def verts_decorated(trans, pose,
v_template, J, weights, kintree_table, bs_style, f,
bs_type=None, posedirs=None, betas=None, shapedirs=None, want_Jtr=False):
for which in [trans, pose, v_template, weights, posedirs, betas, shapedirs]:
if which is not None:
assert ischumpy(which)
v = v_template
if shapedirs is not None:
if betas is None:
betas = chumpy.zeros(shapedirs.shape[-1])
v_shaped = v + shapedirs.dot(betas)
else:
v_shaped = v
if posedirs is not None:
v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose))
else:
v_posed = v_shaped
v = v_posed
if sp.issparse(J):
regressor = J
J_tmpx = MatVecMult(regressor, v_shaped[:,0])
J_tmpy = MatVecMult(regressor, v_shaped[:,1])
J_tmpz = MatVecMult(regressor, v_shaped[:,2])
J = chumpy.vstack((J_tmpx, J_tmpy, J_tmpz)).T
else:
assert(ischumpy(J))
assert(bs_style=='lbs')
result, Jtr = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr=True, xp=chumpy)
tr = trans.reshape((1,3))
result = result + tr
Jtr = Jtr + tr
result.trans = trans
result.f = f
result.pose = pose
result.v_template = v_template
result.J = J
result.weights = weights
result.kintree_table = kintree_table
result.bs_style = bs_style
result.bs_type =bs_type
if posedirs is not None:
result.posedirs = posedirs
result.v_posed = v_posed
if shapedirs is not None:
result.shapedirs = shapedirs
result.betas = betas
result.v_shaped = v_shaped
if want_Jtr:
result.J_transformed = Jtr
return result
def verts_decorated(trans, pose,
v_template, J, weights, kintree_table, bs_style, f,
bs_type=None, posedirs=None, betas=None, shapedirs=None, want_Jtr=False):
for which in [trans, pose, v_template, weights, posedirs, betas, shapedirs]:
if which is not None:
assert ischumpy(which)
v = v_template
if shapedirs is not None:
if betas is None:
betas = chumpy.zeros(shapedirs.shape[-1])
v_shaped = v + shapedirs.dot(betas)
else:
v_shaped = v
pose11 = posemap(bs_type)(pose)
if posedirs is not None:
v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose))
else:
v_posed = v_shaped
v = v_posed
np.save("/home/lgh/SMPLify/point.npy",v)
if sp.issparse(J):
regressor = J
J_tmpx = MatVecMult(regressor, v_shaped[:,0])
J_tmpy = MatVecMult(regressor, v_shaped[:,1])
J_tmpz = MatVecMult(regressor, v_shaped[:,2])
J = chumpy.vstack((J_tmpx, J_tmpy, J_tmpz)).T
else:
assert(ischumpy(J))
assert(bs_style=='lbs')
result, Jtr = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr=True, xp=chumpy)
tr = trans.reshape((1,3))
result = result + tr
Jtr = Jtr + tr
result.trans = trans
result.f = f
result.pose = pose
result.v_template = v_template
result.J = J
result.weights = weights
result.kintree_table = kintree_table
result.bs_style = bs_style
result.bs_type =bs_type
if posedirs is not None:
result.posedirs = posedirs
result.v_posed = v_posed
if shapedirs is not None:
result.shapedirs = shapedirs
result.betas = betas
result.v_shaped = v_shaped
if want_Jtr:
result.J_transformed = Jtr
return result
def verts_core(pose, v, J, weights, kintree_table, bs_style, want_Jtr=False, xp=chumpy):
if xp == chumpy:
assert(hasattr(pose, 'dterms'))
assert(hasattr(v, 'dterms'))
assert(hasattr(J, 'dterms'))
assert(hasattr(weights, 'dterms'))
assert(bs_style=='lbs')
result = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr, xp)
return result
def verts_core(pose, v, J, weights, kintree_table, bs_style, want_Jtr=False, xp=chumpy):
if xp == chumpy:
assert(hasattr(pose, 'dterms'))
assert(hasattr(v, 'dterms'))
assert(hasattr(J, 'dterms'))
assert(hasattr(weights, 'dterms'))
assert(bs_style=='lbs')
result = lbs.verts_core(pose, v, J, weights, kintree_table, want_Jtr, xp)
return result
def verts_decorated(trans,
pose,
v_template,
J,
weights,
kintree_table,
bs_style,
f,
bs_type=None,
posedirs=None,
betas=None,
shapedirs=None,
want_Jtr=False):
for which in [
trans, pose, v_template, weights, posedirs, betas, shapedirs
]:
if which is not None:
assert ischumpy(which)
v = v_template
print "********"
print "betas : " + str(betas.shape)
print "posedirs : " + str(posedirs.shape)
print "shapedirs : " + str(shapedirs.shape)
if shapedirs is not None:
if betas is None:
betas = chumpy.zeros(shapedirs.shape[-1])
v_shaped = v + shapedirs.dot(betas)
#print shapedirs.dot(betas).shape
#v_shaped = np.copy(v)
#for i in range(0,shapedirs.shape[0]):
# v_shaped[i,:] = v[i,:] + shapedirs[i,:,:].dot(betas)
#print v_shaped[1,:]
#xx = np.random.rand(4,4,4)
#print xx
#print xx[0,:,1]
#print (np.array(xx)).tolist()
#print (np.array(xx)).tolist()[1][1][1]
else:
v_shaped = v
if posedirs is not None:
print pose.shape
print posemap(bs_type)(pose).shape
v_posed = v_shaped + posedirs.dot(posemap(bs_type)(pose))
#v_posed = v_shaped
else:
v_posed = v_shaped
v = v_posed
# IS this needed
if sp.issparse(J):
regressor = J
print regressor.shape
J_tmpx = MatVecMult(regressor, v_shaped[:, 0])
J_tmpy = MatVecMult(regressor, v_shaped[:, 1])
J_tmpz = MatVecMult(regressor, v_shaped[:, 2])
J = chumpy.vstack((J_tmpx, J_tmpy, J_tmpz)).T
#XX = np.array(regressor) * np.array(v_shaped[:,0])
#print "--"
#print XX.shape
#print XX
#print v_shaped[200,2]
#print XX[1]
else:
assert (ischumpy(J))
assert (bs_style == 'lbs')
result, Jtr = lbs.verts_core(pose,
v,
J,
weights,
kintree_table,
want_Jtr=True,
xp=chumpy)
tr = trans.reshape((1, 3))
result = result + tr
Jtr = Jtr + tr
result.trans = trans
result.f = f
result.pose = pose
result.v_template = v_template
result.J = J
result.weights = weights
result.kintree_table = kintree_table
result.bs_style = bs_style
result.bs_type = bs_type
if posedirs is not None:
result.posedirs = posedirs
result.v_posed = v_posed
if shapedirs is not None:
result.shapedirs = shapedirs
result.betas = betas
result.v_shaped = v_shaped
if want_Jtr:
result.J_transformed = Jtr
#print Jtr
return result
