imaging_sim 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.defect_dataset(n=8, system=None, size=(256, 256), kinds=('scratch', 'pits', 'crack', 'blob'), pixel_pitch_um=5.5, noise=True, seed=0, out_dir=None, zones=3, field_of_view=None, texture='orange_peel', max_defects=2) (to call the implementation directly, import lensimage; lensimage.defect_dataset(n=8, system=None, size=(256, 256), kinds=('scratch', 'pits', 'crack', 'blob'), pixel_pitch_um=5.5, noise=True, seed=0, out_dir=None, zones=3, field_of_view=None, texture='orange_peel', max_defects=2); from the registry, opsoptics.get("defect_dataset"))
Synthetic defect images through a designed lens, with aligned masks (table).
Each of the *n* records draws 1–*max_defects* defects of the listed *kinds* (defectgen scratch / pits / crack / blob with parameters sampled from *seed*) on a surface_texture background of *size*, renders the composite
with render_through_lens (*system* defaults to the cemented doublet
of raytrace.example_system; *noise* as there, default sensor noise on), and pushes each defect's mask through the same distortion remap only (nearest-neighbour, no blur) so the annotation sits where the blurred defect actually landed. A record is {"image", "mask", "defects": [{"kind", "params", "bbox" [x, y, w, h], "area"}], "lens": {"efl", "fno", "rms_spot_center", "rms_spot_corner", "max_distortion_pct"}, "seed"} with arrays, or with file paths when *out_dir* is given (img_0000.png / mask_0000.png written with imgio.save plus a COCO-like annotations.json: images, annotations with bbox/area/params, categories). Deterministic for *seed*; a defect that lands entirely outside the sensor after distortion is dropped from the annotations rather than reported with an empty box.
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.
• mv_cables — Cables (standards, speed, power delivery, robot cables)
• mv_cameras — Industrial camera makers (sensor mapping, line scan / TDI)
• mv_frame_grabbers — Frame grabber boards (not optics, but they decide whether you can capture at all)
• mv_image_sensors — Industrial image sensors (mainly current products)
• mv_standards — Camera interface standards and the bodies behind them
• 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.
• lens_defect_dataset_demo — py -3.11 examples/lens_defect_dataset_demo.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
imaging_sim)psf_from_opd · distortion_map · render_through_lens · calibration_views
*Provenance: lensimage.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.