gmail.com<javascript:>> 
>> wrote:
>> > I suppose that the most important bit is that functions that support
>> > anisotropy should look whether a "sampling" attribute is present in the
>> > given array.
>>
>> I don't think we currently have any of these, but for now we can
>> probably include a `sampling` argument to functions that support it.
>>
>
> Mmm, you seem to be right, but you're *going* to :)  The new marching 
> cubes algorithm has a sampling argument, and I think Josh spoke about 
> adding it so some morphological operators. I hope to do a PR on the MCP 
> algorithm soon, which will add support for anisotropy as well.
>  
>
>  > I do not necessarily mean that a PointSet represents an image (although 
>> it
>> > could), but more generally to for instance store the locations of 
>> detected
>> > feature points.
>>
>> How do you think this would fit into the scope of image processing?
>> (Asked out of curiosity, not at all to put the idea down.)
>>
>
> That's a good point. I think a PointSet class fits image processing 
> because many image processing algorithms either accept or produce some form 
> of locations or vectors. I would still call it "image processing" when you 
> process the resulting locations/vectors. However, such algorithms probably 
> fall out the scope of scikit-image. 
>
> So probably a better place for a PointSet class would be Scipy, but I have 
> a feeling they would not be interested in including it.
>
>
> I am not related to the development of scikit-image but I guess its goal 
>> is to work on images in general and not get too specialised, for instance 
>> in Medical images.
>>
>
> I agree with that. I think that the only parameter of interest to scikit 
> image is the "sampling" to deal with anisotropic arrays. So in that sense, 
> it should be sufficient that sciki-image provides an Image class to which 
> arbitrary attributes can be attached. Functions in scikit-image can the 
> check if the array has a "sampling" attribute, and use it. If it returns an 
> array with the same shape, it would be nice to also set the sampling on 
> that.
>
>
> May I ask: if you were to add sampling information and spatial 
>> coordinates, such as an origin for the top-left pixel, how would you input 
>> that information into scikit-image, could you read it directly from the 
>> input files or would you need some user input?
>>
>
> I am not sure I understand. In most cases the attributes are added to the 
> image (i.e. numpy array) when it is read. 
>
>
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