fullseye

varmodel_alignment — SPC variation op

• データ種: video → table
• 呼び出し: import fullseye as fs; fs.ledger.varmodel_alignment(stack, max_misalign_px=0.5) (実装を直接呼ぶなら import varmodel; varmodel.varmodel_alignment(stack, max_misalign_px=0.5)、台帳から引くなら opsspc.get("varmodel_alignment"))

使い方

Translation offset of every good sample against the typical position of the stack, and the margin to the tolerance (table).

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.

参考(サンプルデータ・文献)

• サンプルデータ カタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
• 演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
• アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

• poc_good_sample_variation_model — py -3.11 examples/poc_good_sample_variation_model.py

型が繋がる次の op(table 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

同カテゴリ(variation)

varmodel_band_k · varmodel_fit · varmodel_calibrate · varmodel_score

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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.

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