def ready_arguments(fname_or_dict,
posekey4vposed='pose',
shared_args=None,
chTrans=None,
chBetas=None):
import numpy as np
import pickle
import chumpy as ch
from chumpy.ch import MatVecMult
from mano.webuser.posemapper import posemap
if not isinstance(fname_or_dict, dict):
dd = pickle.load(open(fname_or_dict))
else:
dd = fname_or_dict
want_shapemodel = 'shapedirs' in dd
nposeparms = dd['kintree_table'].shape[1] * 3
if 'trans' not in dd:
dd['trans'] = np.zeros(3)
if 'pose' not in dd:
dd['pose'] = np.zeros(nposeparms)
if 'shapedirs' in dd and 'betas' not in dd:
dd['betas'] = np.zeros(dd['shapedirs'].shape[-1])
if chTrans is not None:
dd['trans'] = chTrans
if chTrans is not None:
dd['betas'] = chBetas
for s in [
'v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs',
'betas', 'J', 'fullpose', 'pose_dof'
]:
if (s in dd) and not hasattr(dd[s], 'dterms'):
if shared_args is not None and s in shared_args:
dd[s] = shared_args[s]
else:
dd[s] = ch.array(dd[s])
assert (posekey4vposed in dd)
if want_shapemodel:
dd['v_shaped'] = dd['shapedirs'].dot(dd['betas']) + dd['v_template']
v_shaped = dd['v_shaped']
J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:, 0])
J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:, 1])
J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:, 2])
dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T
dd['v_posed'] = v_shaped + dd['posedirs'].dot(
posemap(dd['bs_type'])(dd[posekey4vposed]))
else:
dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(
posemap(dd['bs_type'])(dd[posekey4vposed]))
return dd
def ready_arguments(fname_or_dict):
if not isinstance(fname_or_dict, dict):
dd = pickle.load(open(fname_or_dict, 'rb'), encoding='latin1')
else:
dd = fname_or_dict
backwards_compatibility_replacements(dd)
want_shapemodel = 'shapedirs' in dd
nposeparms = dd['kintree_table'].shape[1] * 3
if 'trans' not in dd:
dd['trans'] = np.zeros(3)
if 'pose' not in dd:
dd['pose'] = np.zeros(nposeparms)
if 'shapedirs' in dd and 'betas' not in dd:
dd['betas'] = np.zeros(dd['shapedirs'].shape[-1])
for s in [
'v_template', 'weights', 'posedirs', 'pose', 'trans', 'shapedirs',
'betas', 'J'
]:
if (s in dd) and not hasattr(dd[s], 'dterms'):
dd[s] = ch.array(dd[s])
if want_shapemodel:
dd['v_shaped'] = dd['shapedirs'].dot(dd['betas']) + dd['v_template']
v_shaped = dd['v_shaped']
J_tmpx = MatVecMult(dd['J_regressor'], v_shaped[:, 0])
J_tmpy = MatVecMult(dd['J_regressor'], v_shaped[:, 1])
J_tmpz = MatVecMult(dd['J_regressor'], v_shaped[:, 2])
dd['J'] = ch.vstack((J_tmpx, J_tmpy, J_tmpz)).T
dd['v_posed'] = v_shaped + dd['posedirs'].dot(
posemap(dd['bs_type'])(dd['pose']))
else:
dd['v_posed'] = dd['v_template'] + dd['posedirs'].dot(
posemap(dd['bs_type'])(dd['pose']))
return dd
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