scene op• Data kinds: none → table (an op determined by its arguments alone — it takes no image or data input)
• Call: import fullseye as fs; fs.ledger.scene_material(kind: 'str' = 'lambert', albedo=0.6, metal: 'str' = 'al', finish: 'str' = 'random', glass: 'str' = 'N-BK7', sigma_per_mm: 'float' = 0.0, roughness_um: 'float' = 0.05) -> 'dict' (to call the implementation directly, import optscene; optscene.scene_material(kind: 'str' = 'lambert', albedo=0.6, metal: 'str' = 'al', finish: 'str' = 'random', glass: 'str' = 'N-BK7', sigma_per_mm: 'float' = 0.0, roughness_um: 'float' = 0.05) -> 'dict'; from the registry, opsoptics.get("scene_material"))
Make one material. kind is one of three: lambert / conductor / dielectric.
There are only 3 kinds because mixing them goes silently wrong; to add more, split the types (diffuse, metal or transparent fundamentally changes where rays go).
lambert: perfectly diffuse, using only albedo (scalar or 3 RGB elements).
conductor: the colour is set by the complex refractive index of metal (glassmirror.METALS),
and the anisotropic lobe width by finish (metalfinish.FINISHES). No colour is specified.
roughness_um is the surface roughness Rq [µm]. The fraction returned as specular is set by
exp(-(4πσcosθ/λ)²) (Davies 1954 / Bennett-Porteus 1961), so
this 1 value decides specular (0.01 = polished) versus diffuse-leaning (1.0 = satin finish).
The wavelength enters the formula, so the same surface looks different depending on the light source's wavelength.
dielectric: refracts with the Sellmeier dispersion of glass (raytrace.glass_catalog()),
and absorbs with Beer–Lambert using sigma_per_mm.
Returns: a dict storing the above, validated (pass it as is to the other scene_* ops).
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 — Training-dataset conventions — COCO / YOLO / VOC and pitfalls in visual inspection
• mv_cameras — Industrial camera makers (sensor mapping, line scan / TDI)
• mv_illumination_practice — Lighting in practice — wavelength, polarisation, drive modes, ambient light, safety
• mv_image_sensors — Industrial image sensors (mainly current products)
• virtual_machine_vision — Virtual machine vision — enumerating the parameters and an object model
• 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.
• studio_raytrace_scene — py -3.11 examples/studio_raytrace_scene.py
• virtual_machine_vision — py -3.11 examples/virtual_machine_vision.py
• vision_layout_from_catalog — py -3.11 examples/vision_layout_from_catalog.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
scene)scene_plane · scene_sphere · scene_box · scene_cylinder · surface_defect · surface_finish · random_defects · scene_difference
*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.