What should be most efficient way to implement normalizing vector data images (normals) using ImageBuffAlgo?
For this moment I only see the way to do this in four steps. ImageBuffAlgo::madd for [0.0,1.0] -> [-1.0,1.0] ImageBuffAlgo::pow for power of 2 ImageBuffAlgo::sum_channels for vector magnitude ImageBuffAlgo::div (sec and magnitude) to normalize vector length ImageBuffAlgo::madd for normalize to [0.0, 1.0] range. This not only required to make so many steps but also required a lot of temporary buffers, that for huge textures can required lot of memory. When all steps can be done per pixel and perfectly parallelized (shaders, cuda). Maybe I missed some OIIO functions? 🤔 Best regards: Vlad PS: btw, looks like Google completely filter out all Larry messages if Gmail used for subscription to this mailing list. They even not in spam folder, where mail list messages quite often can be moved 😒 _______________________________________________ Oiio-dev mailing list Oiio-dev@lists.openimageio.org http://lists.openimageio.org/listinfo.cgi/oiio-dev-openimageio.org
