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Distributed labeling (#94)

* Initial work on proper distributed labeling

* Use vindex (blech)

* Initial untested implementation

* _label_adj_graph working

* Almost working except relabeling

* Add missing imports

* Fix indexing

* Fix imports and total count

* Specify the chunking in `_relabel_components`

By specifying the chunking of the result from `map_blocks`, we are able
to work with an older version of Dask.

* Fix some flake8 errors

* Drop transpose of `all_mappings`

As we can concatenate along a different axis, which serves our purpose
just as well, go ahead and change the code accordingly to avoid a
transpose.

* Use NumPy's `in1d` instead of `isin`

As older versions of NumPy that we support and test against don't
include `isin`, switching to using `in1d`, which has been around longer.
Since the array in question is already 1-D, there is no need for us to
reshape the result after calling `in1d`. So this is sufficient for our
use case.

* Handle empty adjacency graph for singleton chunk

When there is a singleton chunk, there are no shared faces between
chunks to construct an adjacency graph from. In this case ensure there
is at least an empty array to start with. This doesn't alter the cases
where there are multiple chunks that do share faces. Though it does
avoid branching when there is a single chunk with no shared faces.

* Drop unused `i` from `numblocks` `for`-loop

* Fix connected components dtype

Previously we were incorrectly determining the connected components
dtype. This fixes it by inspecting the result on a trivial case and
seeing what the dtype is. Then using that to set the delayed type when
converting to a Dask Array.

* Fix non-zero increment's type to be `LABEL_DTYPE`

* Make sure relabeling array matches label type

* Get right index for connected component array

* Fix incorrect labeling between multiply-matched labels

* Fix test to test equivalent ... (continued)

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