code: def medial_axis(polygon,
                  resolution=None,
                  clip=None):
      
      from scipy.spatial import Voronoi
  
      if resolution is None:
          resolution = .01
  
      # get evenly spaced points on the polygons boundaries
      samples = resample_boundaries(polygon=polygon,
                                    resolution=resolution,
                                    clip=clip)
      # stack the boundary into a (m,2) float array
      samples = stack_boundaries(samples)
      # create the voronoi diagram on 2D points
      voronoi = Voronoi(samples)
      # which voronoi vertices are contained inside the polygon
      contains = vectorized.contains(polygon, *voronoi.vertices.T)
      # ridge vertices of -1 are outside, make sure they are False
      contains = np.append(contains, False)
      # make sure ridge vertices is numpy array
      ridge = np.asanyarray(voronoi.ridge_vertices, dtype=np.int64)
      # only take ridges where every vertex is contained
      edges = ridge[contains[ridge].all(axis=1)]
  
      return edges, voronoi.vertices
docs: Given a shapely polygon, find the approximate medial axis
      using a voronoi diagram of evenly spaced points on the
      boundary of the polygon.
  
      Parameters
      ----------
      polygon : shapely.geometry.Polygon
        The source geometry
      resolution : float
        Distance between each sample on the polygon boundary
      clip : None, or (2,) int
        Clip sample count to min of clip[0] and max of clip[1]
  
      Returns
      ----------
      edges : (n, 2) int
        Vertex indices representing line segments
        on the polygon's medial axis
      vertices : (m, 2) float
        Vertex positions in space