scene op• Data kinds: table × table × table → rgbimage
• Call: import fullseye as fs; fs.ledger.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' (to call the implementation directly, 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'; from the registry, opsoptics.get("render_optscene"))
Shoot for inspection: place real luminaires in physical units and count the direct light (with ground truth).
No multiple reflections or ambient light for appearance are included. If you need a pretty picture, use render_studio ―― a separate op built differently; mixing them up muddies the basis of the photometry. Returns linear RGB radiance (H, W, 3), before tone mapping.
lights is the result of illumdesign.light_source (1 or several side by side). ambient is uniform ambient light, depth the recursion depth through dielectrics, shadows=False turns off cast shadows (faster, but unusable for inspections that assume shadows). supersample raises the samples per 1 pixel to n×n ―― real pixels integrate over their area, so this removes the staircase jaggies at edges and machining marks (cost n² times).
3 knobs for mass generation (speed is a feature of the generator):
• adaptive=True ―― first shoot with 1 sample, then split only pixels with geometric edges or large luminance gradients into n×n. No samples are wasted on flat surfaces, so it gets faster while keeping edge quality.
• light_samples ―― thins the emitter points of area lights to this number (brightness corrected by weights). What is lost is the softness of shadow edges, not the absolute brightness.
• saturate_at ―― pixels above this luminance are not refined (they clip to white anyway).
wavelength_nm is the wavelength of the light source. It enters the fraction a surface of roughness σ returns as specular, exp(-(4πσcosθ/λ)²), so the same surface looks different depending on wavelength. For broadband (halogen) shoot at several wavelengths and add; for a laser shoot at a single wavelength.
Passing environment lets you mix the way display pictures are made into this: the environment drawn through the anisotropic lobe of metals is multiplied by environment_gain and added. Brushed metal looks like metal because the environment is reflected stretched along the brush marks; with point lights alone it becomes only hard stripes (user insight of 2026-09-05). Default is off ―― so as not to silently mix terms for appearance into inspection images that need a photometric basis. Passing optscene.env_studio is the easy way; your own function ((...,3) direction -> (...,) brightness) also works.
To round for display, apply **(1/2.2) yourself. To add the distortion, PSF, vignetting and sensor noise of a real lens, apply lensimage.render_through_lens afterwards; for quantization and read noise only, apply sensor_capture afterwards.
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.
• 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.