variation op• Datenarten: video → table
• Aufruf: import fullseye as fs; fs.ledger.varmodel_alignment(stack, max_misalign_px=0.5) (die Implementierung direkt: import varmodel; varmodel.varmodel_alignment(stack, max_misalign_px=0.5); aus dem Register: opsspc.get("varmodel_alignment"))
Verschiebung jedes Gutteils gegenüber der typischen Lage des Stapels und der Abstand zur Toleranz (table).
> Die ausführliche Beschreibung unten ist der Originaltext — Zusammenfassung und Überschriften sind übersetzt.
Each image of stack (n, H, W) is phase-correlated (filters_freq.phase_correlation_fft) against a reference built from the stack itself (the most typical sample, then the registered mean); the integer peak is refined to 1/20 px by an upsampled DFT (Guizar-Sicairos et al. 2008). Offsets are relative to the median offset, so a common shift of every sample is not a misalignment. Returns offset (n, 2) = (row, col) in px (positive = the sample sits further down / right than the typical one), abs_offset (n,) = the larger of the two, margin_px = max_misalign_px - abs_offset (negative = this sample breaks the tolerance), ok (all margins >= 0), worst (index of the most shifted sample) and psr (peak-to- sidelobe ratio of the correlation), shift_sd_px (Cramér–Rao lower bound of the offset's
spread: noise variance over the structural gradient energy of the registered mean; the window makes the real spread a few times larger) and observable. A featureless or pure-noise stack has no structure, so its offsets cannot be measured (observable=False, offsets reported as 0, shift_sd_px capped at the image size) — and for the same reason a shift cannot fatten its band, so such a stack is not refused.
Why it matters: a per-pixel spread learned from a misaligned stack is inflated wherever the image has gradient (edges), so the tolerance band gets fatter and real defects there are missed.
Raises ValueError on an empty stack, a stack that is not (n, H, W) / a list of equal 2-D images, non-finite values, or max_misalign_px <= 0.
• Katalog der Beispieldaten (Download-URLs / Lizenzen) — 2-D nutzt skimage.data (BSD/Public Domain) plus synthetische Bilder, 3-D nennt Download-URLs echter Datenquellen (Stanford, PDS, …).
• Herkunft und Literatur der Operatoren — die Quellen der Forschung/Verfahren, auf denen diese Operatorfamilie beruht.
• Der kanonische Algorithmus (Autor, Jahr) und seine Anwendungen stehen im Familienleitfaden oben.
• poc_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py
table als Eingabe)msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo
variation)varmodel_band_k · varmodel_fit · varmodel_calibrate · varmodel_score
*Provenance: varmodel.py — SPC Operator-Registry. Diese Notiz wird von tools/opdocs.py md erzeugt (nicht von Hand bearbeiten).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.