def simpleTest():
width = 50
height = 100
#points = np.array([
# [0.2, 0.3],
# [0.7, 0.5],
# [0.6,0.2],
# [0.3, 0.9],
# [0.8, 0.1]
#])
points = sampleRandomPoints(100, width, height) #np.random.rand(20, 2)
print("Points:")
print(points)
cells = bounded_voronoi(points, [0, width, 0, height])
for i, cell in enumerate(cells):
print("cell", i, "with point", cell.generator())
print(cell.corner())
# example density map
density = np.zeros((width, height))
for x in range(width):
for y in range(height):
density[x, y] = 1 - math.sqrt((x / width - 0.5)**2 +
(y / height - 0.5)**2)
# compute cell integrals
cell_densities = [cell.integrate(density) for cell in cells]
# plotting
plt.figure(figsize=(20, 10))
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(density, cmap=cm.hot)
ax2 = plt.subplot(1, 2, 2)
for cell, (d, _) in zip(cells, cell_densities):
vertices = np.array(list(cell.corner()) + [cell.corner()[0]])
ax2.fill(vertices[:, 0], vertices[:, 1], facecolor=cm.hot(d))
ax2.plot([cell.generator()[0] for cell in cells],
[cell.generator()[1] for cell in cells], 'b.')
ax2.plot([d[1][0] for d in cell_densities],
[d[1][1] for d in cell_densities], 'r.')
for cell in cells:
ax2.plot(cell.corner()[:, 0], cell.corner()[:, 1], 'go')
for cell in cells:
vertices = np.array(list(cell.corner()) + [cell.corner()[0]])
ax2.plot(vertices[:, 0], vertices[:, 1], 'k-')
#fig.show()
plt.savefig("bounded_voronoi.png")
def redo_voronoi(self):
bound = self.boundary()
points = map(lambda x: (x.x, x.y), self.stones)
self.voronoi = bounded_voronoi(bound, points)
def init_voronoi(self):
bound = self.boundary()
points = []
self.voronoi = bounded_voronoi(bound, points)
def simpleOptim():
N = 200
width = 100
height = 100
iterations = 10
# create density
density = np.zeros((width, height))
for x in range(width):
for y in range(height):
density[x,
y] = (1 -
math.sqrt(2) * math.sqrt((x / width - 0.5)**2 +
(y / height - 0.5)**2))**2
# create algorithms
algs = [WeightedLloyd(), WeightedLindeBuzoGray()]
algNames = ["Lloyd", "Linde-Buzo-Gray"]
init_points = sampleRandomPoints(N, width, height)
for alg in algs:
alg.init(density, N, init_points)
plt.figure(figsize=(5, 5))
ax1 = plt.subplot(1, 1, 1)
ax1.imshow(density, cmap=cm.hot)
plt.savefig("stipplingDensity.png")
plt.close()
# create animation
def plot(cellsx, next_pointsx, algNames, filename):
plt.figure(figsize=(10 * len(algs), 10))
for i in range(len(algs)):
ax2 = plt.subplot(1, len(algs), i + 1)
ax2.title.set_text(algNames[i])
ax2.set_xlim(0, width)
ax2.set_ylim(0, height)
cells = cellsx[i]
ax2.plot([cell.generator()[0] for cell in cells],
[cell.generator()[1] for cell in cells], 'b.')
ax2.plot(next_pointsx[i][:, 0], next_pointsx[i][:, 1], 'r.')
for cell in cells:
vertices = np.array(list(cell.corner()) + [cell.corner()[0]])
ax2.plot(vertices[:, 0], vertices[:, 1], 'k-')
plt.savefig(filename)
plt.close()
#plot(algs, algNames, "stippling%02d.png"%0)
for i in range(iterations):
cellsx = []
next_pointsx = []
for alg, algName in zip(algs, algNames):
points = alg.points()
cells = bounded_voronoi(points, [0, width, 0, height])
converged = alg.step()
next_points = alg.points()
cellsx.append(cells)
next_pointsx.append(next_points)
print(algName, "converged:", converged)
plot(cellsx, next_pointsx, algNames, "stippling%02d.png" % i)