fly_egomotion_from_flow — FLYVISION `selfmotion` op

• 数据种类:matrix × table → table

• 调用: import fullseye as fs; fs.ledger.fly_egomotion_from_flow(flow, lattice, axes=None, weights=None)(要直接调用实现,import flyvision; flyvision.fly_egomotion_from_flow(flow, lattice, axes=None, weights=None);从台账取用则 opsflyvision.get("fly_egomotion_from_flow"))

用法

> 该算子的说明尚无译文,以下照原文给出。

Least-squares rotation of the eye from its flow field — and how badly the

eye's own shape conditions the answer.

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.

详细使用指南

• fly_vision 族使用指南

参考(示例数据・文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者・年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py

类型可衔接的下一个算子(可接受 `table` 作为输入)

fly_t4t5_field · fly_flow_from_directions · fly_matched_filter · fly_eye_merge · fly_hex_resample · fly_hs_readout

同类别(`selfmotion`)

fly_matched_filter · fly_eye_merge

*Provenance: flyvision.py — FLYVISION 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

fly_egomotion_from_flow — FLYVISION selfmotion op

用法