variation op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.varmodel_alignment(stack, max_misalign_px=0.5) (implementation को सीधे call करना हो तो import varmodel; varmodel.varmodel_alignment(stack, max_misalign_px=0.5), registry से लेना हो तो opsspc.get("varmodel_alignment"))
हर अच्छे sample का stack की सामान्य स्थिति से translation offset, और tolerance तक का margin (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
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.
• 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
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_band_k · varmodel_fit · 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.