[Numpy-discussion] ENH: Proposal to add atleast_nd function

[Robert Kern]

On Fri, Feb 12, 2021 at 9:45 AM Joseph Fox-Rabinovitz <
jfoxrabinovitz at gmail.com> wrote:

>
>
> On Fri, Feb 12, 2021, 09:32 Robert Kern <robert.kern at gmail.com> wrote:
>
>> On Fri, Feb 12, 2021 at 5:15 AM Eric Wieser <wieser.eric+numpy at gmail.com>
>> wrote:
>>
>>> > There might be some linear algebraic reason why those axis positions
>>> make sense, but I’m not aware of it...
>>>
>>> My guess is that the historical motivation was to allow grayscale `(H,
>>> W)` images to be converted into `(H, W, 1)` images so that they can be
>>> broadcast against `(H, W, 3)` RGB images.
>>>
>>
>> Correct. If you do introduce atleast_nd(), I'm not sure why you'd
>> deprecate and remove the one existing function that *isn't* made redundant
>> thereby.
>>
>
> `atleast_nd` handles the promotion of 2D to 3D correctly. The `pos`
> argument lets you tell it where to put the new axes. What's unintuitive to
> my is that the 1D case gets promoted to from shape `(x,)` to shape `(1, x,
> 1)`. It takes two calls to `atleast_nd` to replicate that behavior.
>

When thinking about channeled images, the channel axis is not of the same
kind as the H and W axes. Really, you tend to want to think about an RGB
image as a (H, W) array of colors rather than an (H, W, 3) ndarray of
intensity values. As much as possible, you want to treat RGB images similar
to (H, W)-shaped grayscale images. Let's say I want to make a separable
filter to convolve with my image, that is, we have a 1D filter for each of
the H and W axes, and they are repeated for each channel, if RGB. Setting
up a separable filter for (H, W) grayscale is straightforward with
broadcasting semantics. I can use (ntaps,)-shaped vector for the W axis and
(ntaps, 1)-shaped filter for the H axis. Now, when I go to the RGB case, I
want the same thing. atleast_3d() adapts those correctly for the (H, W,
nchannels) case.

-- 
Robert Kern
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