ndarray subclasses

[Kevin Keraudren]

Le 19/09/2013 10:36, Almar Klein a �crit :
>
> 1) Allowing extra attributes for the Image class, like "sampling" that 
> specifies the distance between the pixels. This attribute can then be 
> used by algorithms to take anisotropy into account, and visualization 
> toolkits could use it to scale the image in the correct way 
> automatically. This may not seem a very common use case for 2D images, 
> but 3D data is usually not isotropic.
>
> Other attributes that I think may be of use for the Image class are 
> "origin" that specifies the location of the topleft pixel relative to 
> an arbitrary coordinate frame, and "meta" for the meta data (e.g. EXIF 
> tags). 

Hi,

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 am working on a Python interface for a C++ medical imaging library, 
and I chose to subclass np.ndarray, images keeping a header information 
( 'dim', 'orientation', 'origin' and 'pixelSize'), the main feature is 
to keep track of those spatial coordinate when we slice or resample the 
array.

As St�fan pointed out " when you slice out a scalar, or sum, you get an 
Image object out!", but this has not been an issue so far, I just hide 
it by overriding:
     def __str__(self):
         if len(self.shape) == 0:
             return str(self.view(np.ndarray))
         else:
             return self.__repr__()

The documentation I placed online might give you more ideas on what 
could be done with a subclass of np.ndarray dedicated to images:
http://www.doc.ic.ac.uk/~kpk09/irtk/#irtk.imread

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?

Kind regards,

Kevin
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