anomaly_eval op• Data kinds: video × video → table
• Call: import fullseye as fs; fs.ledger.anomaly_pixel_auroc(scores, masks) (to call the implementation directly, import anomeval; anomeval.anomaly_pixel_auroc(scores, masks); from the registry, opsspc.get("anomaly_pixel_auroc"))
Pixel-level AUROC of anomaly score maps against ground-truth defect masks (table).
scores and masks are (n, H, W) (or lists / single 2-D maps); higher score = more anomalous. Exact Mann–Whitney statistic with mid-ranks for ties: the probability that a random defect pixel outscores a random good pixel, ties counting 1/2. Returns auroc, n_pos, n_neg.
Raises ValueError on empty / mismatched / non-finite input, non-binary masks, or when the masks contain no defect pixel or no good pixel (AUROC undefined).
• 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_pro · anomaly_region_overlap · 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.