Author: Dr Jan Schindler ([email protected])
Install development packages in Ubuntu:
sudo apt install g++ cmake libgmp10 libgmp-dev libmpfr6 libmpfrc++-dev libmpfi-dev
Create Python environment:
conda create --name nnpycgal -c conda-forge python=3.8 numpy cgal -c conda-forge
Activate the environment:
conda activate nnpycgal
Build and install the Python C++ extension:
python setup.py install
import datetime
from nnpycgal.nninterpol import nninterpol
import numpy as np
step = 1
nx, ny = 1000, 1000
nsamples = 100
xo = np.arange(0, nx, step)
yo = np.arange(0, ny, step)
xx, yy = np.meshgrid(xo, yo)
x = np.random.randint(0, nx, nsamples)
y = np.random.randint(0, ny, nsamples)
z = np.random.random(nsamples)
result = np.array(nninterpol(x, y, z, nx, ny))
This implementation also works well for surface model generation from LiDAR data (~4.5 million points):
Natural Neighbor interpolation in CGAL can be extremely fast compared to other libraries such as PyNGL's natgrid method [https://www.pyngl.ucar.edu/Functions/Ngl.natgrid.shtml]. This is achieved in this C++ extension with an efficient look-up table and multiprocessing with OpenMP.
