Speed up granularity ~2x by only upsampling labeled voxels

[d33bs]

Description

compute_granularity was upsampling the entire subsampled image back to full resolution every scale, just to compute means over a handful of labeled objects. Now it only interpolates/scans the voxels that actually belong to a labeled object — mathematically identical result (verified byte-identical against the old implementation, plus unchanged accuracy-lock fingerprints), ~2.8x faster on real data (5.15s → 1.85s).

Also fixes a pre-existing bug in the benchmark scorecard's real-world colocalization loader (missing image_id) so the full scorecard runs end-to-end.

What kind of change(s) are included?

• Documentation (changes docs or other related content)
• Bug fix (fixes an issue).
• Enhancement (adds functionality).
• Breaking change (these changes would cause existing functionality to not work as expected).

Checklist

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• I have added a test which covers the code changes found within this PR
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[codecov]

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Additional details and impacted files

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[gwaybio]

Great to see the speedup! A few comments, mostly on tests.

[MikeLippincott]

LGTM! This will speed up "~empty" images but the images with high information content and object counts will still take a lot of time to compute.

This change will increase efficiency and increase resource requirement heterogeneity as a by product, which IMO is worth the speed up!

[d33bs]

Thank you @gwaybio and @MikeLippincott !