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.sensor_spec(pixel_um: 'float' = None, resolution=None, quantum_efficiency: 'float' = None, full_well_e: 'float' = None, read_noise_e: 'float' = None, dark_e_per_s: 'float' = 5.0, bit_depth: 'int' = 8, gain_e_per_unit: 'float' = 50000.0, shutter: 'str' = None, model: 'str' = None, qe_curve=None, qe_reference_nm: 'float' = None, si_collection_um: 'float' = 3.0) -> 'dict' (to call the implementation directly, import optscene; optscene.sensor_spec(pixel_um: 'float' = None, resolution=None, quantum_efficiency: 'float' = None, full_well_e: 'float' = None, read_noise_e: 'float' = None, dark_e_per_s: 'float' = 5.0, bit_depth: 'int' = 8, gain_e_per_unit: 'float' = 50000.0, shutter: 'str' = None, model: 'str' = None, qe_curve=None, qe_reference_nm: 'float' = None, si_collection_um: 'float' = 3.0) -> 'dict'; from the registry, opsoptics.get("sensor_spec"))
An image sensor's specification. It holds only the values on the side that decides the number of electrons.
quantum_efficiency is the photon -> electron conversion efficiency, dark_e_per_s the dark current (this matters in dark field, where exposures are long), shutter global / rolling (parts on a conveyor show rolling-shutter distortion).
Optical brightness (how many photons arrive) is set by the lens and layout; here only how it is recorded is held ―― mixing them leads to untraceable changes like "I changed the sensor and the field of view changed too".
Passing a real model number to model (a key of sensor_catalog()) fills in, in addition to resolution, pixel pitch and shutter, QE, read noise and full-well capacity from the EMVA1288 measurements published by the camera maker (values given explicitly as arguments take precedence). The source is camera measurements, so with a different camera they change a little. The QE in the ledger is the value at 545 nm (the source table says "All measurements have been taken at 545 nm"), so qe_reference_nm becomes 545.
★ Whether a value is "explicit" is decided by whether the argument was passed (default None). Until 2026-10-15 it was compared with the default value, so explicitly giving read_noise_e=2.5 or quantum_efficiency=0.6 was silently replaced with the ledger value (e.g. 14.0 e-) for the reason that it equalled the default. Explicit values leave their names in explicit_overrides. pixel_um / shutter contradicting the model number raise ValueError (the sensor's physics is fixed by the model number). An explicit resolution is honoured as an ROI (exceeding the ledger's pixel count raises ValueError).
The quantum efficiency curve QE(λ) used in spectral mode (layout_capture(mode="spectral")):
• Passing pairs(λ [nm], QE) to qe_curve uses it as is (a datasheet curve; no extrapolation).
• If omitted, the Si photodiode model spectralrad.silicon_photodiode_qe (n,k of Si from Green 2008) is built with collection depth si_collection_um (default 3 µm), and scaled so that at qe_reference_nm it matches quantum_efficiency. It is a model of the shape, not a datasheet curve.
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.