anomaly_eval op• Data kinds: video × video → table
• Call: import fullseye as fs; fs.ledger.anomaly_region_overlap(predicted, masks, min_overlap=0.5) (to call the implementation directly, import anomeval; anomeval.anomaly_region_overlap(predicted, masks, min_overlap=0.5); from the registry, opsspc.get("anomaly_region_overlap"))
Per-region overlap of a binary defect prediction with the ground-truth regions at one operating point (table).
predicted and masks are binary (n, H, W) (or 2-D). For every 8-connected ground-truth region C: overlap = |predicted ∩ C| / |C|; a region counts as detected when overlap >= min_overlap. Returns overlap (per region, in mask order), pro (their mean = one point of the PRO curve), detected_fraction, hit_any (fraction of regions touched by at least one predicted pixel), fpr (predicted good pixels / good pixels), n_regions, min_overlap.
Raises ValueError on empty / mismatched / non-binary input, no ground-truth region, or min_overlap outside (0, 1].
• 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.
• anomaly_eval_metrics — py -3.11 examples/anomaly_eval_metrics.py
• poc_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py
• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py
table as input)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
anomaly_eval)anomaly_image_auroc · anomaly_pixel_auroc · anomaly_pro · anomaly_pixel_f1_max · anomaly_image_f1_max · anomaly_operating_point
*Provenance: anomeval.py — SPC 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.