scene op• Data kinds: table × points × table → signal
• Call: import fullseye as fs; fs.ledger.illumination_visibility(scene, points, light) -> 'np.ndarray' (to call the implementation directly, import optscene; optscene.illumination_visibility(scene, points, light) -> 'np.ndarray'; from the registry, opsoptics.get("illumination_visibility"))
The visible fraction of the emitters seen from each point, in [0, 1] (i.e. cast shadow).
For each point, occlusion toward every emitter point is tested and averaged. In setups where the light source is behind the part, like a backlight, it becomes 0 and the silhouette stands out. Returns (M,).
Procedure: from each point p take the unit direction d toward each emitter point e, and shoot a ray from p + 1e-4 d (an offset of 0.1 µm so as not to re-hit its own surface). If it hits any primitive before e, it is "blocked". Visibility = number of unblocked emitter points / total number of emitter points. Geometric occlusion only; the emitters' orientation (directions), intensity, cosine law and distance attenuation are not included (those belong to the shading side of render_optscene).
• scene: a scene_* dict or a list of them. ValueError if empty.
• points: (M, 3) [mm], finite. To pass points on surfaces, use trace_rays(...)["point"]. A point with emitters behind its surface is blocked by its own part and becomes 0.
• light: result of light_spec or light_source (a dict with emitters and directions). ValueError if missing.
• Returns: (M,) float64 in [0, 1]. With n emitter points, in steps of 1/n.
• Cost is number of points × number of emitter points × number of primitives.
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
signal as input)emva_photon_transfer · emva_quantum_efficiency · emva_linearity_error · emva_snr_curve · emva_dark_current · cie_xyz_from_wavelength · spectrum_to_srgb · thin_film_reflectance
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.