Metadata-Version: 2.4
Name: pyedt
Version: 0.1.5
Summary: Euclidian Distance Transform functions for GPU and parallel CPU
Home-page: https://pypi.org/project/pyedt/
Author: LTrace technologies
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM
License-File: LICENSE
Requires-Dist: matplotlib>=3.5.1
Requires-Dist: numba>=0.56.2
Requires-Dist: numpy>=1.23.1
Requires-Dist: scipy>=1.8.1
Requires-Dist: pytest>=7.4.1
Dynamic: author
Dynamic: classifier
Dynamic: description
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[![](https://badge.fury.io/py/pyedt.svg)](https://pypi.python.org/pypi/pyedt)

# pyedt
Python Euclidian distance transform with numba and cuda

![Benchmarks](doc/benchmarks.png)

Legend
 * cpu - PyEDT running on cpu only
 * gpu - PyEDT running with CUDA, sending the whole image to the GPU
 * gpu_split_n - PyEDT running with CUDA, the image is split in n^2 prisms and processed one at a time in the GPU
 
Benchmark executed in a Windows 10 machine, with 64 Gb of RAM, an AMD Ryzen 7 2700 CPU and a GeForce RTX 3090 Ti GPU

The methods used were developed based on modifications over the work published by Lotufo and Zampirolli[1]

[1]Lotufo, Roberto & Zampirolli, Francisco. (2001). Fast multidimensional parallel Euclidean distance transform based on mathematical morphology. 100 - 105. 10.1109/SIBGRAPI.2001.963043. 
