anomaly_patch op• Data kinds: video → table
• Call: import fullseye as fs; fs.ledger.patchbank_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, coreset=0.1, proj_dim=32, position_weight=0.0, seed=0) (implementation को सीधे call करना हो तो import anomaly_patch; anomaly_patch.patchbank_fit(stack, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4, coreset=0.1, proj_dim=32, position_weight=0.0, seed=0), registry से लेना हो तो opsspc.get("patchbank_fit"))
अच्छे patch features का memory bank बनाना, greedy k-centre covering से छाँटा हुआ (table)।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
stack is (n, H, W) or (n, H, W, 3) of good images (n >= 1). Features are standardised with the mean and the standard deviation of each dimension over all good patches. With position_weight > 0 the grid position (0..1 in each direction) times position_weight * sqrt(D) is appended, so a patch is compared with good patches from nearby positions (0 = position-free). coreset in (0, 1] is the fraction of patches kept: the greedy k-centre cover picks, from a seeded start, always the patch farthest from the ones already kept, measuring distances in a seeded random projection to proj_dim dimensions (None = exact). The covering radius in that space after the selection is returned: the largest distance from any good patch to the bank. coreset=1 keeps everything (radius 0, no selection, so seed and proj_dim have no effect).
Returns the model: bank (m, D'), mean / std (D), n_total patches, n_memory, fraction, covering_radius, the feature parameters and image_shape.
Raises ValueError for invalid parameters, a feature that is constant over all good patches, or a stack that would exceed the memory guard.
• 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_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
anomaly_patch)patch_features · patchdist_fit · patchdist_score · patchbank_score
*Provenance: anomaly_patch.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.