typed op• 데이터 종류: lightfield → image
• 호출: fullseye.apply(img, "tb_lf_depth_from_focus", a=0.5, b=0.5)(2-D 는 이미지 1 장 + 스칼라 노브 2 개 a,b∈[0,1] 모델)
리포커스 스윕 전체에서 선명도 피크로부터 구한 픽셀별 기울기.
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Refocus at every slope in *slopes*, measure local sharpness (*measure*:
`laplacian` = summed modified Laplacian, the classical depth-from-focus
operator; `variance = local variance; gradient` = local gradient
energy) in a `window x window` neighbourhood, and take the slope at which
each pixel is sharpest. With `subpixel=True` (default) the peak is refined
by fitting a parabola through the winning sample and its two neighbours on a
uniformly spaced sweep — on a non-uniform sweep the refinement is
skipped rather than applied with the wrong spacing.
Unbiased where :func:lf_epi_slope is not: measured 2026-09-01 on a
5x5x64x64 synthetic field over a 121-point sweep from -3 to +3, the argmax
landed exactly on the true slope in 18 of 18 combinations (true slopes
0.0, +0.5, +1.0, +1.5, +2.0, -1.0 crossed with texture sigma 1.5 / 3.0 /
5.0 px), and the sub-pixel refinement left every one of them unmoved. Its
resolution, though, is whatever you put in *slopes* — it cannot see a plane
you never refocused on.
Returns `(slope_map, sharpness): the (H, W)` map of estimated slopes
(in px per angular step) and the `(H, W)` peak focus-measure value, which
is the honest confidence — a textureless pixel has no sharpness peak, gets
an essentially arbitrary slope, and its `sharpness` is ~0. Threshold on it
rather than trusting the map everywhere.
Raises `ValueError`: *lf* not a valid light field, *slopes* empty /
over :data:MAX_STACK_SLICES / over :data:MAX_STACK_ELEMENTS / containing
a non-finite or over-large value, an even or non-positive *window*, unknown
*measure* / *interp* / *edge*.
Typed bridge of the lightfield op `lf_depth_from_focus into the 2-D evolution registry: the same implementation, called under the op(v, a, b) convention. This op has no tunable parameter; a and b` are unused.
• 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)
image 를 입력으로 받는 것)identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter
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.