delaunay

Setup Given some set of nodes within a convex hull, assume the domain contains one or more concave areas: where blue dots are points, and the black line illustrates the domain. Assume the points are held as a 2D array points of length n , where n is the number of point-pairs. Let us then triangulate the points, using something like the Delaunay method from scipy.spatial: As you can see, one may experience the creation of triangles crossing through the domain. Question What is a good algorithmic approach to removing any triangles that span outside of the domain? Ideally but not necessarily,

I'm trying to construct a Delaunay triangulation for the very specific case where the input x and y coordinates are orthogonal and relatively equidistant. Given the data size is relatively large (1000x1200 triangulation points) and that the Qhull algorithm doesn't know about my extra orthogonal condition, the triangulation is relatively slow (25 seconds on my machine). As such, I'd like to manually construct a Delaunay triangulation with each of my known quads subdivided into two triangles. I appreciate that this won't always result in a valid Delaunay triangulation (e.g. when the x and y step

Having a cloud point shaped like some sort of distorted paraboloid, I would like to use Delaunay Triangulation to interpolate the points. I have tried other techniques (f.ex. splines) but did not manage to enforce the desired behavior. I was wondering if there's a quick way to use the results of scipy.spatial.Delaunay in a way where I can give the (x,y) coords and get the z-coord of the point on the simplex (triangle). From the documentation looks like I can pull out the index of the simplex but I am not sure how to take it from there. You can give the Delaunay triangulation to scipy

I want to triangulate the complex (but not self-intersecting) polygon with holes, so that resulting triangles all lay inside the polygon, cover that polygon completely, and obey the Delaunay triangle rules. Obviously, I could just build the Delaunay triangulation for all points, but I fear that some edges of the polygon will not be included into resulting triangulation. So, is such triangulation possible? And if yes, how can I do it? Just in case - I need it to construct the approximation of polygon medial axis (I hope it can be done via connecting all circumference points of resulting

I have a pointlist=[p1,p2,p3...] where p1 = [x1,y1],p2=[x2,y2] ... I want to use scipy.spatial.Delaunay to do trianglation on these point clouds and then plot it How can i do this ? The documentation for the Delaunay is really scarce so far i have this code from subprocess import Popen, PIPE import os os.environ['point_num'] = "2000" cmd = 'rbox $point_num D2 | tail -n $point_num' sub_process = Popen(cmd, shell=True,stdout=PIPE,stderr=PIPE) output = sub_process.communicate() points = [line.split() for line in output[0].split('\n') if line] x = [p[0] for p in points if p] y = [p[1] for p in

I succesfully implemented a Delaunay triangulation of a contour in OpenCV 2.3.1. With cvPointPolygonTest I can get all the triangles in the convex hull, then I tried to perform another cvPointPolygonTest on triangles centroid to know if they are in the main contour or not , so I can have the constrained triangulation of the contour. But, it doesn't work well, as some triangles are (eg. with a walking man who has his two legs distant) 'over' a hole . Does anyone know a way to perform a constrained triangulation. I thought about convexityDefects, but can't manage to understand how to begin with

I need to find a largest inscribed circle of a convex polygon, I've searched many sites and I get that this can be done by using Delaunay triangulation. I found a thread in CGAL discussion with an algorithm using CGAL: You can compute this easily with CGAL: First, compute the Delaunay triangulation of the points. Then, iterate on all the finite faces of the triangulation. For each finite face f compute its circumcenter c locate c in the triangulation (to speed up things, you can give one vertex of f as starting hint for the point location) if the face returned by locate(c,hint) is finite, then