lorenzomammana / morphological-operators-cupy Goto Github PK

Implementation of the van Herk/Gil-Werman algorithm for fast morphological dilation/erosion exploiting shared memory for maximum GPU parallelization.

For even sized array i've followed the same technique used by the scipy algorithm, in particular the window for the erosion is such the center elemnent is in position (p / 2) + 1.

The repository contains a Python wrapper around the CUDA code based on cupy RawKernels.

The current implementation is generally 5-10x time faster than the one contained in Scipy when using an Nvidia GTX 1060 against an overclocked Intel i5-6600k.

In particular this implementation is 50x faster when working with the top-hat filter, even more when the structuring element is very large. Don't know why but the Scipy implementation of the white and black top hat is terribly slow.

As i'm new to CUDA develop i'm also quite sure this can go even faster!

This repo is heavily inspired by "Parallel van Herk/Gil-Werman image morphology on GPUs using CUDA" Luke Domanski, Pascal Vallotton, Dadong Wang (2016) and of course "A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels" Van Herk, M. (1992).