vol_rle_volume — 3D `rle_region` op

• Data kinds: rle_region → measurement

• Call: import fullseye as fs; fs.ledger.vol_rle_volume(region) -> 'int' (to call the implementation directly, import volregion; volregion.vol_rle_volume(region) -> 'int'; from the registry, ops3d.get("vol_rle_volume"))

Usage

Voxel count of the region, computed on the runs (no decode). Measured

> The detailed description below is the original text — the summary and the headings are translated.

~300x faster than summing the dense mask.

計算: `sum(ends - starts)`(差は int64 で取るので run 数が多くても溢れない)。

返り値は Python の `int`(voxel 個数、単位は voxel。物理体積 mm^3 が欲しければ

`spacing の積 sz*sy*sx` を自分で掛ける — この op は spacing を受けない)。

入力は `_require_rle で検証してから使う(VolRLE` 以外、shape・run 配列の

不整合、範囲外の run は `ValueError)。空の region は 0` を返す(エラーに

しない)。`vol_rle_decode してから sum()` するのと同じ値を、密配列を作らずに

返すのが目的。`vol_rle_components` の各成分にかければ成分ごとの体積になる。

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

Runnable examples (verified samples that actually call this op)

• rle_region_efficiency — py -3.11 examples_3d/rle_region_efficiency.py

Ops the type connects to (they accept `measurement` as input)

vol_gaussian_psf · fuse_to_voxel · fresnel_reflectance · snell_angle

Same category (`rle_region`)

vol_rle_encode · vol_rle_decode · vol_rle_bbox · vol_rle_centroid · vol_rle_union · vol_rle_intersect · vol_rle_difference · vol_rle_components

*Provenance: volregion.py — 3D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

vol_rle_volume — 3D rle_region op