watervision op• Data kinds: image2d → table
• Call: import fullseye as fs; fs.ledger.caustic_floor_segmentation(irradiance, method='otsu') -> 'dict' (to call the implementation directly, import watervision; watervision.caustic_floor_segmentation(irradiance, method='otsu') -> 'dict'; from the registry, opsoptics.get("caustic_floor_segmentation"))
Segment the caustic lines of a floor irradiance image -> dict (mask, threshold, line period).
The bright web of caustics on a pool floor is what a crack detector mistakes for cracks. method="otsu": the finite values are normalised by their mean and split into two classes at the Otsu threshold (256 bins); the mask is the bright class. method="fold": ridge detection by curvature: the smaller eigenvalue of the Hessian of the Gaussian-smoothed irradiance (sigma 1 pixel) is the curvature across a line; where it is strongly negative (above the Otsu threshold of its negative part) and the irradiance is above its mean, the pixel is on a caustic ridge. inf pixels (the folds of the wavefront method of caustic_irradiance) are caustic by definition and are always in the mask. The table has mask (bool), threshold, fraction (of pixels in the mask), period_px_x / period_px_y (the fundamental period in pixels of the column and row profiles of the mask, from the lowest strong spectral peak refined by a parabola; NaN when the profile is flat), dominant_axis ("x", "y" or "none": the axis whose profile of the mask varies most) and n_inf. NaN, negative values, an image under 8 x 8 and a constant image (no caustic to find) are refused.
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.
• 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_water_inspection_images — py -3.11 examples/poc_water_inspection_images.py
table as input)abcd_matrix · wavefront_stats · sfr_from_edge · paraxial_trace · seidel_coefficients · spot_stats · tolerance_analysis · wavefront_from_opd
watervision)caustic_irradiance · bottle_image · fill_level_from_image · wet_surface_appearance · wet_dry_inspection_pair · underwater_inspection_image
*Provenance: watervision.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.