seg_contingency — EMPROOF score op

• Data kinds: labels2d × labels2d → table

• Call: import fullseye as fs; fs.ledger.seg_contingency(a, b, ignore_label=None) (to call the implementation directly, import segcompare; segcompare.seg_contingency(a, b, ignore_label=None); from the registry, opsemproof.get("seg_contingency"))

Usage

The contingency table of truth a against candidate b (sparse).

Returns labels_a / labels_b (the distinct labels), rows / cols (indices into them) and counts for every co-occurring pair, plus n (pixels counted), row_sums (size of each truth object) and col_sums (size of each candidate object). counts.sum() == n, row_sums.sum() == col_sums.sum() == n.

Raises ValueError: non-integer, non-finite, boolean-free-but-string or masked input; not 2-D / 3-D; empty; different shapes; every pixel ignored.

Detailed usage guide

• emproof family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• (none yet)

Ops the type connects to (they accept table as input)

seg_synapse_partners · seg_wiring_variation · seg_synapse_nri · seg_wiring_exposure

Same category (score)

seg_variation_of_information · seg_rand

*Provenance: segcompare.py — EMPROOF operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.