scene op• Data kinds: table × table × table → rgbimage
• Call: import optscene; optscene.render_optscene(scene, camera, lights, ambient: 'float' = 0.0, depth: 'int' = 2, shadows: 'bool' = True, supersample: 'int' = 1, adaptive: 'bool' = False, light_samples: 'int' = None, edge_threshold: 'float' = 0.06, saturate_at: 'float' = None, wavelength_nm: 'float' = 550.0, environment=None, environment_gain: 'float' = 1.0, environment_samples: 'int' = 12) -> 'np.ndarray' (or opsoptics.get("render_optscene"))
Shoot for inspection: place real luminaires in physical units and count the direct light (with ground truth).
> The detailed description below is the original text — the summary and the headings are translated.
見た目を作るための多重反射・環境光は入れない。きれいな絵が要るなら
:func:render_studio ―― 作り方が違う別の op で、取り違えると測光の
根拠が濁る。返り値は線形 RGB の放射輝度 (H, W, 3)、トーンマップ前。
`lights は illumdesign.light_source` の結果(1 つでも並べてもよい)。
`ambient は一様な環境光、depth` は誘電体を通す再帰の深さ、
`shadows=False` で落ち影を切る(速いが、影がある前提の検査には使えない)。
`supersample` は 1 画素あたりの標本を n×n に増やす ―― 実際の画素は面積を
積分するので、縁や加工目の階段状のギザギザはこれで消える(コストは n² 倍)。
大量生成のための 3 つのつまみ(速度は生成器の機能):
• `adaptive=True` ―― まず 1 標本で撮り、幾何の縁と輝度勾配が大きい画素だけ
を n×n に割る。平坦な面には標本を捨てないので、縁の質を保ったまま速くなる。
• `light_samples` ―― 面光源の発光点をこの数へ間引く(重みで明るさを補正)。
落ちるのは影の縁の柔らかさで、明るさの絶対値ではない。
• `saturate_at` ―― この輝度を超える画素は精細化しない(どうせ白飛びする)。
`wavelength_nm` は光源の波長。面粗さ σ の面が鏡面として返す割合
exp(-(4πσcosθ/λ)²) に入るので、同じ面でも波長で見え方が変わる。
広帯域(ハロゲン)なら数波長で撮って足す、レーザーなら単一波長で撮る。
`environment` を渡すと、見せる絵の作り方をここに混ぜられる: 金属の異方性
ローブで環境を引いた分を `environment_gain` 倍して足す。ブラシ目の金属が
金属に見えるのは環境が目に沿って引き伸ばされて映るからで、点光源だけでは硬い
縞にしかならない(2026-09-05 のユーザー着眼)。既定は off ―― 測光の根拠が要る
検査画像に、見た目のための項を黙って混ぜないため。`optscene.env_studio` を
渡すのが手軽で、自前の関数((...,3) 方向 -> (...,) 明るさ)でもよい。
表示用に丸めたい場合は自分で `**(1/2.2)` する。実レンズの歪曲・PSF・
周辺光量・センサ雑音を足すには `lensimage.render_through_lens` を後段に、
量子化と読み出し雑音だけなら `sensor_capture` を後段に掛ける。
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.
• virtual_machine_vision — py -3.11 examples/virtual_machine_vision.py
rgbimage as input)clearcoat_shade · wetness · defocus_blur · diffraction_blur · sensor_capture
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.