L = sp.csr_matrix((W, (I, J)), shape=(n, n))
L = L - sp.spdiags(L * np.ones(n), 0, n, n)
return L
if __name__ == "__main__":
# from psbody.mesh import Mesh
# m0 = Mesh(filename='assets/sphube.obj')
# m1 = Mesh(filename='assets/sphube.obj')
# m1.v *= np.array([0.5, 1., 2.])
from utils.smpl_paths import SmplPaths
smp = SmplPaths()
m0 = smp.get_mesh(smp.get_smpl())
L0 = cpu_laplacian(m0.v.astype(np.float32), m0.f)
lap0 = L0.dot(m0.v.astype(np.float32))
tf_v0 = tf.expand_dims(m0.v.astype(np.float32), 0)
tf_v = tf.tile(tf_v0, (5, 1, 1))
tf_L = batch_laplacian(tf_v, m0.f.astype(np.int32))
tf_L0 = batch_laplacian(tf_v0, m0.f.astype(np.int32))
tf_lap = sparse_dense_matmul_batch(tf_L, tf_v)
# tf_diff = tf.reduce_max(tf.abs(f_L[0] - tf_L[-1]))
with tf.Session():
tf_L_e = tf.sparse_tensor_to_dense(tf_L).eval()
else:
verts = self.smooth_cotlap(verts, smoothness)
return verts
if __name__ == "__main__":
IS_SMPL = True
fpath = "/BS/cpatel/work/data/learn_anim/mixture_exp31/000_0/smooth_TShirtNoCoat/0990/pred_0.ply"
if not IS_SMPL:
ms = Mesh(filename=fpath)
else:
from utils.smpl_paths import SmplPaths
dp = SmplPaths(gender='female')
smpl = dp.get_smpl()
ms = Mesh(v=smpl.r, f=smpl.f)
smoothing = DiffusionSmoothing(ms.v, ms.f)
verts_smooth = ms.v.copy()
for i in range(20):
verts_smooth = smoothing.smooth(verts_smooth, smoothness=0.05)
ms_smooth = Mesh(v=verts_smooth, f=ms.f)
# from psbody.mesh import MeshViewers
# mvs = MeshViewers((1,3))
# mvs[0][0].set_static_meshes([ms])
# mvs[0][1].set_static_meshes([ms_smooth])
# mvs[0][2].set_static_meshes([ms_smooth2])
# import ipdb