feat_image_vector — MULTIVAR `features` op

• 資料種類:images → table

• 呼叫: 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'(要直接呼叫實作,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';從台帳取用則 opsmultivar.get("feat_image_vector"))

用法

影像 -> 特徵表 {"matrix" (N, F), "names", "units", ...}，每張影像一列。

> 以下的詳細說明為原文 —— 摘要與標題已翻譯。

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).

詳細使用指南

• design_of_experiments 族使用指南

參考(範例資料、文獻)

• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。

• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。

• 演算法的正典(作者、年份)與用途見上面的族使用指南。

可執行的範例(實際呼叫該運算子並已驗證的樣例)

• multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

型別可銜接的下一個運算子(可接受 `table` 作為輸入)

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同類別(`features`)

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*Provenance: multivar.py — MULTIVAR 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*

feat_image_vector — MULTIVAR features op

用法