imaging op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.edge_spread(img, roi, *, oversample=4, orientation='auto') (to call the implementation directly, import edgesfr; edgesfr.edge_spread(img, roi, *, oversample=4, orientation='auto'); from the registry, opsoptics.get("edge_spread"))
Builds an ESF finer than a pixel from the ROI of a slanted edge (ISO 12233).
Procedure: align the ROI so that "the edge crosses the rows" (orientation="auto" decides from the gradient magnitudes in the column and row directions, "vertical" = the edge runs vertically / "horizontal" = it runs horizontally) → per row, locate the edge by the centroid of the 1st difference → fit a straight line of position against row number (least squares) → compute each pixel centre's perpendicular distance d from the edge, distribute into bins of width 1/oversample px and average.
Returns {"x": distance of the bin centre [px] (perpendicular to the edge), "esf": bin mean, "count": pixels per bin, "angle_deg": edge tilt (relative to the rows, signed), "oversample", "orientation": orientation used, "edge_x0": edge position in row 0 of the ROI [px]}.
★If the tilt is too small (the pixel phases do not fill the bins), empty bins occur —— no silent interpolation, ValueError instead. This is why ISO 12233 asks for a tilt of about 2–10°. If the tilt exceeds 45°, orientation is the wrong way round.
Raises ValueError: image not 2-D real / NaN, inf / roi outside the image, empty, or smaller than 4 rows × 8 columns / oversample not an integer 1–16 / no edge (change in difference) within a row / tilt of 45° or more / an empty bin occurred.
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.
• measurement_uncertainty — Measurement uncertainty and calibration — what it takes to claim you are measuring
• mv_cameras — Industrial camera makers (sensor mapping, line scan / TDI)
• 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_veiling_glare — py -3.11 examples/poc_veiling_glare.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
imaging)psf_to_mtf · mtf_diffraction · wavefront_stats · sfr_from_edge · mtf50 · veiling_glare_index
*Provenance: edgesfr.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.