polarization_render — SPECULAR `polarization` op

• 数据种类:image2d × image2d → polsweep

• 调用:import specularity; specularity.polarization_render(diffuse, specular, angles_deg=(0.0, 45.0, 90.0, 135.0), azimuth_deg=0.0)(或 opsspecular.get("polarization_render"))

用法

偏振片旋转的正向模型:把已知的分离变成偏振相机会记录到的帧序列。→ (N, H, W)。

> 以下的详细说明为原文 —— 摘要与标题已翻译。

`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.

详细使用指南

• specular_photometric 族使用指南

参考(示例数据・文献)

• 示例数据目录(下载 URL / 许可证) —— 2-D 用 skimage.data(BSD/公有领域)加合成图,3-D 给出真实数据源(Stanford/PDS 等)的下载 URL。

• 算子来历与参考文献 —— 该算子族所依据的研究/方法出处。

• 算法的正典(作者・年份)与用途见上面的族使用指南。

可运行的示例(实际调用该算子并已验证的样例)

• specular_photometric — py -3.11 examples/specular_photometric.py

类型可衔接的下一个算子(可接受 `polsweep` 作为输入)

polarization_separate · polarization_dolp_map · polarization_stokes

同类别(`polarization`)

polarization_separate · polarization_dolp_map · polarization_stokes

*Provenance: specularity.py — SPECULAR 算子登记表。本条目由 tools/opdocs.py md 自动生成(请勿手工编辑)。*

polarization_render — SPECULAR polarization op

用法