CUDA implementation of Meijster's parallel algorithm for calculating the distance transform of a 2D image
Uses CUDA v9.1.
This started out as an attempt to implement "A Fast CUDA-based Implementation for the Euclidean Distance Transform" by Zampirolli and Filipe, as that is the top result when searching for CUDA and distance transforms.
I could not get their sample codes to run or produce anything, so I have modified the edt_cols kernel for a simpler procedure. The edt_rows kernel has been written based on equation (1) of Meijster's "A General Algorithm for Computing Distance Transforms In Linear Time".
The performance is very similar to what Zampirolli and Filipe claim: My NVIDIA GTX 1060 6GB is capable of calculating the distance transform of a 256x256 image in 3ms with this kernel. Zampirolli and Filipe claim their kernel completes, for the same image, in about 1.7ms with an NVIDIA Titan X.
- Easiest way is to duplicate the "0_Simple/template" Cuda sample project and just replace all the code with the single .cu file in this repository
- The code will generate .dat image files that are just packed 32-bit floats. For easy viewing, I recommend using ImageJ, and picking Import->Raw, choosing "32-bit Real" for the image type, 256x256 pixels and checking "Little-endian byte order", if that is the case for you
- I have not tried to optimize this extremely hard, as the performance is sufficient for me right now. I believe the
edt_colskernel can be further broken down, having multiple threads per column