def compute_v2(v, gt_mesh):
new_v = igl.eigen.MatrixXd(np.array(v, dtype=np.double))
S = igl.eigen.MatrixXd()
I = igl.eigen.MatrixXi()
C = igl.eigen.MatrixXd()
N = igl.eigen.MatrixXd()
igl.signed_distance(new_v, gt_mesh.v, gt_mesh.f,
igl.SignedDistanceType.SIGNED_DISTANCE_TYPE_UNSIGNED,
S, I, C, N)
distance = np.array(S)
return np.average(distance)
def query(self, queries):
"""Returns numpy array of SDF values for each point in queries"""
queryV = iglhelpers.p2e(queries)
S = igl.eigen.MatrixXd()
B = igl.eigen.MatrixXd()
I = igl.eigen.MatrixXi()
C = igl.eigen.MatrixXd()
N = igl.eigen.MatrixXd()
if self._precomputed and self._signType == igl.SIGNED_DISTANCE_TYPE_FAST_WINDING_NUMBER:
# generate samples from precomputed bvh's
print("[INFO] Generating SDFs")
igl.signed_distance_fast_winding_number(queryV, self._V, self._F,
self._tree, self._fwn_bvh,
S)
print("[INFO] SDFs done")
else:
igl.signed_distance(queryV, self._V, self._F, self._signType, S, I,
C, N)
return iglhelpers.e2p(S)
z = lerp(res, Vmin, Vmax, zi, 2)
for yi in range(res[1]):
y = lerp(res, Vmin, Vmax, yi, 1)
for xi in range(res[0]):
x = lerp(res, Vmin, Vmax, xi, 0)
GV.setRow(xi + res[0] * (yi + res[1] * zi), igl.eigen.MatrixXd([[x, y, z]]))
# compute values
print("Computing distances...")
S = igl.eigen.MatrixXd()
B = igl.eigen.MatrixXd()
I = igl.eigen.MatrixXi()
C = igl.eigen.MatrixXd()
N = igl.eigen.MatrixXd()
igl.signed_distance(GV, V, F, igl.SIGNED_DISTANCE_TYPE_PSEUDONORMAL, S, I, C, N)
# Convert distances to binary inside-outside data --> aliasing artifacts
B = S.copy()
for e in range(B.rows()):
if B[e] > 0:
B[e] = 1
else:
if B[e] < 0:
B[e] = -1
else:
B[e] = 0
print("Marching cubes...")
SV = igl.eigen.MatrixXd()
BV = igl.eigen.MatrixXd()
SF = igl.eigen.MatrixXi()
for yi in range(res[1]):
y = lerp(res, Vmin, Vmax, yi, 1)
for xi in range(res[0]):
x = lerp(res, Vmin, Vmax, xi, 0)
GV.setRow(xi + res[0] * (yi + res[1] * zi),
igl.eigen.MatrixXd([[x, y, z]]))
# compute values
print("Computing distances...")
S = igl.eigen.MatrixXd()
B = igl.eigen.MatrixXd()
I = igl.eigen.MatrixXi()
C = igl.eigen.MatrixXd()
N = igl.eigen.MatrixXd()
igl.signed_distance(GV, V, F, igl.SIGNED_DISTANCE_TYPE_PSEUDONORMAL, S, I,
C, N)
# Convert distances to binary inside-outside data --> aliasing artifacts
B = S.copy()
for e in range(B.rows()):
if B[e] > 0:
B[e] = 1
else:
if B[e] < 0:
B[e] = -1
else:
B[e] = 0
print("Marching cubes...")
SV = igl.eigen.MatrixXd()
BV = igl.eigen.MatrixXd()
SF = igl.eigen.MatrixXi()
for i, index in enumerate(np.random.permutation(opt.lighting.shape[0])):
tic = time.time()
optimizer.zero_grad()
loss = rendering.loss_func_parallel(index, gt_transient, mesh,
mesh_optimization, opt, render_opt,
device)
loss.backward()
optimizer.step()
mesh.v = igl.eigen.MatrixXd(mesh_optimization.v.cpu().data.numpy())
S = igl.eigen.MatrixXd()
I = igl.eigen.MatrixXi()
C = igl.eigen.MatrixXd()
N = igl.eigen.MatrixXd()
igl.signed_distance(mesh.v, space_carving_mesh.v, space_carving_mesh.f,
igl.SignedDistanceType(0), S, I, C, N)
for x in list(compress(range(mesh.v.rows()), S > 0)):
mesh.v.setRow(x, C.row(x))
igl.per_face_normals(mesh.v, mesh.f, mesh.fn)
l2, transient = rendering.evaluate_L2(gt_transient, mesh, render_opt)
print('%05d update time: %5.5f L2 loss: %5.5f ' %
(t * opt.lighting.shape[0] + i, time.time() - tic, l2))
l2_record[t * opt.lighting.shape[0] + i] = l2
filename = folder_name + '%05d.mat' % (t * opt.lighting.shape[0] + i)
scipy.io.savemat(filename,
mdict={
'v': np.array(mesh.v),
'transient': transient,
'l2': l2,