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"))
> 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 (fly_flow_from_directions or 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 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.
• 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。
• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py
table 作为输入)fly_t4t5_field · fly_flow_from_directions · fly_matched_filter · fly_eye_merge · fly_hex_resample · fly_hs_readout
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)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.