Integrate ICC v5 / iccMAX Support into OpenCV

该 issue 没有指定文件、测试或入口点。首先确定 ICC v5/iccMAX 支持应属于这个 Python 打包工具链还是上游 OpenCV，然后制定一个范围明确的实现计划，涵盖所请求的配置文件处理、绑定和向后兼容性，再确定完成标准。

Dear OpenCV Maintainers,

I am writing to propose an enhancement to OpenCV’s color management capabilities: the integration of ICC v5 / iccMAX profile support. As imaging workflows increasingly rely on wide-gamut, high-dynamic-range content, modern color management standards are essential for accurate processing, visualization, and output.

Motivation
Current OpenCV workflows typically rely on ICC v2/v4 profiles and manual gamma approximations for wide-gamut color spaces (e.g., ProPhoto RGB, ROMM RGB) or HDR content. This approach has several limitations:

Limited channel support: Only 3-channel RGB is fully supported; multi-channel or spectral data cannot be handled.

Approximate gamma and clipping: Wide-gamut transforms require manual gamma adjustments, introducing artifacts and loss of fidelity.

No perceptual color appearance modeling: Viewing conditions, adaptation, and flare corrections are not considered.

Limited HDR support: Floating-point and extended dynamic range are only partially implemented, restricting true HDR workflows.

Proposed Upgrade
Integrate ICC v5 / iccMAX profile handling directly into OpenCV, leveraging:

Multi-channel LUTs and transforms: Accurate conversion between wide-gamut color spaces and high-bit-depth or spectral data.

High-precision floating-point processing: Maintain HDR, linear-light, and extended dynamic range data throughout pipelines.

Color appearance models (CAM02 / CAM16): Support perceptual transforms for soft-proofing and viewing condition adaptation.

Backward compatibility: Ensure existing workflows using v2/v4 profiles remain functional.

Benefits

Professional color accuracy: OpenCV would handle modern cinema cameras (RED, Arri, Blackmagic) and high-end photography workflows accurately.

HDR and wide-gamut support: True representation of HDR content without manual approximations.

Scientific and industrial applications: Enhanced precision for research, medical imaging, and machine vision systems.

Enhanced Python and C++ pipelines: Enable real-time processing of color-managed content for computer vision, machine learning, and display.

Improved dataset quality: Training datasets generated in OpenCV would more accurately reflect real-world color and lighting, improving model generalization.

Suggested Implementation

Integrate an ICC v5 parser and transform engine (e.g., leverage DemoIccMax / iccDEV).

Provide Python bindings for profile application in cv2.cvtColor() or a new cv2.colorProfileTransform() function.

Maintain internal floating-point representation for linear color processing.

Add support for perceptual adaptation and multi-channel LUTs.

Adding ICC v5 / iccMAX support would bring OpenCV to parity with professional imaging software and ensure Python, C++, and GPU-based pipelines can process true wide-gamut HDR content accurately. This upgrade would significantly benefit research, industrial, and machine vision applications, positioning OpenCV as a robust choice for modern imaging workflows.
Integrating the iccMAX ICC file by default

Thank you for considering this enhancement.
https://www.color.org/iccmax.xalter