voronoi

问题 I would like to know how to create weighted Voronoi diagram . (For example, additively weighted Voronoi diagram and multiplicatively weighted Voronoi diagram.) In my recognition, a class of scipy.spatial.voronoi is just a euclidean distance Voronoi diagram. Can I create weighted Voronoi diagram using this class? Or there are no ways but to make codes by originally? 回答1: You could try my php implementation:https://tetramatrix.github.io/awvd/. 来源： https://stackoverflow.com/questions/51983360

问题 Would anyone with Python experience be able to take a look at this for me? I'm using this code: https://bitbucket.org/mozman/geoalg/src/5bbd46fa2270/geoalg/voronoi.py to perform voronoi tesselation on a group of points. It works, but the problem is that the code only provides a list of all the vertices used to create the polygons, and which pairs must be joined together. It doesn't provide any information as to what points are used to make up each polygon, which I need. Thanks. 回答1: context

问题 I find that in the boost library, for the voronoi diagram, some edge data is infinite. According to the instruction, it has to be clipped. But I cant find how to do it. Could anybody can give me a example code, please? Thx 回答1: This is a rather stale question, but I found it when trying to solve the exact same problem. It's only fair to share the solution I hit upon so the next poor sap doesn't have to figure it out themselves. I have no idea if this is a particularly good way to do things,

问题 I would like to compute the mean and standard deviation of the areas of a set of Voronoi regions in 2D (if the region extends to infinity, I'll just clip it to the unit square). However if possible I would like to do this computation from the Delaunay Triangulation without explicitly computing the Voronoi regions? Is this even possible, or is it better to just compute the Voronoi diagram explicitly? 回答1: In order to calculate the voronoi region of a vertex you need to iterate the 1-ring

问题 I will be working with a set of thousands of points. I can implement or use existing implementations of Fortunes Algorithm to produce the Voronoi diagram of the points, but my application also requires me to know adjacency with respect to each Voronoi Cell. More specifically, for any Voronoi cell I need to know the cells that are adjacent to this. At this point I'm not to concerned with output or storage method as I can likely massage an implementation to work in my favor. Is anyone aware of

问题 I am computing a voronoi diagram from a set of points as follows: from scipy.spatial import Voronoi import numpy as np np.random.seed(0) points = np.random.uniform(-0.5, 0.5, (100, 2)) // Compute Voronoi v = Voronoi(points) voronoi_plot_2d(v) plt.show() This creates an image as follows: As one can see, this is creating vertices which are going to infinity (dashed lines) and also beyond the original bounding box for the points which is: bbox = np.array([[-0.5, -0.5], [0.5, -0.5], [0.5, 0.5], [

问题 i want to calculate the "density" of a 3d point cloud obtained by stereo vision. I implemented the 3D Voronoi diagram like in https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.SphericalVoronoi.html Result is raise ValueError("Radius inconsistent with generators.") for many different magnitudes (i tried a lot) Example of my pointcloud is: [[ 0.63492548 0.10921954 0.12711886] [ 0.14530358 0.02687934 -0.0357723 ] [ 0.16594444 0.02741969 0.04187516] [ 0.69606036 0.06983382 -0

问题 I am using the Voronoi diagram-based approach outlined here to find midlines of binary masks of root images. I am using the Python code more or less exactly as described: import skimage.morphology as morphology WHITE = 255 image_bool = binary_mask == WHITE d = morphology.disk(2) img = morphology.binary_closing(image_bool, selem=d) skeleton = morphology.medial_axis(img) Then comes the graphing: I feed the skeletonized image into buildTree, as described in user Gabriel's iPython notebook: https

问题 Following this two resources: Boost basic tutorial SO Question I wrote a Delaunay triangulation with boost . It works fine if the points coordinates are integral (I generated several random tests and I did not observed error). However if the points are non integral I found many incorrect triangulations with missing edges or wrong edges. For example this image has been build with rounded value and is correct (see code below) But this image as been build with raw values and is incorrect (see

问题 Code bellow assumes input is points, not line segments (which is wrong). Following this 2D Voronoi Diagram Adaptor example, I am trying to write a program which takes as input line segments and prints the vertices of the faces of the Voronoi diagram. Here is my attempt (keeping includes/typedefs of the example): // standard includes #include <iostream> #include <fstream> #include <cassert> // includes for defining the Voronoi diagram adaptor #include <CGAL/Exact_predicates_inexact