variation opvideo → tableimport fullseye as fs; fs.ledger.varmodel_fit(stack, spread='moderated', far=0.001, align='check', max_misalign_px=0.5, spread_floor=0.0, mask=None, *, chunk=262144) (実装を直接呼ぶなら import varmodel; varmodel.varmodel_fit(stack, spread='moderated', far=0.001, align='check', max_misalign_px=0.5, spread_floor=0.0, mask=None, *, chunk=262144)、台帳から引くなら opsspc.get("varmodel_fit"))Learn a per-pixel variation model (centre, spread, band half-width k) from a stack of good samples (table).
stack is (n, H, W) or a list of equal 2-D images, all good. spread chooses the
estimator (SPREADS): std (mean / per-pixel sample std, needs n >= 3), pooled
(mean / one std for all valid pixels — few samples, homoscedastic noise), moderated
(mean / empirical-Bayes shrinkage of every pixel’s variance toward a prior estimated from
all pixels, Smyth 2004 — few samples, heteroscedastic noise; the default) or mad
(median / 1.4826 MAD — robust to a few defective images hidden in the “good” set, needs
n >= 5). far is the declared two-sided per-pixel false-alarm rate; k follows from it
(see varmodel_band_k) and is never tuned on defects.
Alignment: align="check" measures every sample’s translation (varmodel_alignment)
and REFUSES the stack if any exceeds max_misalign_px; align="translate" registers the
samples to the median by a Fourier shift (all-pass, so white noise keeps its variance),
re-checks the residual against max_misalign_px and marks the wrapped border invalid.
A stack without structure (alignment["observable"] False) is neither refused nor shifted;
its band is then only an intensity threshold on the overall variation. Stochastic textures,
whose every sample shows a different pattern, are therefore not a variation-model problem
(on DAGM 2007 Class1 it scores at chance — see the PoC).
spread_floor (>= 0, image units) lower-bounds the spread (8-bit data has pixels whose
good samples are all equal); any remaining zero spread is refused. mask (H, W) bool
limits the inspected area. Returns center, spread, k, far, n, df,
spread_kind, lower / upper (the band), valid (bool), n_floored,
d0 / s0 (the prior’s degrees of freedom and scale: 0 for std / mad; for pooled and a
fully pooled moderated prior d0 = valid pixels x (n - 1)), k_source,
mc_far_rel_se and alignment (the offset report, after registration if any).
Raises ValueError on an empty / malformed / non-finite stack, too few samples, an
unknown spread / align, far outside its range, a misaligned stack, a mask of the
wrong shape, or zero spread left after the floor.
py -3.11 examples/poc_good_sample_variation_model.pypy -3.11 examples/poc_patch_anomaly.pytable を入力に取れる)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
variation)varmodel_alignment · varmodel_band_k · varmodel_calibrate · varmodel_score
Provenance: varmodel.py — SPC operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.