fly_egomotion_from_flow — FLYVISION `selfmotion` op

• データ種: matrix × table → table

• 呼び出し: import fullseye as fs; fs.ledger.fly_egomotion_from_flow(flow, lattice, axes=None, weights=None) (実装を直接呼ぶなら import flyvision; flyvision.fly_egomotion_from_flow(flow, lattice, axes=None, weights=None)、台帳から引くなら opsflyvision.get("fly_egomotion_from_flow"))

使い方

フロー場から眼の回転を線形最小二乗で求め、識別できたかを条件数で返す。

> 以下の詳細説明は原文のままです —— 要約と見出しは訳出済み。

Given the flow `f_i at known viewing directions d_i`, a pure rotation

`w predicts f_i = -(w x d_i)`, which is linear in w: projecting on

the tangent basis gives `f_az = -w . (d x e_az)` and

`f_el = -w . (d x e_el), so the estimate is one 2n x 3` least-squares

solve with no iteration and no starting guess (Franz et al.'s linear

egomotion estimate, *Biol. Cybern.* 2004).

The catch is not the algebra, it is the eye. A single patch of ommatidia sees

a small piece of the sphere, and over a small piece the flow of a yaw and the

flow of a sideways translation — or of a pitch — look nearly the same. This

op therefore returns the condition number of that solve next to the

answer, so that "the fit converged" and "the fit was identifiable" stay

separate claims.

flow: `(n, 2)` azimuth/elevation components per ommatidium

(:func:fly_flow_from_directions or :func:fly_matched_filter).

lattice: the eye they were measured on.

axes: `None to solve for the full 3-D rotation, or a (k, 3)` array of

axes to restrict the fit to (`[[0, 0, 1]]` = yaw only, the well-conditioned

question a forward-looking eye can actually answer).

weights: `None or (n,)` non-negative per-ommatidium weights — a

confidence, e.g. the local contrast, or zeros to drop the rim.

Returns a dict::

{"omega_rad_s": (3,), "yaw_rad_s": float, "pitch_rad_s": float,

"roll_rad_s": float, "residual_rms": float, "flow_rms": float,

"explained": float, "condition": float, "n_ommatidia": int}

with yaw about +z (left positive), pitch about +y, roll about +x, and

`explained = 1 - residual_rms/flow_rms` (1.0 = the flow is exactly a

rotation, 0.0 = the fit explains none of it).

Ground truth: handed a :func:fly_matched_filter template scaled by a known

rate, it returns that rate to machine precision and `explained = 1`; handed

a pure translation field it returns a small rate with a low `explained`; and

the condition number of a narrow forward eye is large (the tests measure it)

while the yaw-only fit is near 1.

Raises `ValueError: a *flow* that is not (n, 2)` for this lattice,

non-finite entries, a malformed *axes* / *weights*, all-zero weights, and a

lattice with fewer ommatidia than the fit has unknowns.

詳しい使い方ガイド

• fly_vision 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)

• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py

型が繋がる次の op(`table` を入力に取れる)

fly_t4t5_field · fly_flow_from_directions · fly_matched_filter · fly_eye_merge · fly_hex_resample · fly_hs_readout

同カテゴリ(`selfmotion`)

fly_matched_filter · fly_eye_merge

*Provenance: flyvision.py — FLYVISION 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)。*

fly_egomotion_from_flow — FLYVISION selfmotion op

使い方