A Simple Delaunay triangulation and Voronoi diagram constructor in 2D. Written by Jose M. Espadero
It is written in pure python + numpy (tested with python2.7 and python3). A test example is provided showing how to call and plot the results using matplotlib.
It support the robust inCircle2D predicate from Jonathan Richard Shewchuk, but it is disabled by default due to perfomance penalties, so do not expect to work on degenerate set of points. If you really need to compute triangulation on big or degenerate set of points, try scipy.spatial.Delaunay instead.
Is it fast?
No. This code has been written to stay clear, easy to read by novices,
instead of highly-optimized. There is a section in
performs specially bad:
# Search the triangle(s) whose circumcircle contains p for T in self.triangles: if self.inCircle(T, p): bad_triangles.append(T)
Here, we should avoid iterating over the complete list of triangles. Best way is to use a structure that allows a spatial search (as a QuadTree). Then, continue the search over the neighbours of the initial search.
Despite that, it will compute DT of less than 1000 points in a reasonable time.
Again, just pretend to keep the code simple, didactic and with minimal dependencies.
Why did you write it?
Mainly, to provide a didactic implementation of the algorithm. You can use:
import numpy as np from delaunay2D import Delaunay2D # Create a random set of points seeds = np.random.random((10, 2)) # Create delaunay Triangulation dt = Delaunay2D() for s in seeds: dt.addPoint(s) # Dump triangles print (dt.exportTriangles())
as a minimal example of build a triangulation and dump the triangles.
Also, because sometimes it is not possible/worth to import the complete scipy.spatial package (for example, when running a script inside of blender python interpreter)