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.observe_surface(material: 'str' = 'al', finish: 'str' = 'hairline', pitch_um: 'float' = 90.0, depth_um: 'float' = 1.0, roughness_um: 'float' = 0.08, focal_mm: 'float' = 25.0, working_distance_mm: 'float' = 200.0, f_number: 'float' = None, na: 'float' = None, pixel_um: 'float' = 3.45, resolution=(256, 256), wavelength_nm: 'float' = 550.0, illumination: 'str' = 'coaxial', tilt_deg: 'float' = 0.0, source_size_mm: 'float' = 40.0, supersample: 'int' = 2, exposure: 'str' = 'auto', defects: 'dict' = None, seed: 'int' = 0) -> 'dict' (to call the implementation directly, import optscene; optscene.observe_surface(material: 'str' = 'al', finish: 'str' = 'hairline', pitch_um: 'float' = 90.0, depth_um: 'float' = 1.0, roughness_um: 'float' = 0.08, focal_mm: 'float' = 25.0, working_distance_mm: 'float' = 200.0, f_number: 'float' = None, na: 'float' = None, pixel_um: 'float' = 3.45, resolution=(256, 256), wavelength_nm: 'float' = 550.0, illumination: 'str' = 'coaxial', tilt_deg: 'float' = 0.0, source_size_mm: 'float' = 40.0, supersample: 'int' = 2, exposure: 'str' = 'auto', defects: 'dict' = None, seed: 'int' = 0) -> 'dict'; from the registry, opsoptics.get("observe_surface"))
Assemble an observation optic and shoot the finished surface of a named material (a one-line entry point).
User request (2026-09-05): "It would be nice to assume a layout of a basic optical system for observation, give its parameters, specify a material such as aluminium, and generate a hairline image. Coaxial illumination is fine to start with."
The optical system can be specified by NA or F-number. From working distance, focal length, pixel pitch and wavelength, optical_budget derives magnification, Airy radius, depth of field and resolution limit, and these act directly on the imaging (diffraction blur via diffraction_blur, the depth dependence of blur via depth_of_field). The material is glassmirror.METALS, the finish is a kind of surface_finish.
illumination is coaxial / ring / dome / bar / backlight. With coaxial, if the surface is a mirror the fixture is reflected and it is bright (bright field); a low-angle ring is not reflected and so it is dark (dark field).
Return value dict: image (linear RGB) / camera / scene / light / budget (the optical budget above) / defect_mask (when defects is passed).
★ The default arguments take 117 seconds. It is described as "an entry point usable in one line", but the default resolution=(256, 256) / supersample=2 is a 2-minute computation (measured 2026-09-06, on this machine):
============== ============= =============
resolution supersample 1 supersample 2
============== ============= =============
32 x 32 0.25 s —
64 x 64 0.69 s —
128 x 128 2.40 s 19.10 s
256 x 256 19.12 s 116.93 s
============== ============= =============
The cost per pixel is not constant (32→256 is 64 times the pixels, yet 76 times the time). The breakdown: _light_background takes 11.0 of 16.2 seconds, a loop over 196 area-light points x 25 specular candidates. A rewrite that processes the emitter points together was tried, but gave only 1.3 times (the bottleneck is memory bandwidth, not the Python loop), and it is not yet solved —— it is recorded in docs/KNOWN_ISSUES.md.
Call it small when trying it: observe_surface(resolution=(64, 64), supersample=1) takes 0.69 seconds and shows the differences in how finishes look well enough. The chain fuzzer is also fixed to 32 x 32 / supersample 1 for this reason (typed_catalog.OP_PARAM_HINTS).
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.
• 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.