fly_lamina_filter — FLYVISION `lamina` op

• 데이터 종류: matrix → matrix

• 호출: import fullseye as fs; fs.ledger.fly_lamina_filter(movie, dt_s, tau_adapt_s=0.2, tau_lp_s=0.02, mode='divisive', floor=0.001)(구현을 직접 호출하려면 import flyvision; flyvision.fly_lamina_filter(movie, dt_s, tau_adapt_s=0.2, tau_lp_s=0.02, mode='divisive', floor=0.001), 원장에서 가져오려면 opsflyvision.get("fly_lamina_filter"))

사용법

> 이 연산자의 설명은 아직 번역이 없습니다. 원문을 그대로 싣습니다.

Photoreceptor adaptation + the lamina's band-pass: intensities in, contrast out.

The first thing the optic lobe does to a picture is throw away its brightness.

A photoreceptor adapts to the running mean light level and the large monopolar

cells (L1/L2) report the *deviation* from it, so the same scene at dawn and at

noon arrives at the motion detectors as the same signal. Two closed-form

stages, in that order:

1. adaptation — a first-order low-pass of time constant *tau_adapt_s*

per ommatidium is the adaptation state `a(t). mode="divisive"`

returns the Weber contrast `(x - a)/(a + eps) (`eps = floor *

mean(x)``, so it scales with the picture and a dark ommatidium cannot

divide by zero); `mode="subtractive" returns x - a`, which is the

same high-pass without the gain control.

2. membrane — a first-order low-pass of time constant *tau_lp_s*, the

cell's own bandwidth.

movie: `(T, n) intensities, rows = time. mode="divisive"` refuses a

negative entry (a negative light level is not a measurement) and an all-zero

movie (its contrast is 0/0, which would be fabricated rather than measured).

dt_s: sample interval, seconds. tau_adapt_s / tau_lp_s: the two time

constants, seconds. floor: the divisive guard, relative to the mean intensity.

Returns `(T, n)` float64 contrast.

Ground truth, both exact rather than approximate:

• Weber invariance. In `"divisive"` mode, scaling the whole movie by

any positive constant returns *the same array* — both `a and eps`

scale with it. That is the point of the stage and the tests pin it to

machine precision.

• The transfer is the product of the two first-order filters. With

`A = 1 - exp(-dt/tau_adapt) and B = 1 - exp(-dt/tau_lp)`, the

steady-state gain at angular frequency `w` is

`|1 - H_A(w)| * |H_B(w)| where H(w) = C/(1 - (1-C) exp(-i w dt))` —

a band-pass that blocks DC exactly and is measured at four frequencies in

the tests.

Raises `ValueError`: a non-2-D / too-short / non-finite *movie*, a movie

over :data:MAX_MOVIE_ELEMENTS, a non-positive *dt_s* / *tau_adapt_s* /

*tau_lp_s*, a non-positive *floor*, an unknown *mode*, and (divisive only) a

negative or all-zero movie.

자세한 사용 가이드

• fly_vision 패밀리 가이드

참고(샘플 데이터·문헌)

• 샘플 데이터 카탈로그(DL URL / 라이선스) —— 2-D 는 skimage.data(BSD/public)+ 합성, 3-D 는 실데이터 소스(Stanford/PDS 등)의 DL URL.

• 연산자의 내력·참고문헌 —— 이 연산자 족의 바탕이 된 연구/기법의 출처.

• 알고리즘의 정전(저자·연도)과 용도는 위의 패밀리 사용 가이드에 적혀 있습니다.

실행 가능한 예제(이 연산자를 실제로 호출하는 검증된 샘플)

• poc_fly_optomotor_steering — py -3.11 examples/poc_fly_optomotor_steering.py

타입이 이어지는 다음 연산자(`matrix` 를 입력으로 받는 것)

fly_onoff_split · fly_t4t5_field · fly_flow_from_directions · fly_egomotion_from_flow · fly_hs_readout

같은 카테고리(`lamina`)

fly_onoff_split

*Provenance: flyvision.py — FLYVISION 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*

fly_lamina_filter — FLYVISION lamina op

사용법