I have points (e.g., lat, lon pairs of cell tower locations) and I need to get the polygon of the Voronoi cells they form.
from scipy.spatial import Voronoi
tower = [[ 24.686 , 46.7081],
[ 24.686 , 46.7081],
[ 24.686 , 46.7081]]
c = Voronoi(towers)
Now, I need to get the polygon boundaries in lat,lon coordinates for each cell (and what was the centroid this polygon surrounds). I need this Voronoi to be bounded as well. Meaning that the boundaries don't go to infinity, but rather within a bounding box.
Given a rectangular bounding box, my first idea was to define a kind of intersection operation between this bounding box and the Voronoï diagram produce by scipy.spatial.Voronoi. An idea not necessarily great, since this requires to code a large number of basic functions of computational geometry.
However, here is the second idea (hack?) that came to my mind: the algorithms to compute the Voronoï diagram of a set of n points in the plane have a time complexity of O(n ln(n)). What about adding points to constraint the Voronoï cells of the initial points to lie in the bounding box?
Solution for a bounded Voronoï diagram
A picture is worth a great speech:
What I did here? That's pretty simple! The initial points (in blue) lie in [0.0, 1.0] x [0.0, 1.0]. Then I get the points (in blue) on the left (i.e. [-1.0, 0.0] x [0.0, 1.0]) by a reflection symmetry according to x = 0.0 (left edge of the bounding box). With reflection symmetries according to x = 1.0, y = 0.0 and y = 1.0 (other edges of the bounding box), I get all the points (in blue) I need to do the job.
Then I run scipy.spatial.Voronoi. The previous image depicts the resulting Voronoï diagram (I use scipy.spatial.voronoi_plot_2d).
What to do next? Just filter points, edges or faces according to the bounding box. And get the centroid of each face according to the well-known formula to compute centroid of polygon. Here is an image of the result (centroids are in red):
Some fun before showing code
Great! It seems to work. What if after one iteration I try to re-run the algorithm on the centroids (in red) rather than the initial points (in blue)? What if I try it again and again?
Step 2
Step 10
Step 25
Cool! Voronoï cells tend to minimize their energy...
Here is the code
import matplotlib.pyplot as pl
import numpy as np
import scipy as sp
import scipy.spatial
import sys
eps = sys.float_info.epsilon
n_towers = 100
towers = np.random.rand(n_towers, 2)
bounding_box = np.array([0., 1., 0., 1.]) # [x_min, x_max, y_min, y_max]
def in_box(towers, bounding_box):
return np.logical_and(np.logical_and(bounding_box[0] <= towers[:, 0],
towers[:, 0] <= bounding_box[1]),
np.logical_and(bounding_box[2] <= towers[:, 1],
towers[:, 1] <= bounding_box[3]))
def voronoi(towers, bounding_box):
# Select towers inside the bounding box
i = in_box(towers, bounding_box)
# Mirror points
points_center = towers[i, :]
points_left = np.copy(points_center)
points_left[:, 0] = bounding_box[0] - (points_left[:, 0] - bounding_box[0])
points_right = np.copy(points_center)
points_right[:, 0] = bounding_box[1] + (bounding_box[1] - points_right[:, 0])
points_down = np.copy(points_center)
points_down[:, 1] = bounding_box[2] - (points_down[:, 1] - bounding_box[2])
points_up = np.copy(points_center)
points_up[:, 1] = bounding_box[3] + (bounding_box[3] - points_up[:, 1])
points = np.append(points_center,
np.append(np.append(points_left,
points_right,
axis=0),
np.append(points_down,
points_up,
axis=0),
axis=0),
axis=0)
# Compute Voronoi
vor = sp.spatial.Voronoi(points)
# Filter regions
regions = []
for region in vor.regions:
flag = True
for index in region:
if index == -1:
flag = False
break
else:
x = vor.vertices[index, 0]
y = vor.vertices[index, 1]
if not(bounding_box[0] - eps <= x and x <= bounding_box[1] + eps and
bounding_box[2] - eps <= y and y <= bounding_box[3] + eps):
flag = False
break
if region != [] and flag:
regions.append(region)
vor.filtered_points = points_center
vor.filtered_regions = regions
return vor
def centroid_region(vertices):
# Polygon's signed area
A = 0
# Centroid's x
C_x = 0
# Centroid's y
C_y = 0
for i in range(0, len(vertices) - 1):
s = (vertices[i, 0] * vertices[i + 1, 1] - vertices[i + 1, 0] * vertices[i, 1])
A = A + s
C_x = C_x + (vertices[i, 0] + vertices[i + 1, 0]) * s
C_y = C_y + (vertices[i, 1] + vertices[i + 1, 1]) * s
A = 0.5 * A
C_x = (1.0 / (6.0 * A)) * C_x
C_y = (1.0 / (6.0 * A)) * C_y
return np.array([[C_x, C_y]])
vor = voronoi(towers, bounding_box)
fig = pl.figure()
ax = fig.gca()
# Plot initial points
ax.plot(vor.filtered_points[:, 0], vor.filtered_points[:, 1], 'b.')
# Plot ridges points
for region in vor.filtered_regions:
vertices = vor.vertices[region, :]
ax.plot(vertices[:, 0], vertices[:, 1], 'go')
# Plot ridges
for region in vor.filtered_regions:
vertices = vor.vertices[region + [region[0]], :]
ax.plot(vertices[:, 0], vertices[:, 1], 'k-')
# Compute and plot centroids
centroids = []
for region in vor.filtered_regions:
vertices = vor.vertices[region + [region[0]], :]
centroid = centroid_region(vertices)
centroids.append(list(centroid[0, :]))
ax.plot(centroid[:, 0], centroid[:, 1], 'r.')
ax.set_xlim([-0.1, 1.1])
ax.set_ylim([-0.1, 1.1])
pl.savefig("bounded_voronoi.png")
sp.spatial.voronoi_plot_2d(vor)
pl.savefig("voronoi.png")
来源：https://stackoverflow.com/questions/28665491/getting-a-bounded-polygon-coordinates-from-voronoi-cells