feat_image_vector — MULTIVAR `features` op

• 数据种类:images → table

• 调用: import fullseye as fs; fs.ledger.feat_image_vector(images=None, features=('mean', 'std', 'p01', 'p99', 'rms_contrast', 'michelson_contrast', 'grad_energy', 'laplacian_var'), data_range=None, unit='DN', roi=None) -> 'dict'(要直接调用实现,import multivar; multivar.feat_image_vector(images=None, features=('mean', 'std', 'p01', 'p99', 'rms_contrast', 'michelson_contrast', 'grad_energy', 'laplacian_var'), data_range=None, unit='DN', roi=None) -> 'dict';从台账取用则 opsmultivar.get("feat_image_vector"))

用法

图像 -> 特征表 {"matrix" (N, F), "names", "units", ...}，每幅图像一行。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

The bridge from pictures to the multivariate ops: each image becomes a row of named numbers with units, ready for mv_pca / mv_pls / rsm_fit. Features (see FEATURES): mean, std, p01, p50, p99, rms_contrast, michelson_contrast, grad_energy, laplacian_var need no range; saturated_fraction, dark_fraction, entropy_bits need data_range=(lo, hi) — the range of the *sensor or format* (e.g. (0, 4095) for 12-bit), which the data cannot tell: the brightest pixel of a dim image is not the white level. It is therefore required, never inferred, and pixels outside it are refused (the range given is then wrong).

unit names the grey-level unit ("DN" by default) used in the unit strings. roi=(r0, r1, c0, c1) restricts every feature to that window (same for all images).

详细使用指南

• design_of_experiments 族使用指南

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)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

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同类别(`features`)

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

feat_image_vector — MULTIVAR features op

用法