photometric op• 資料種類:images → image2d
• 呼叫:import specularity; specularity.photometric_residual(images, lights, normals=None, albedo=None, normalize=True)(或 opsspecular.get("photometric_residual"))
Lambert 模型逐像素的失配程度。→ (H, W) 的 RMS 殘差。
> 以下的詳細說明為原文 —— 摘要與標題已翻譯。
`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
:func: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
:func: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
:func: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
:func: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`).
• 範例資料目錄(下載 URL / 授權) —— 2-D 用 skimage.data(BSD/公有領域)加合成圖,3-D 給出真實資料源(Stanford/PDS 等)的下載 URL。
• 運算子來歷與參考文獻 —— 該運算子族所依據的研究/方法出處。
• 演算法的正典(作者・年份)與用途見上面的族使用指南。
• specular_photometric — py -3.11 examples/specular_photometric.py
image2d 作為輸入)photometric)*Provenance: specularity.py — SPECULAR 運算子登記表。本條目由 tools/opdocs.py md 自動產生(請勿手動編輯)。*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.