patch_features — SPC anomaly_patch op

• 数据种类:image2d → table

• 调用: import fullseye as fs; fs.ledger.patch_features(image, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4)(要直接调用实现,import anomaly_patch; anomaly_patch.patch_features(image, patch=8, stride=4, scales=(1.0, 2.0, 4.0), groups=('gradient', 'gabor', 'lbp'), n_orient=4);从台账取用则 opsspc.get("patch_features"))

用法

网格上的经典图块特征:每个图块一个 D 维向量(梯度、Gabor、LBP、频带、颜色)(table)。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

image is (H, W) or (H, W, 3). Pixel channels are computed at every Gaussian scale in scales (sigma, px) and averaged over patch x patch windows every stride px:

intensity (smoothed mean), gradient (Gaussian-derivative magnitude and the doubled-angle components |g|cos2t, |g|sin2t: the structure tensor), gabor (zero-mean Gabor energy, wavelength 4 sigma, n_orient orientations), lbp (histogram of the 10 rotation-invariant uniform local binary patterns, radius = round(sigma)), color (red-green and yellow-blue opponents, needs a colour image). fourier is computed on the raw patches, not per scale: the log share of a Hann-windowed patch's spectrum in three radial bands. Returns features (gh, gw, D), names (D), grid (gh, gw), dim and the parameters.

Raises ValueError on an empty / non-finite / wrongly shaped image, an image smaller than patch, unknown groups, or color on a gray image.

参考(示例数据、文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者、年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_patch_anomaly — py -3.11 examples/poc_patch_anomaly.py

类型可衔接的下一个算子(可接受 table 作为输入)

msa_anova_table · msa_gauge_rr · msa_bias_linearity · msa_attribute_agreement · gum_standard_uncertainty · gum_propagate · gum_expanded · gum_monte_carlo

同类别(anomaly_patch)

patchdist_fit · patchdist_score · patchbank_fit · patchbank_score


*Provenance: anomaly_patch.py — SPC 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.