photometric op• Data kinds: images → image2d
• Call: import fullseye as fs; fs.ledger.photometric_residual(images, lights, normals=None, albedo=None, normalize=True) (implementation को सीधे call करना हो तो import specularity; specularity.photometric_residual(images, lights, normals=None, albedo=None, normalize=True), registry से लेना हो तो opsspecular.get("photometric_residual"))
हर pixel पर Lambertian model कितनी बुरी तरह विफल होता है। → (H, W) RMS residual।
> नीचे का विस्तृत विवरण मूल text में है — सारांश और headings का अनुवाद हो चुका है।
sqrt(mean_n (albedo * (n.L_n) - I_n)^2) — the root-mean-square disagreement between the linear model and the measurements, in the units of the input radiance. On a synthetic Lambertian surface with the true float64 normals and albedo supplied it measures 1.4e-16 at worst; with them omitted the floor rises to 4.5e-08, because photometric.photometric_stereo returns float32 and that is its precision, not a modelling error (supplying the *same* truth cast to float32 reproduces 4.5e-08 exactly). It is large where the assumption actually broke: 0.50 at worst on the same scene with 3 of 8 lights blocked by a cast shadow — fifteen orders of magnitude above the clean floor. All four numbers measured in tests/test_specularity.py.
This is the diagnostic that tells you *whether* you need photometric_stereo_robust before you reach for it, and it is the map an inspection routine thresholds to find glossy defects.
With *normals* and *albedo* omitted it solves them first with photometric.photometric_stereo and reports the residual of that fit — the honest self-assessment of the plain estimator. Pass them to score an estimate that came from somewhere else (a robust fit, a CAD model, a previous frame).
Note the residual uses n.L without the max(., 0) clamp, because that is the linear system the solver actually inverted; a pixel in attached shadow therefore shows a residual, which is the intended signal rather than an artefact.
Raises ValueError: *images* / *lights* problems as in photometric_stereo_robust; *normals* is not (H, W, 3) matching the images; *albedo* is not (H, W); exactly one of *normals* / *albedo* is given (the pair is meaningless apart — the model is albedo * n).
• specular_photometric family guide
• Sample data catalog (DL URL / license) — 2-D के लिए skimage.data (BSD/public) + synthetic, 3-D के लिए real data sources (Stanford/PDS आदि) के DL URL।
• Operators का स्रोत और संदर्भ — इस op family के मूल research/methods के स्रोत।
• Algorithm का मानक स्रोत (लेखक, वर्ष) और उपयोग ऊपर की family usage guide में दिए गए हैं।
• specular_photometric — py -3.11 examples/specular_photometric.py
image2d को input के रूप में लेते हैं)photometric)*Provenance: specularity.py — SPECULAR operator registry. यह per-op note tools/opdocs.py md से अपने-आप generate होता है (हाथ से edit न करें)।*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.