scene op• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)
• Call: import optscene; optscene.observe_surface(material: 'str' = 'al', finish: 'str' = 'hairline', pitch_um: 'float' = 90.0, depth_um: 'float' = 1.0, roughness_um: 'float' = 0.08, focal_mm: 'float' = 25.0, working_distance_mm: 'float' = 200.0, f_number: 'float' = None, na: 'float' = None, pixel_um: 'float' = 3.45, resolution=(256, 256), wavelength_nm: 'float' = 550.0, illumination: 'str' = 'coaxial', tilt_deg: 'float' = 0.0, source_size_mm: 'float' = 40.0, supersample: 'int' = 2, exposure: 'str' = 'auto', defects: 'dict' = None, seed: 'int' = 0) -> 'dict' (or opsoptics.get("observe_surface"))
Assemble an observation optic and shoot the finished surface of a named material (a one-line entry point).
> The detailed description below is the original text — the summary and the headings are translated.
ユーザー要望(2026-09-05)「基本的な観察用の光学系をレイアウトした場合を想定して、
それらのパラメータを与えて、アルミなどの素材を指定した上で、ヘアライン画像を
生成できるといいな。最初は同軸照明でも良い」。
光学系は NA でも F 値でも指定できる。作動距離・焦点距離・画素ピッチ・波長から
:func:optical_budget が倍率・エアリー半径・被写界深度・分解限界を出し、その
まま撮像に効く(回折ぼけは :func:diffraction_blur、ぼけの深さ方向は
:func:depth_of_field)。素材は `glassmirror.METALS`、仕上げは
`surface_finish` の種類。
`illumination` は coaxial / ring / dome / bar / backlight。同軸は面が鏡なら
器具が映って明るく(明視野)、低角のリングは映らないので暗い(暗視野)。
返り値 dict: `image(線形 RGB)/ camera / scene / light` /
`budget(上の光学バジェット)/ defect_mask(defects` を渡したとき)。
★ 既定の引数は 117 秒かかる。 「一行で使える入口」と書いてあるが、
既定 `resolution=(256, 256) / supersample=2` は 2 分の計算である
(2026-09-06 実測、この機械):
============== ============= =============
resolution supersample 1 supersample 2
============== ============= =============
32 x 32 0.25 s —
64 x 64 0.69 s —
128 x 128 2.40 s 19.10 s
256 x 256 19.12 s 116.93 s
============== ============= =============
画素あたりの費用が一定ではない(32→256 で画素は 64 倍なのに時間は 76 倍)。
内訳は `_light_background` が 16.2 秒中の 11.0 秒で、面光源 196 点 x 鏡面
候補 25 回のループ。発光点をまとめて処理する書き換えは試したが 1.3 倍
にしかならず(律速は Python のループではなくメモリ帯域)、まだ解いていない
—— `docs/KNOWN_ISSUES.md` に残してある。
試すときは小さく呼ぶこと: ``observe_surface(resolution=(64, 64),
supersample=1)`` なら 0.69 秒で、仕上げの見え方の違いは十分わかる。
連鎖ファザーもこの理由で 32 x 32 / supersample 1 に固定してある
(`typed_catalog.OP_PARAM_HINTS`)。
Every optics op validates its input before computing (nothing slips through silently):
• Units are baked into the argument name — _mm / _um / _deg / _mrad. Confusing mm with µm does not crash; it yields a plausible-looking wrong answer, so the name prevents it. Nothing here guesses the unit from the magnitude.
• **Strings raise ValueError** — float('50') succeeds, so an unparsed configuration value would slip through as a length (measured: thin_lens('50', '200') returned a plausible 66.667 mm). bool is refused too, as the implicit promotion True == 1.
• **complex / masked arrays raise ValueError (real-valued slots only; silently dropping the imaginary part or peeling off the mask is refused). NaN/Inf raises ValueError on every input.**
• Division by zero and its relatives are refused by name: focal length 0, radius of curvature 0, refractive index <= 0, a fully opaque aperture (all zeros, so the normalisation is 0/0), a PSF whose sum is <= 0, a Stokes vector with S0 = 0, and an object sitting at the front focal point (the image is at infinity).
• Only two ops return a non-finite value, and both state it as a contract: depth_of_field returns far_mm = inf beyond the hyperfocal distance (that is what the hyperfocal distance means), and gaussian_beam returns wavefront_radius_mm = inf at the waist (the radius of curvature of a plane wavefront). Both also return a finite companion (far_is_infinite / curvature_per_mm). **Any other silent NaN/Inf is detected internally and raises ValueError** — "float64 overflowed" and "the answer is infinite" are different claims, so the first is never returned wearing the face of the second.
• Size caps: generated grids are capped by optics.MAX_GRID (4096); supplied fields/PSFs/apertures by optics.MAX_FIELD_ELEMENTS (2^24); ABCD element chains by optics.MAX_SYSTEM_ELEMENTS (1024); Zernike by MAX_ZERNIKE_TERMS (512) / MAX_ZERNIKE_ORDER (40) / MAX_ZERNIKE_BASIS (2^25). This closes, fail-closed, the paths where a small argument triggers a huge internal allocation (measured: n_max=40 × 4096² needs 108 GB).
• Physically impossible states are refused too: a Stokes vector with degree of polarisation > 1, negative transmittance, negative intensity, and invalid Zernike indices such as n-|m| odd.
• dataset_conventions — 学習データセット規約の知識 — COCO / YOLO / VOC と外観検査での落とし穴
• mv_cameras — 産業用カメラメーカー（センサとの紐付け・ラインスキャン / TDI）
• mv_illumination_practice — 照明の実務知識 — 波長・偏光・点灯方式・外光・安全
• mv_image_sensors — 産業用イメージセンサ（現行品中心）
• virtual_machine_vision — 仮想マシンビジョン — パラメータの洗い出しとオブジェクト模型
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• vision_layout_from_catalog — py -3.11 examples/vision_layout_from_catalog.py
table as input)abcd_matrix · wavefront_stats · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd · spot_diagram
scene)scene_material · scene_plane · scene_sphere · scene_box · scene_cylinder · surface_defect · surface_finish · random_defects
*Provenance: optscene.py — OPTICS operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.