scene op• Data kinds: rgbimage × image2d × table → rgbimage
• Call: import optscene; optscene.defocus_blur(image, depth_mm, camera, f_number: 'float' = 5.6, focus_mm: 'float' = None, layers: 'int' = 7) -> 'np.ndarray' (or opsoptics.get("defocus_blur"))
Apply depth-of-field blur from a ground-truth depth map (ideal pinhole image → the image of a real lens).
> The detailed description below is the original text — the summary and the headings are translated.
名前が optics.depth_of_field と紛らわしいので分けてある: あちらは被写界深度の
数値を返し、こちらは画像にぼけを掛ける(2026-09-05 に台帳で同名衝突を
起こして発覚。同名 op は後勝ちで静かに上書きされるので、名前で区別する)。
錯乱円の直径は幾何光学の閉じた式
`c = (f² / (N (z_f − f))) · |z − z_f| / z`(f = 焦点距離, N = F 値, z_f = 合焦距離)。
画素数に直して、深度でまとめた層ごとにガウスぼかしを掛け、手前の層から合成する。
これが無いと「視野には入っているのに実機では合焦しない」構成を見逃す ――
仮想化の目的は絵を作ることではなく、その光学構成で検査が成立するかを
先に知ることなので、被写界深度は省けない。
`focus_mm を省くと camera` の作動距離に合わせる。返り値は入力と同じ形。
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.
• poc_focus_stacking — py -3.11 examples/poc_focus_stacking.py
• virtual_machine_vision — py -3.11 examples/virtual_machine_vision.py
rgbimage as input)clearcoat_shade · wetness · 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.