def updatecolor():
global c0, c1, c2, showpoints
if colorflag:
showpoints = np.vstack([ptcloud[i][::1], pred[i]])
value = distmap[i] / distmap[i].max()
rgb = np.zeros((len(value), 3), dtype='float32')
rgb[:, 2] = (value * 2).clip(0, 1) * 255
rgb[:, 1] = (2 - value * 2).clip(0, 1) * 255
c0 = np.hstack([rgb[:, 1][::1], np.zeros(len(pred[i]))])
c1 = np.hstack([rgb[:, 2][::1], np.zeros(len(pred[i]))])
c2 = np.hstack([rgb[:, 0][::1], np.ones(len(pred[i]))])
else:
showpoints = np.vstack([pred[i]])
a = pred[i]
b = ptcloud[i]
hausdorff_a_to_b = pcu.hausdorff(a, b)
hausdorff_b_to_a = pcu.hausdorff(b, a)
hausdorff_dist = max(hausdorff_a_to_b, hausdorff_b_to_a)
print("hausdorff_dist", hausdorff_dist)
M = pcu.pairwise_distances(a, b)
w_a = np.ones(a.shape[0], dtype='float32')
w_b = np.ones(b.shape[0], dtype='float32')
P = pcu.sinkhorn(w_a, w_b, M, eps=1e-3)
sinkhorn_dist = (M * P).sum()
print("sinkhorn_dist", sinkhorn_dist)
'''
def test_sinkhorn(self):
import point_cloud_utils as pcu
import numpy as np
# a and b are arrays where each row contains a point
# Note that the point sets can have different sizes (e.g [100, 3], [111, 3])
a = np.random.rand(100, 3)
b = np.random.rand(100, 3)
# M is a 100x100 array where each entry (i, j) is the squared distance between point a[i, :] and b[j, :]
M = pcu.pairwise_distances(a, b)
# w_a and w_b are masses assigned to each point. In this case each point is weighted equally.
w_a = np.ones(a.shape[0])
w_b = np.ones(b.shape[0])
# P is the transport matrix between a and b, eps is a regularization parameter, smaller epsilons lead to
# better approximation of the true Wasserstein distance at the expense of slower convergence
P = pcu.sinkhorn(w_a, w_b, M, eps=1e-3)
# To get the distance as a number just compute the frobenius inner product <M, P>
sinkhorn_dist = (M * P).sum()
