scene op• Data kinds: table × table × table → table
• Call: import optscene; optscene.inspection_dataset(scene, camera, lights, n: 'int' = 8, seed: 'int' = 0, exposure_ms: 'float' = 10.0, bit_depth: 'int' = 8, jitter_mm: 'float' = 0.0, tilt_jitter_deg: 'float' = 0.0, intensity_jitter: 'float' = 0.0, depth: 'int' = 1, defects: 'dict' = None, supersample: 'int' = 1, adaptive: 'bool' = False, light_samples: 'int' = None, environment=None, environment_gain: 'float' = 1.0) -> 'list' (or opsoptics.get("inspection_dataset"))
Generate n training images for a visual-inspection AI, with pixel-perfect labels.
> The detailed description below is the original text — the summary and the headings are translated.
同じ部品を、照明(`lights` に複数渡すと 1 枚ごとに巡回)・置き方
(`jitter_mm の並進、tilt_jitter_deg` のカメラ傾き)・明るさ
(`intensity_jitter` の相対ゆらぎ)を振って撮る = ドメインランダム化。
返り値は 1 枚あたり dict:
`image 量子化済み (H, W, 3) / defect_mask` 欠陥の真値 /
`part_mask 部品の真値 / depth_mm` 深度の真値 /
`meta` 使った照明種別・露光・ゆらぎ量・欠陥ラベル(再現に必要な値をすべて)。
`defects に :func:random_defects` の引数 dict を渡すと、**1 枚ごとに
欠陥を引き直す**(`scene` の先頭を対象にする)。これが外観検査 AI の学習
データ生成そのもので、欠陥の種類・位置・大きさ・深さと照明が同時に振れる。
`seed` を固定すれば決定的。同じ欠陥でも照明を変えると見え方が変わる
ことがこの生成器の要点で、だから照明を振った枚数が効く。
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
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.