polarization op• Data kinds: image2d × image2d → polsweep
• Call: import specularity; specularity.polarization_render(diffuse, specular, angles_deg=(0.0, 45.0, 90.0, 135.0), azimuth_deg=0.0) (or opsspecular.get("polarization_render"))
Forward model of a polariser sweep: turn a known split into the frames a polarisation camera would record. → (N, H, W).
`I(t) = 0.5 * diffuse + specular * cos^2(t - azimuth)`. The diffuse term is
treated as completely unpolarised, so it contributes half its radiance at
every analyser angle; the specular term is treated as completely linearly
polarised at *azimuth_deg*, so it follows Malus's law. Those are the two
assumptions :func:polarization_separate inverts, and rendering with them
is how the inversion gets a ground truth to be exact against.
Physically the assumptions hold near Brewster's angle for a dielectric and
fail at normal incidence; the module docstring says where. This operator
does not model the incidence angle at all — it takes the two radiance maps
you specify and produces the sweep they imply.
Raises `ValueError`: *diffuse* / *specular* are not 2-D arrays of the
same shape, or are complex / masked / non-finite / string-typed; either has
a negative value (a negative radiance is not a scene); *angles_deg* has
fewer than 3 entries or does not determine the three unknowns.
• specular_photometric family guide
• 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_polarization_specular — py -3.11 examples/poc_polarization_specular.py
• specular_photometric — py -3.11 examples/specular_photometric.py
polsweep as input)polarization_separate · polarization_dolp_map · polarization_stokes
polarization)polarization_separate · polarization_dolp_map · polarization_stokes
*Provenance: specularity.py — SPECULAR 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.