scene op• Data kinds: table × table → image2d
• Call: import fullseye as fs; fs.ledger.optscene_mask(scene, camera, index: 'int') -> 'np.ndarray' (to call the implementation directly, import optscene; optscene.optscene_mask(scene, camera, index: 'int') -> 'np.ndarray'; from the registry, opsoptics.get("optscene_mask"))
• Return value through the ledger: fullseye.ledger.optscene_mask(...) returns only the declared out type image2d (the underlying function also returns auxiliary values). When you need what was dropped, use fullseye.ledger.optscene_mask.raw(...) or call optscene.optscene_mask directly.
A bool ground-truth mask (H, W) of the pixels where primitive number index is visible.
Pixel-exact (no anti-aliasing). IoU against detection results can be measured directly.
Procedure: camera_rays(camera) creates rays for all pixels, trace_rays takes the first intersection, and pixels with hit["index"] == index are set to True. Parts hidden behind another part in front are False (only visible pixels). There is 1 ray through each pixel centre, so border pixels are decided by "whether the centre falls on that part".
• scene: a scene_* dict or a list. index is the position in the list (0-based). Outside the range [0, len(scene)) gives ValueError. scene_difference counts as 1 item.
• camera: result of optical_camera (K, R, eye, width, height).
• Returns: (H, W) bool. If no ray hits, all False (no exception).
Built with the same camera as the render_optscene image, the pixels correspond one-to-one. Depth ground truth is optscene_depth, defect ground truth optscene_defect_mask, the per-part instance list optscene_instances.
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
image2d as input)fraunhofer_pattern · pupil_psf · pupil_blur · psf_to_mtf · edge_spread · veiling_glare_index · emva_pair_statistics · emva_defect_pixels
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.