Improve histogram matching performance on unsigned integer data (resume #6209)

[grlee77]

Description

This PR adds the suggestions from #6209. Benchmark results show a large improvement for unsigned integer types as compared to floating point types. The improvement is > 10x at larger sizes.

[100.00%] ··· benchmark_exposure.MatchHistogramsSuite.time_match_histogram 
[100.00%] ··· ============== =============== ============= =============
              --                                     multichannel       
              ------------------------------ ---------------------------
                  shape           dtype          False          True    
              ============== =============== ============= =============
                 (64, 64)      numpy.uint8     54.8±0.4μs     149±2μs   
                 (64, 64)      numpy.uint32    56.0±0.9μs     154±1μs   
                 (64, 64)     numpy.float32     287±1μs       843±7μs   
                 (64, 64)     numpy.float64    285±0.8μs      827±20μs  
                (256, 256)     numpy.uint8      354±7μs     1.30±0.02ms 
                (256, 256)     numpy.uint32     411±5μs     1.64±0.01ms 
                (256, 256)    numpy.float32   4.25±0.03ms   13.2±0.04ms 
                (256, 256)    numpy.float64    4.40±0.1ms    13.4±0.1ms 
               (1024, 1024)    numpy.uint8    5.33±0.04ms    23.2±0.5ms 
               (1024, 1024)    numpy.uint32   6.35±0.05ms    31.3±0.6ms 
               (1024, 1024)   numpy.float32    87.0±0.2ms     268±3ms   
               (1024, 1024)   numpy.float64    96.2±0.8ms     288±7ms   
              ============== =============== ============= =============

Checklist

• Docstrings for all functions
• Gallery example in ./doc/examples (new features only)
• Benchmark in ./benchmarks, if your changes aren't covered by an
  existing benchmark
• Unit tests
• Clean style in the spirit of PEP8
• Descriptive commit messages (see below)

For reviewers

• Check that the PR title is short, concise, and will make sense 1 year
  later.
• Check that new functions are imported in corresponding __init__.py.
• Check that new features, API changes, and deprecations are mentioned in
  doc/release/release_dev.rst.
• There is a bot to help automate backporting a PR to an older branch. For
  example, to backport to v0.19.x after merging, add the following in a PR
  comment: @meeseeksdev backport to v0.19.x
• To run benchmarks on a PR, add the run-benchmark label. To rerun, the label
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[pep8speaks]

Hello @grlee77! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2022-04-25 12:19:54 UTC

[rfezzani]

Thank you @grlee77, looks good 😉. Can you add a unitest to check the consistency of the result depending on input dtype?

[rfezzani]

Thank you @grlee77!

[grlee77]

Thanks for adding this fast uint code path @kwikwag