Other SciPy N-D generic filters

_From @jakirkham on May 4, 2017 21:3_

Pulled from the listing in issue ( https://github.com/dask-image/dask-ndfilters/issues/13 ).

Some of the generic filters from SciPy N-D filters' module [`scipy.ndimage.filters`]( https://docs.scipy.org/doc/scipy-0.19.0/reference/ndimage.html#filters ) were skipped initially. They are listed below. The intent it to wrap them using the [map_overlap]( http://dask.pydata.org/en/latest/array-api.html?highlight=map_overlap#dask.array.Array.map_overlap ) method of Dask Arrays with appropriate halos. Will also include tests to verify these retain the behavior that they would using ordinary NumPy Arrays. This issue exists as a reminder that these still need to be done.

* [ ] [generic_gradient_magnitude]( https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.ndimage.generic_gradient_magnitude.html )
* [ ] [generic_laplace]( https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.ndimage.generic_laplace.html )

Note: Have excluded 1-D filters from this for now. These have been added to a separate issue to be discussed and addressed later. ( https://github.com/dask-image/dask-ndfilters/issues/14 )

_Copied from original issue: dask-image/dask-ndfilters#22_

Read the scipy.ndimage.filters documentation for generic_gradient_magnitude and generic_laplace, then inspect the existing Dask image filter wrappers that use Array.map_overlap. Implement wrappers with appropriate halos and add tests comparing their behavior with ordinary NumPy arrays; done means both listed filters are covered and the tests pass.

Written by the indexing model from the issue text.