piv_flow_to_rgbimage — PIV `visualise` op

• Data kinds: flow2d → rgb

• Call: import pivops; pivops.piv_flow_to_rgbimage(flow, scale=None) (or opspiv.get("piv_flow_to_rgbimage"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

色相 = 向き、明度 = 速さの標準的なフロー可視化。返りは `(h, w, 3)`。

`reprconv.flow_to_rgbimage の 2 次元版(あちらは (3, D, H, W)` の

3-D シーンフロー専用で、平面フローは形で弾かれる)。

色相環の凡例を図の側で必ず一緒に焼くこと —— 色の意味が書いていない

フロー図は綺麗なだけで読めない。`scale` を省くと最大の速さで正規化する

ので、図ごとに色の意味が変わる。複数の図を並べるなら明示的に固定する。

Args:

flow: `(2, h, w)`。

scale: 明度 1.0 に対応する速さ [px]。`None` で最大値。

Returns:

`(h, w, 3)` float64、値域 [0, 1]。

Detailed usage guide

• piv_displacement family guide

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.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• (none yet)

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

—

Same category (`visualise`)

piv_line_integral_convolution

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

piv_flow_to_rgbimage — PIV visualise op