fullseye

feat_image_vector — MULTIVAR `features` op

データ種: images → table
呼び出し: 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' (実装を直接呼ぶなら 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'、台帳から引くなら opsmultivar.get("feat_image_vector"))

使い方

Images -> feature table {"matrix" (N, F), "names", "units", ...}, one row per image.

The bridge from pictures to the multivariate ops: each image becomes a row of named numbers with units, ready for :func:mv_pca / :func:mv_pls / :func:rsm_fit. Features (see :data: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).

詳しい使い方ガイド

design_of_experiments ファミリガイド

参考(サンプルデータ・文献)

サンプルデータカタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

multivar_inspection_tour — py -3.11 examples/multivar_inspection_tour.py

型が繋がる次の op(`table` を入力に取れる)

—

同カテゴリ(`features`)

—

Provenance: multivar.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。

This site is open source. Improve this page.