typed op• 데이터 종류: qimage → image
• 호출: fullseye.apply(img, "tb_monogenic_orientation", a=0.5, b=0.5)(2-D 는 이미지 1 장 + 스칼라 노브 2 개 a,b∈[0,1] 모델)
모노제닉 신호의 국소 방향 `atan2(R2, R1)`. → (H, W).
> 아래 상세 설명은 원문입니다 —— 요약과 제목은 번역되어 있습니다.
Radians in `[0, pi): an orientation is defined modulo pi` (a grating at
10 degrees and one at 190 degrees are the same grating), and the value is
folded into that range rather than left in `(-pi, pi]` where the same
structure would read as two different numbers on either side of a contrast
reversal. `display=True maps it to [0, 1]`.
Continuous, not quantised — the angle is read directly from two filters,
for any angle, where a steerable bank with `K` orientations interpolates
between its `K`. Measured against eight grid-exact grating orientations the
error is at most 3.6e-15 rad, including the obliques. (Whether that
buys anything downstream is a separate question, and the measured answer is
mostly *no* — see :func:riesz_displacement.)
Where it is undefined, and the mask is not the one you expect. The
orientation dies where the *Riesz vector* dies, which is at every
even-symmetric point — local phase 0 or pi, the crest of a bright or dark
line — and the amplitude is at full strength there. Measured on a 45-degree
grating, the worst orientation error over the whole frame is 0.2764 rad, at a
pixel where `|R| = 6.8e-16 and :func:monogenic_amplitude` reads
`1.0000`. So masking on the amplitude does not protect you; mask on
`hypot(q[..., 1], q[..., 2])`, the Riesz magnitude. With that mask the
error over the same eight orientations is at most 3.6e-15 rad.
Where the Riesz vector is exactly zero, `atan2(0, 0) = 0` is returned —
a *value*, not a measurement.
Raises `ValueError`: the input is not a valid quaternion field, or its
`k` component is non-zero; *display* is not a bool.
Typed bridge of the quat op `monogenic_orientation 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.