variation op• Data kinds: video → table
• Call: import 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) (implementation को सीधे call करना हो तो import varmodel; varmodel.varmodel_fit(stack, spread='moderated', far=0.001, align='check', max_misalign_px=0.5, spread_floor=0.0, mask=None), registry से लेना हो तो opsspc.get("varmodel_fit"))
अच्छे samples के stack से per-pixel variation model (centre, spread, band की आधी चौड़ाई k) सीखना (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• 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 को input के रूप में लेते हैं)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. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.