def get_rho(orig_v, vertices, faces):
orig_v = normalize_area(orig_v, faces)
a1 = igl.massmatrix(orig_v, faces,
igl.MASSMATRIX_TYPE_BARYCENTRIC).diagonal()
a2 = igl.massmatrix(vertices, faces,
igl.MASSMATRIX_TYPE_BARYCENTRIC).diagonal()
return np.divide(a1, a2)
def test_massmatrix(self):
a = igl.massmatrix(self.v, self.f)
b = igl.massmatrix(self.v, self.f, type=igl.MASSMATRIX_TYPE_BARYCENTRIC)
self.assertTrue(a.shape == (self.v.shape[0], self.v.shape[0]))
self.assertTrue(b.shape == (self.v.shape[0], self.v.shape[0]))
self.assertTrue(b.dtype == np.float64)
self.assertTrue(a.dtype == np.float64)
self.assertTrue(type(a) == type(b) == csc.csc_matrix)
def makeLaplacianMatrixSolverIGLHard(VPos, ITris, anchorsIdx):
VPosE = igl.eigen.MatrixXd(VPos)
ITrisE = igl.eigen.MatrixXi(ITris)
L = igl.eigen.SparseMatrixd()
M = igl.eigen.SparseMatrixd()
M_inv = igl.eigen.SparseMatrixd()
igl.cotmatrix(VPosE,ITrisE,L)
igl.massmatrix(VPosE,ITrisE,igl.MASSMATRIX_TYPE_VORONOI,M)
igl.invert_diag(M,M_inv)
L = M_inv*L
deltaCoords = L*VPosE
deltaCoords = np.array(deltaCoords)
#Bi-laplacian
Q = L.transpose()*L
def key_pressed(viewer, key, modifier):
global V
global U
global F
global L
if key == ord('r') or key == ord('R'):
U = V;
elif key == ord(' '):
# Recompute just mass matrix on each step
M = igl.eigen.SparseMatrixd()
igl.massmatrix(U,F,igl.MASSMATRIX_TYPE_BARYCENTRIC,M);
# Solve (M-delta*L) U = M*U
S = (M - 0.001*L)
solver = igl.eigen.SimplicialLLTsparse(S)
U = solver.solve(M*U)
# Compute centroid and subtract (also important for numerics)
dblA = igl.eigen.MatrixXd()
igl.doublearea(U,F,dblA)
print(dblA.sum())
area = 0.5*dblA.sum()
BC = igl.eigen.MatrixXd()
igl.barycenter(U,F,BC)
centroid = igl.eigen.MatrixXd([[0.0,0.0,0.0]])
for i in range(0,BC.rows()):
centroid += 0.5*dblA[i,0]/area*BC.row(i)
U -= centroid.replicate(U.rows(),1)
# Normalize to unit surface area (important for numerics)
U = U / math.sqrt(area)
else:
return False
# Send new positions, update normals, recenter
viewer.data.set_vertices(U)
viewer.data.compute_normals()
viewer.core.align_camera_center(U,F)
return True
def conformal_flow(vertices, faces):
vertices = normalize(np.copy(vertices))
L = igl.cotmatrix(vertices, faces)
itrs = 20
time_step = .1
for i in range(itrs):
vertices = normalize(vertices)
# this is a lumped mass matrix
M = igl.massmatrix(vertices, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
S = (M - time_step * L)
b = M.dot(vertices)
vertices = spsolve(S, b)
vertices = normalize(vertices)
print("flow error: " + str(get_error(vertices)))
vertices = project_sphere(vertices)
print("percentage of flipped faces: " +
str(100 * get_flipped_normals(vertices, faces)))
return vertices
def test_harmonic(self):
l = igl.cotmatrix(self.v1, self.f1)
m = igl.massmatrix(self.v1, self.f1, igl.MASSMATRIX_TYPE_VORONOI)
b = np.array([1, 2, 10, 7])
bc = self.v1[b, :]
k = 1
w = igl.harmonic_weights_from_laplacian_and_mass(l, m, b, bc, k)
self.assertTrue(w.flags.c_contiguous)
def get_mass_matrix(v, f, area_type):
if (area_type == 'voronoi') or (area_type == 'barycentric'):
return igl.massmatrix(v, f, AREA_TYPES[area_type])
elif area_type == 'mayer':
return mayer_area(v, f)
else:
raise ValueError("Please choose one of the supported mass"
+f" matrix types:{list(AREA_TYPES.keys())}")
def smooth_data(v, f, w):
# Smoothing = Minimize curvature
#
# min_{p*}{E(p*)}
#
# E(p*) = sum_{p in points on mesh}{A_p * ((Lp*)^2 + w(p* - p)^2)},
# where A_p is the voronoi region around point p
#
# Solve dE(p*)/dp* = 0
# => (L'ML + wM)p* = (wM)p ~= Ax = b
#
# L = M^(-1)L_w, w = cotangent[c]/uniform[u]/...
#
# L_c defined in slides 5 page 66
# M and L defined in slides 6 page 23
# Constructing the squared Laplacian and squared Hessian energy
# NOTE L_c is sparse
# L_c = igl.cotmatrix(v, f) # discrete laplacian
# L_c.data[np.isnan(L_c.data)] = 0
# L_c.data[L_c.data > 1e7] = 1e7
# L_c.data[L_c.data < -1e7] = -1e7
# Uniform laplacian
# adopted from here:
# https://libigl.github.io/libigl-python-bindings/igl_docs/#cotmatrix
adj = igl.adjacency_matrix(f)
# Sum each row = number of neighbours
adj_sum = np.squeeze(np.asarray(np.sum(adj, axis=1)))
# Convert row sums into diagonal of sparse matrix
adj_diag = sp.sparse.diags(adj_sum)
# Build uniform laplacian
L_u = adj - adj_diag
# NOTE M is sparse
M = igl.massmatrix(v, f, igl.MASSMATRIX_TYPE_VORONOI)
# Give corrupted triangles only very little weight
mask = np.logical_or(np.isnan(M.data), M.data == 0)
M.data[mask] = 1e-7
M_inv = sp.sparse.diags(1 / M.diagonal())
# NOTE L_c can become very weird when dealing with corrupted meshes
# L = M_inv @ L_c
L = M_inv @ L_u
A = L.T @ M @ L + w * M
# TODO lambda * ID ?
A += sp.sparse.identity(A.shape[0])
b = w * M @ v
v_star = sp.sparse.linalg.spsolve(A, b)
return v_star
def canonical_rotation(orig_v, vertices, faces):
weights, A = IRF(orig_v, vertices, faces, 3)
Q = np.vstack([-weights[3], -weights[1], weights[2]]).T
u, s, vh = np.linalg.svd(Q)
# product here is reversed because the vertices are row vectors (ie: A * v -> v^T * A^T)
orig_v = orig_v.dot(u)
vertices = vertices.dot(vh.T)
# project to sphere again because of numerical issues w rotation
vertices = project_sphere(vertices)
# pi rotations: yz, xz, xy
rx = np.diag([1, -1, -1])
ry = np.diag([-1, 1, -1])
rz = np.diag([-1, -1, 1])
M = igl.massmatrix(orig_v, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
weights, A = IRF(orig_v, vertices, faces, 8)
weights_x, A_x = IRF(orig_v.dot(rx), vertices.dot(rx), faces, 8)
weights_y, A_y = IRF(orig_v.dot(ry), vertices.dot(ry), faces, 8)
weights_z, A_z = IRF(orig_v.dot(rz), vertices.dot(rz), faces, 8)
# l = [
# sum(weights[4])+sum(weights[5])+sum(weights[7]),
# sum(weights_x[4])+sum(weights_x[5])+sum(weights_x[7]),
# sum(weights_y[4])+sum(weights_y[5])+sum(weights_y[7]),
# sum(weights_z[4])+sum(weights_z[5])+sum(weights_z[7]),
# ]
l = [
np.sum(np.sum(M.dot(A.dot(weights)), axis=0)),
np.sum(np.sum(M.dot(A_x.dot(weights_x)), axis=0)),
np.sum(np.sum(M.dot(A_y.dot(weights_y)), axis=0)),
np.sum(np.sum(M.dot(A_z.dot(weights_z)), axis=0)),
]
# print(np.sum(M.dot(A.dot(weights)), axis=0)),
# print(np.sum(M.dot(A_x.dot(weights_x)), axis=0)),
# print(np.sum(M.dot(A_y.dot(weights_y)), axis=0)),
# print(np.sum(M.dot(A_z.dot(weights_z)), axis=0)),
ind = l.index(max(l))
if ind == 0:
return orig_v, vertices
elif ind == 1:
return orig_v.dot(rx), vertices.dot(rx)
elif ind == 2:
return orig_v.dot(ry), vertices.dot(ry)
elif ind == 3:
return orig_v.dot(rz), vertices.dot(rz)
def set_parameters(self, V, F):
# reset
self.reset()
# compute property given V and F
self.N = igl.per_vertex_normals(V, F)
self.L = igl.cotmatrix(V, F)
VA = igl.massmatrix(V, F, 0)
self.VA = VA.diagonal()
# get face adjacency list
VF, NI = igl.vertex_triangle_adjacency(F, V.shape[0])
adjFList = construct_adjacency_list(VF, NI)
# arap
self.K = igl.arap_rhs(V, F, d=3, energy=1)
# they are all list since length can be different
self.hEList = [None] * V.shape[0]
self.WVecList = [None] * V.shape[0]
self.dVList = [None] * V.shape[0]
for i in range(0, V.shape[0]):
adjF = adjFList[i]
len_adjF = adjF.shape[0]
self.hEList[i] = np.zeros((len_adjF * 3, 2), dtype=int)
self.WVecList[i] = np.zeros(len_adjF * 3)
self.dVList[i] = np.zeros((3, 3 * len_adjF))
for j in range(0, len_adjF):
vIdx = adjF[j]
v0 = F[vIdx, 0]
v1 = F[vIdx, 1]
v2 = F[vIdx, 2]
# half edge indices
# hE = np.array([[v0, v1], [v1, v2], [v2, v0]])
# self.hEList[i] = hE
self.hEList[i][3 * j, 0] = v0
self.hEList[i][3 * j, 1] = v1
self.hEList[i][3 * j + 1, 0] = v1
self.hEList[i][3 * j + 1, 1] = v2
self.hEList[i][3 * j + 2, 0] = v2
self.hEList[i][3 * j + 2, 1] = v0
# weight vec
self.WVecList[i][3 * j] = self.L[v0, v1]
self.WVecList[i][3 * j + 1] = self.L[v1, v2]
self.WVecList[i][3 * j + 2] = self.L[v2, v0]
V_hE0 = V[self.hEList[i][:, 0], :]
V_hE1 = V[self.hEList[i][:, 1], :]
self.dVList[i] = np.transpose(V_hE1 - V_hE0)
self.WVecList[i] = np.diag(self.WVecList[i])
# other var
numV = V.shape[0]
self.zAll = np.random.rand(3, numV) * 2.0 - 1.0
self.uAll = np.random.rand(3, numV) * 2.0 - 1.0
self.zAll = np.zeros((3, numV))
self.uAll = np.zeros((3, numV))
self.rhoAll = np.full(numV, self.param.rhoInit)
def normalize_area(vertices, faces, M=None):
if M is None:
M = igl.massmatrix(vertices, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
A = get_area(vertices, faces)
v_w = M.dot(vertices)
centroid = np.sum(v_w, axis=0) / A
vertices -= centroid
vertices /= np.sqrt(A / (np.pi * 4))
return vertices
def construct_matrices(self):
"""
Construct FEM matrices
"""
V = self.vertices
F = self.faces
# Compute gradient operator: #F*3 by #V
G = igl.grad(V, F).tocoo()
L = igl.cotmatrix(V, F).tocoo()
N = igl.per_face_normals(V, F, np.array([0., 0., 0.]))
A = igl.doublearea(V, F)
A = A[:, np.newaxis]
M = igl.massmatrix(V, F, igl.MASSMATRIX_TYPE_VORONOI).tocoo()
M = M.data
# Compute latitude and longitude directional vector fields
NS = np.reshape(G.dot(self.lat), [self.nf, 3], order='F')
EW = np.cross(NS, N)
# Compute F2V matrix (weigh by area)
# adjacency
i = self.faces.ravel()
j = np.arange(self.nf).repeat(3)
one = np.ones(self.nf * 3)
adj = sparse.csc_matrix((one, (i, j)), shape=(self.nv, self.nf))
tot_area = adj.dot(A)
norm_area = A.ravel().repeat(3) / np.squeeze(tot_area[i])
F2V = sparse.csc_matrix((norm_area, (i, j)), shape=(self.nv, self.nf))
# Compute interpolation matrix
if self.level > 0:
intp = self.intp[self.nv_prev:]
i = np.concatenate(
(np.arange(self.nv), np.arange(self.nv_prev, self.nv)))
j = np.concatenate((np.arange(self.nv_prev), intp[:, 0], intp[:,
1]))
ratio = np.concatenate(
(np.ones(self.nv_prev), 0.5 * np.ones(2 * intp.shape[0])))
intp = sparse.csc_matrix((ratio, (i, j)),
shape=(self.nv, self.nv_prev))
else:
intp = sparse.csc_matrix(np.eye(self.nv))
# Compute vertex mean matrix
self.G = G # gradient matrix
self.L = L # laplacian matrix
self.N = N # normal vectors (per-triangle)
self.NS = NS # north-south vectors (per-triangle)
self.EW = EW # east-west vectors (per-triangle)
self.F2V = F2V # map face quantities to vertices
self.M = M # mass matrix (area of voronoi cell around node. for integration)
self.Seq = self._rotseq(self.vertices)
self.Intp = intp
def features_extractor(classes,DNA_size=50):
"""Extract the features from the samples in the dataset. The type of features is the ShapeDNA.
:param classes: list
list of the classes
:param DNA_size: int
size of the feature vector
"""
print("\nFeature extraction")
for c in classes:
print(c)
for file in os.listdir(os.path.join('.', 'Dataset', c)):
if file.endswith(".off"):
print("\t", file, end=" ... ")
# Load mesh
v, f = igl.read_triangle_mesh(os.path.join('.', 'Dataset',c, file))
M = igl.massmatrix(v, f, igl.MASSMATRIX_TYPE_VORONOI)
v = v / np.sqrt(M.sum())
# Compute Laplacian
L = -igl.cotmatrix(v, f)
M = igl.massmatrix(v, f, igl.MASSMATRIX_TYPE_VORONOI)
# Compute EigenDecomposition
try:
evals, evecs = sp.sparse.linalg.eigsh(L, DNA_size+2, M, sigma=0.0, which='LM', maxiter=1e9, tol=1.e-15)
except:
evals, evecs = sp.sparse.linalg.eigsh(L + 1e-8 * sp.sparse.identity(v.shape[0]), DNA_size+2,
M, sigma=0.0, which='LM', maxiter=1e9, tol=1.e-15)
# Shape DNA
descriptors = evals[2:] / evals[1]
# Save descriptor
np.save(os.path.join('.', 'Dataset', c, file[:-4] + "_DNA"), descriptors, allow_pickle=False)
print("done.")
print("Finished")
def compute_cotan_laplacian(df):
"""
compute mass matrix and cotan laplacian matrix
:param df: pandas data frame
:return L, M, in that order, CSR-sparse matrix
"""
points = df2points(df).T
assert points.shape[-1] == 3
ch = ConvexHull(points)
assert points.shape[0] == ch.points.shape[0]
L = -csr_matrix(igl.cotmatrix(ch.points, ch.simplices)) # positive semidefiniteness
# ISSUE: some areas are way too small such that igl ends up considering it exactly zero.
# as a result, it's not even stored in the sparse matrix. coarsening needed
M = csr_matrix(igl.massmatrix(ch.points, ch.simplices, igl.MASSMATRIX_TYPE_VORONOI))
return L, M
def IRF(orig_v, vertices, faces, max_degree=16):
num_harm = max_degree**2
sigma = 0.001
theta = np.arccos(vertices[:, 2])
phi = np.arctan2(vertices[:, 1], vertices[:, 0])
orig_v = normalize_area(orig_v, faces)
# weighted least squares with mass matrix to account for unequal mesh resolution.
W = igl.massmatrix(orig_v, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
weights = np.zeros((num_harm, 3))
i = 0
A = []
residual = np.copy(orig_v)
for l in range(0, max_degree):
Y = []
for m in range(-l, l + 1):
y = sph_real(l, m, phi, theta)
A.append(y)
Y.append(y)
smooth = 1 #np.exp(-sigma*l*(l+1))
Y = np.vstack(Y).T
#w = np.linalg.solve(Y.T.dot(Y),Y.T.dot(residual))
w = np.linalg.solve(Y.T.dot(W.dot(Y)), Y.T.dot(W.dot(residual)))
residual = residual - Y.dot(w)
i1 = l**2
i2 = (l + 1)**2
weights[i1:i2] = smooth * w
A = np.vstack(A).T
#err = A.dot(weights) - orig_v
#print("mean reconstruction error: " + str(np.mean(norm(err, axis=1))))
return weights, A
def __init__(self, mesh, OUTPUT_PATH):
utils.check_package_is_installed('igl')
import igl
logger.info('GeodesicsSolver')
self.mesh = mesh
self.OUTPUT_PATH = OUTPUT_PATH
self.use_forwards_euler = True
v, f = mesh.to_vertices_and_faces()
v = np.array(v)
f = np.array(f)
# compute necessary data
self.cotans = igl.cotmatrix_entries(v, f) # compute_cotan_field(self.mesh)
self.L = igl.cotmatrix(v, f) # assemble_laplacian_matrix(self.mesh, self.cotans)
self.M = igl.massmatrix(v, f) # create_mass_matrix(mesh)
def mobius_center(orig_v, vertices, faces):
M = igl.massmatrix(orig_v, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
for i in range(10):
# center of mass
mu = np.sum(M.dot(vertices), axis=0)
err = norm(mu)
print("mobius error: " + str(norm(mu)))
if err < 1e-10:
break
# c = -J^-1 * mu
J = compute_jacobian(M, vertices)
c = -np.linalg.inv(J).dot(mu)
# compute inversion
vertices = np.divide((vertices + c),
norm(vertices + c, axis=1).reshape((-1, 1))**2)
vertices = (1 - norm(c)**2) * vertices + c
return vertices
def mass_matrix(self):
if ('mass_matrix' not in self.cache):
self.cache['mass_matrix'] = igl.massmatrix(
self.vertices, self.faces, igl.MASSMATRIX_TYPE_VORONOI)
return self.cache['mass_matrix']
def get_area(vertices, faces):
M = igl.massmatrix(vertices, faces, igl.MASSMATRIX_TYPE_BARYCENTRIC)
return np.sum(M.dot(np.ones(vertices.shape[0])), axis=0)
viewer = igl.viewer.Viewer() viewer.data.set_mesh(V, F) viewer.core.show_lines = False viewer.callback_key_down = key_down # One fixed point on belly b = igl.eigen.MatrixXi([[2556]]) bc = igl.eigen.MatrixXd([[1]]) # Construct Laplacian and mass matrix L = igl.eigen.SparseMatrixd() M = igl.eigen.SparseMatrixd() Minv = igl.eigen.SparseMatrixd() igl.cotmatrix(V, F, L) igl.massmatrix(V, F, igl.MASSMATRIX_TYPE_VORONOI, M) igl.invert_diag(M, Minv) # Bi-Laplacian Q = L.transpose() * (Minv * L) # Zero linear term B = igl.eigen.MatrixXd.Zero(V.rows(), 1) # Lower and upper bound lx = igl.eigen.MatrixXd.Zero(V.rows(), 1) ux = igl.eigen.MatrixXd.Ones(V.rows(), 1) # Equality constraint constrain solution to sum to 1 Beq = igl.eigen.MatrixXd([[0.08]]) Aeq = M.diagonal().transpose().sparseView()
viewer = igl.viewer.Viewer() viewer.data.set_mesh(V, F) viewer.core.show_lines = False viewer.callback_key_down = key_down # One fixed point on belly b = igl.eigen.MatrixXi([[2556]]) bc = igl.eigen.MatrixXd([[1]]) # Construct Laplacian and mass matrix L = igl.eigen.SparseMatrixd() M = igl.eigen.SparseMatrixd() Minv = igl.eigen.SparseMatrixd() igl.cotmatrix(V,F,L) igl.massmatrix(V,F,igl.MASSMATRIX_TYPE_VORONOI,M); igl.invert_diag(M,Minv) # Bi-Laplacian Q = L.transpose() * (Minv * L) # Zero linear term B = igl.eigen.MatrixXd.Zero(V.rows(),1) # Lower and upper bound lx = igl.eigen.MatrixXd.Zero(V.rows(),1) ux = igl.eigen.MatrixXd.Ones(V.rows(),1) # Equality constraint constrain solution to sum to 1 Beq = igl.eigen.MatrixXd([[0.08]]) Aeq = M.diagonal().sparseView().transpose()
c = 0
bbd = 1.0
twod = False
if not igl.read_triangle_mesh("../tutorial/shared/beetle.off",V,F):
print("failed to load mesh")
twod = V.col(2).minCoeff() == V.col(2).maxCoeff()
bbd = (V.colwiseMaxCoeff() - V.colwiseMinCoeff()).norm()
L = igl.eigen.SparseMatrixd()
M = igl.eigen.SparseMatrixd()
igl.cotmatrix(V,F,L)
L = -L
igl.massmatrix(V,F,igl.MASSMATRIX_TYPE_DEFAULT,M)
k = 5
D = igl.eigen.MatrixXd()
if not igl.eigs(L,M,k+1,igl.EIGS_TYPE_SM,U,D):
print("Eigs failed.")
U = (U-U.minCoeff())/(U.maxCoeff()-U.minCoeff());
viewer = igl.viewer.Viewer()
def key_down(viewer,key,mod):
global U, c
if key == ord(' '):
U = U.rightCols(k)
import polyscope
import igl
import numpy as np
import polyscope as ps
import scipy
# https://www.cs.cmu.edu/~kmcrane/Projects/ModelRepository/spot.zip
path = "/Users/cbabraham/data/mesh/spot/spot_triangulated.obj"
# verts and faces
V,F = igl.read_triangle_mesh(path)
# cotan laplacian and mass matrix
L = igl.cotmatrix(V,F)
M = igl.massmatrix(V,F)
# eigenvectors of the laplacian
_, E = scipy.sparse.linalg.eigsh(L, 1000, M, which='BE')
C = V.T.dot(E) # inner product of each eigenvector and the mesh
R = np.einsum('km,nm->nk',C,E)
# V: n x k verts (k = 3)
# E: n x m eigenvectors, to use as bases
# C: k x m basis weights
# R: n x k synthesized vertices from weighted bases
ps.init()
original = ps.register_surface_mesh("mesh", V,F)
def makeLaplacianMatrixSolverIGLSoft(VPos, ITris, anchorsIdx, anchorWeights, makeSolver = True):
VPosE = igl.eigen.MatrixXd(VPos)
ITrisE = igl.eigen.MatrixXi(ITris)
'''
#Doing this check slows things down by more than a factor of 2 (convert to numpy to make faster?)
for f in range(ITrisE.rows()):
v_list = ITrisE.row(f)
v1 = VPosE.row(v_list[0])
v2 = VPosE.row(v_list[1])
v3 = VPosE.row(v_list[2])
if (v1-v2).norm() < 1e-10 and (v1-v3).norm() < 1e-10 and (v2-v3).norm() < 1e-10:
print 'zero area triangle!',f
'''
L = igl.eigen.SparseMatrixd()
M = igl.eigen.SparseMatrixd()
M_inv = igl.eigen.SparseMatrixd()
igl.cotmatrix(VPosE,ITrisE,L)
igl.massmatrix(VPosE,ITrisE,igl.MASSMATRIX_TYPE_VORONOI,M)
#np.set_printoptions(threshold='nan')
#print 'what is M?',M.diagonal()
igl.invert_diag(M,M_inv)
#L = M_inv*L
deltaCoords = (M_inv*L)*VPosE
#TODO: What to do with decaying_anchor_weights?
'''
anchor_dists = []
for i in range(VPosE.rows()):
anchor_dists.append(min([ (VPosE.row(i)-VPosE.row(j)).norm() for j in anchorsIdx ]))
max_anchor_dist = max(anchor_dists)
# assume linear weighting for anchor weights -> we are 0 at the anchors, anchorWeights at max_anchor_dist
decaying_anchor_weights = []
for anchor_dist in anchor_dists:
decaying_anchor_weights.append(anchorWeights*(anchor_dist/max_anchor_dist))
'''
solver = None
if makeSolver:
Q = L*(M_inv*M_inv)*L
#Now add in sparse constraints
diagTerms = igl.eigen.SparseMatrixd(VPos.shape[0], VPos.shape[0])
# anchor points
for a in anchorsIdx:
diagTerms.insert(a, a, anchorWeights)
# off points
'''
for adx,decay_weight in enumerate(decaying_anchor_weights):
if decay_weight == 0:
diagTerms.insert(adx, adx, anchorWeights)
else:
diagTerms.insert(adx, adx, decay_weight)
'''
Q = Q + diagTerms
Q.makeCompressed()
start_time = time.time()
solver = igl.eigen.SimplicialLLTsparse(Q)
#solver = igl.eigen.CholmodSupernodalLLT(Q)
end_time = time.time()
print 'factorization elapsed time:',end_time-start_time,'seconds'
return (L, M_inv, solver, np.array(deltaCoords))
c = 0
bbd = 1.0
twod = False
if not igl.read_triangle_mesh("../../tutorial/shared/beetle.off", V, F):
print("failed to load mesh")
twod = V.col(2).minCoeff() == V.col(2).maxCoeff()
bbd = (V.colwiseMaxCoeff() - V.colwiseMinCoeff()).norm()
L = igl.eigen.SparseMatrixd()
M = igl.eigen.SparseMatrixd()
igl.cotmatrix(V, F, L)
L = -L
igl.massmatrix(V, F, igl.MASSMATRIX_TYPE_DEFAULT, M)
k = 5
D = igl.eigen.MatrixXd()
if not igl.eigs(L, M, k + 1, igl.EIGS_TYPE_SM, U, D):
print("Eigs failed.")
U = (U - U.minCoeff()) / (U.maxCoeff() - U.minCoeff())
viewer = igl.viewer.Viewer()
def key_down(viewer, key, mod):
global U, c
if key == ord(' '):
