def make_case(name, nb_diracs, dim):
# default domain
if name == "random":
positions = np.random.rand(nb_diracs, dim)
elif name == "grid":
positions = make_grid(nb_diracs, dim)
elif name == "grid_with_rand":
positions = make_grid(nb_diracs, dim, rand_val=1)
elif name == "faces":
# voronoi with 100 points
pd = PowerDiagram(np.random.rand(5, dim))
# quantization
lot = OptimalTransport(positions=make_grid(nb_diracs, dim))
lot.obj_max_dw = 1e-5
lot.verbosity = 1
for ratio in [1 - 0.85**n for n in range(50)]:
# density
img_size = 1000
img_points = []
items = [range(img_size) for i in range(dim)]
for i in itertools.product(*items):
img_points.append(i)
img = pd.distances_from_boundaries(
np.array(img_points) / img_size).reshape((img_size, img_size))
img = (1 - ratio) + ratio * np.exp(-(100 * img)**2)
lot.set_domain(ScaledImage([0, 0], [1, 1], img / np.mean(img)))
# opt
for _ in range(10):
lot.adjust_weights()
B = lot.get_centroids()
lot.set_positions(lot.get_positions() + 0.3 *
(B - lot.get_positions()))
positions = lot.get_positions()
plt.plot(positions[:, 0], positions[:, 1], ".")
plt.show()
np.save("/data/{}_n{}_d{}_voro.npy".format(name, nb_diracs, dim),
(positions[:, 0], positions[:, 1]))
# solve
if nb_diracs < 32000000:
ot = OptimalTransport(positions)
# ot.verbosity = 1
# solve
ot.adjust_weights()
# display
# ot.display_vtk( "results/pd.vtk" )
np.save("/data/{}_n{}_d{}.npy".format(name, nb_diracs, dim),
(positions[:, 0], positions[:, 1], ot.get_weights()))
from pysdot.domain_types import ConvexPolyhedraAssembly
from pysdot import OptimalTransport
import numpy as np
positions = np.random.rand(200, 2)
# diracs
ot = OptimalTransport()
ot.set_positions(np.array(positions))
ot.set_weights(np.ones(ot.get_positions().shape[0]))
ot.verbosity = 1
# solve
ot.adjust_weights()
# display
ot.display_vtk("results/pd.vtk")
# print( ot.pd.display_html() )
dws = 5
img = imageio.imread("clay.jpg")
img = img[:, :, 1]
beg = int(img.shape[1] / 2 - img.shape[0] / 2)
end = beg + img.shape[0]
img = img[:, beg:end]
img = downscale_local_mean(img, (dws, dws))
img = np.max(img) * 1.05 - img
img /= np.sum(img)
# plt.imshow( img )
# plt.show()
# domain
domain = ConvexPolyhedraAssembly()
domain.add_img([0, 0], [1, 1], img)
# diracs
nd = 100000
ot = OptimalTransport(domain)
ot.set_positions(np.random.rand(nd, 2))
ot.set_weights(np.ones(nd) / nd)
ot.obj_max_dw = 1e-5
ot.verbosity = True
# solve
ot.adjust_weights(relax=1.0)
# display
ot.pd.display_vtk("results/pd.vtk", centroids=True)