scene op• Data kinds: table → table
• Call: import fullseye as fs; fs.ledger.layout_capture(layout: 'dict', exposure_ms: 'float' = 10.0, supersample: 'int' = 2, spectral_samples: 'int' = 1, apply_diffraction: 'bool' = True, apply_defocus: 'bool' = False, raw: 'bool' = False, mode: 'str' = 'rgb') -> 'dict' (to call the implementation directly, import optscene; optscene.layout_capture(layout: 'dict', exposure_ms: 'float' = 10.0, supersample: 'int' = 2, spectral_samples: 'int' = 1, apply_diffraction: 'bool' = True, apply_defocus: 'bool' = False, raw: 'bool' = False, mode: 'str' = 'rgb') -> 'dict'; from the registry, opsoptics.get("layout_capture"))
Shoot exactly as the layout says (spectrum → diffraction → depth of field → sensor).
spectral_samples > 1 superposes the spectrum of the light source (broadband sources such as halogen need this; 1-3 lines suffice for LED, 1 for a laser). With raw=True the radiance before quantization is also returned.
mode has two values (the default is "rgb", as before):
• "rgb" —— materials are visible RGB (metal colour, RGB albedo); transmission and QE are scalars independent of wavelength.
• "spectral" —— image = Σ_λ w(λ) · radiance(λ) · T_lens(λ) · QE(λ) · λ/550 nm. w is the energy weight from light_wavelengths, λ/550 nm is the energy → photon-count ratio (λ/hc), QE(λ) is the qe_curve of sensor_spec or a Si model (n,k from Green 2008, matched to quantum_efficiency at qe_reference_nm), and radiance(λ) is captured with the material reduced to that wavelength (metals use n,k from the material table, dielectrics use n(λ) and optionally absorption from k, lambert uses reflectance_curve). The 3 channels hold the same value (monochrome sensor). With a single 550 nm line, flat QE, flat transmission and a grey lambert it gives the same numbers as "rgb".
Diffraction blur is applied per wavelength and then summed (the Airy radius is proportional to λ). Until 2026-10-15 it was applied with only the single wavelength of the budget. If there is coaxial illumination, the image is multiplied by the transmittance of the half mirror.
Return value dict: image (quantized) / radiance (when raw) / depth_mm / part_mask / defect_mask / budget / mode / spectral_terms (list of wavelength, weight and T·QE·photon ratio) / assumptions (assumptions made on the spectral path).
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.
• poc_nir_silicon_transmission — py -3.11 examples/poc_nir_silicon_transmission.py
• poc_optics_drawings — py -3.11 examples/poc_optics_drawings.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_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.