Developer's guide

Understanding color and color management

Some useful ressources to jump into digital color management, editing pipeline, calibrations, view transform, etc. :

• https://www.visualeffectssociety.com/sites/default/files/files/cinematic_color_ves.pdf
• https://acescentral.com/
• http://last.hit.bme.hu/download/firtha/video/Colorimetry/Fairchild_M._Color_appearance_models__2005.pdf

Writing efficient code

Pixels are essentially 4D RGBA vectors. Since 2004, processors have got special abilities to process vectors and apply Single Instructions on Multiple Data (SIMD). This allows to speed-up the computations by processing 1 pixel (SSE2) to 4 pixels (AVX-512) at the same time, saving a lot of CPU cycles.

darktable has 3 version of its IOPs : pure C (scalar), SSE2 (vectorized for 4 floats) and OpenCL (vectorized on GPU). That triggers some redundancy in the code. However, modern compilers and the OpenMP library have auto-vectorization options that could optimize pure C, provided the code is written in a vectorizable way and uses some pragmas to give hints to the compiler.

Write vectorizable code : https://info.ornl.gov/sites/publications/files/Pub69214.pdf

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Modules

Modules are the interfaces for IOPs, i.e. image-processing filters stacked in the pixelpipe. IOPs can be found in src/iop and the IOP API can be found in the header src/iop/iop_api.h.

Most IOP have 3 variant of their pixel-filtering part:

1. a pure C implementation, in process()
2. a C optimized version, with SSE2 intrinsics, in process_sse2()
3. an OpenCL version, offloading the computation to the GPU, in process_opencl().

An example of a dummy IOP can be found in src/iop/useless.c and used as a boilerplate.

If you add a new IOP, be sure to add the C file in src/iop/CMakeLists.txt#L69 and deal with its priority in the pixelpipe by adding a new node in tools/iop_dependencies.py

Libs