watervision op• Data kinds: image2d × image2d → table
• Call: import fullseye as fs; fs.ledger.wet_dry_inspection_pair(texture, defect_mask, roughness, water_film_um, light_deg, view_deg, n_water=None, seed=268, normal_sigma_px=2.0) -> 'dict' (to call the implementation directly, import watervision; watervision.wet_dry_inspection_pair(texture, defect_mask, roughness, water_film_um, light_deg, view_deg, n_water=None, seed=268, normal_sigma_px=2.0) -> 'dict'; from the registry, opsoptics.get("wet_dry_inspection_pair"))
The same textured plate rendered dry and wet, and the contrast of its defect in each -> dict.
texture (H, W) in [0, 1] is the diffuse albedo of the plate and defect_mask (H, W, bool) marks the defect (at least one pixel inside and one outside). Both images use the per-pixel wet_surface_appearance for the diffuse part. The specular part uses a local normal: a Gaussian-smoothed random height map (seed, smoothing normal_sigma_px) whose slopes are scaled to an RMS slope per axis of roughness; each pixel mirrors the light with the Fresnel reflectance of its local incidence angle into the Gaussian lobe of wet_surface_appearance about the viewing direction, so a glint appears where the local normal bisects the light and the view and moves when light_deg changes. The dry and the wet plate share the normals, so they differ only by the film. light_deg is in the xz plane from the plate normal, the viewer on the other side at view_deg. The defect contrast is the Michelson contrast (mean_ok - mean_defect) / (mean_ok + mean_defect) of the region means, reported for the whole image and for the diffuse part alone. Because the film darkens dark pixels proportionally more (the factor (1 - r) / (1 - a r) grows with a), a dark defect on a brighter plate gains diffuse contrast when wet; the glints (shared by both regions) pull the total contrast the other way, so the table has both and predicted_diffuse_contrast_wet (the one-step prediction from the region mean albedos).
The table has dry, wet, diffuse_dry, diffuse_wet, specular_dry, specular_wet (images), contrast_dry, contrast_wet, contrast_diffuse_dry, contrast_diffuse_wet, predicted_diffuse_contrast_wet, coverage, glint_pixels_dry / glint_pixels_wet (pixels whose specular term exceeds the median diffuse albedo) and normals. A film of 0 gives identical images. Shapes that differ, a mask without both classes, textures outside [0, 1] or a negative film 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 · underwater_inspection_image · caustic_floor_segmentation
*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.