Link to PR: https://github.com/scipy/scipy/pull/18170
I would like to propose a new function for the ndimage measurements module: `bounding_box()`
This function finds the bounding box of the nonzero elements in the input array. It accepts any dimensionality and dtype input.
> Why do I think this is needed?
It is true that same result can be obtained by:
- numpy: `nz=np.nonzero(arr) ; nz[0].min(), nz[0].max(), nz[1].min(),....` : SLOW
- scipy: `ndi.find_objects(arr!=0,max_label=1)` : Faster, implemented in C
I actually implemented `bounding_box` simply by adapting the existing implementation of `find_objects`, so the logic is fundamentally the same.
However, it runs significantly faster - namely between 10% and 30% faster than `find_objects`, per my tests locally.
This is mainly due to the fact that `bounding_box` runs *in-place* on the input array, while running `find_objects(arr!=0) requires allocation of a new array, to store the boolean result of `arr!=0`, which is then passed as the argument to `find_objects`.
Of course, avoiding this allocation also means memory footprint is significantly reduced, and in case of large multidimensional arrays this can be crucial.
The only similar functionality I have found in Python libraries is `PIL.Image.getbbox()` : https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.getbbox
This is limited to 2D images.
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