typed op• 데이터 종류: signal → signal
• 호출: fullseye.apply(img, "tb_smooth_funct_1d_mean", a=0.5, b=0.5)(2-D 는 이미지 1 장 + 스칼라 노브 2 개 a,b∈[0,1] 모델)
반복 이동 평균 평활화(HALCON `smooth_funct_1d_mean`).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Applies a length-*size* uniform (box) filter *iterations* times with
`nearest` (edge-replicating) boundary handling. Repeated box filtering
approaches a Gaussian (central limit theorem).
:param y: 1-D function, at least 1 sample.
:param size: window length in samples; truncated to int, must be >= 1.
Even sizes are accepted but shift the window origin by half a sample
(scipy's origin convention) — prefer odd sizes for a symmetric window.
:param iterations: number of passes; truncated to int, must be >= 0.
`iterations=0` returns the (float64-coerced) input unchanged.
:returns: smoothed float64 array, same length as *y*.
:raises ValueError: non-1-D / NaN / Inf input, empty input, `size < 1`,
or `iterations < 0`.
Typed bridge of the 1d op `smooth_funct_1d_mean into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. a drives size (default 3) and b drives iterations` (default 1).
• 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.
• (아직 없음)
signal 를 입력으로 받는 것)identity · tb_create_funct_1d_array · tb_smooth_funct_1d_gauss · tb_derivate_funct_1d · tb_integrate_funct_1d · tb_zero_crossings_funct_1d · tb_abs_funct_1d · tb_negate_funct_1d
typed)tb_points_to_voxel · tb_estimate_point_normals · tb_iss_keypoints · tb_angle_3points · tb_project_points · tb_render_point_depth · tb_statistical_outlier_removal · tb_radius_outlier_removal
*Provenance: ops.py — 2D 연산자 레지스트리. 이 op 노트는 tools/opdocs.py md 가 자동 생성합니다(직접 편집하지 마세요).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.