def generate_transform_matrices(mesh, factors):
factors = map(lambda x: 1.0 / x, factors)
M, A, D, U, E = [], [], [], [], []
A.append(get_vert_connectivity(mesh))
M.append(mesh)
E.append(get_vertices_per_edge(mesh))
for factor in factors:
ds_f, ds_D = qslim_decimator_transformer(M[-1], factor=factor)
D.append(ds_D)
new_mesh_v = ds_D.dot(M[-1].v)
new_mesh = Mesh(v=new_mesh_v, f=ds_f)
M.append(new_mesh)
A.append(get_vert_connectivity(new_mesh))
U.append(setup_deformation_transfer(M[-1], M[-2]))
E.append(get_vertices_per_edge(new_mesh))
return M, A, D, U, E
def test_connectivity_smoke_test(self):
from psbody.mesh import Mesh
from psbody.mesh.topology.connectivity import get_vert_connectivity, get_faces_per_edge
m = Mesh(
filename=os.path.join(test_data_folder, 'female_template.ply'))
vconn = get_vert_connectivity(m)
fpe = get_faces_per_edge(m)
self.assertIsNotNone(vconn)
self.assertIsNotNone(fpe)
def laplacian(part_mesh):
""" Compute laplacian operator on part_mesh. This can be cached.
"""
import scipy.sparse as sp
from sklearn.preprocessing import normalize
from psbody.mesh.topology.connectivity import get_vert_connectivity
import numpy as np
connectivity = get_vert_connectivity(part_mesh)
# connectivity is a sparse matrix, and np.clip can not applied directly on
# a sparse matrix.
connectivity.data = np.clip(connectivity.data, 0, 1)
lap = normalize(connectivity, norm='l1', axis=1)
lap = sp.eye(connectivity.shape[0]) - lap
return lap
def numpy_laplacian_uniform(v, f):
"""Computes uniform laplacian operator on mesh."""
import scipy.sparse as sp
from sklearn.preprocessing import normalize
from psbody.mesh.topology.connectivity import get_vert_connectivity
connectivity = get_vert_connectivity(Mesh(v=v, f=f))
# connectivity is a sparse matrix, and np.clip can not applied directly on
# a sparse matrix.
connectivity.data = np.clip(connectivity.data, 0, 1)
lap = normalize(connectivity, norm='l1', axis=1)
lap = lap - sp.eye(connectivity.shape[0])
return lap
def calc_laplacian(mesh):
"""
メッシュに対して Laplacian deformation での Laplacian を計算する
"""
# pytorch3d -> psbody.mesh への変換
mesh = Mesh(mesh.verts_packed().detach().cpu().numpy(),
mesh.faces_packed().detach().cpu().numpy())
# メッシュを頂点連結関係を取得？
connectivity = get_vert_connectivity(mesh)
# connectivity is a sparse matrix, and np.clip can not applied directly on a sparse matrix.
connectivity.data = np.clip(connectivity.data, 0, 1)
laplacian = normalize(connectivity, norm='l1', axis=1)
laplacian = sp.eye(connectivity.shape[0]) - laplacian
return laplacian
