def create_prior_from_cmu(self):
"""Load the gmm from the CMU motion database."""
from os.path import dirname
# import cPickle as pickle
import pickle
with open(
os.path.join(dirname(dirname(__file__)), 'models',
'gmm_%02d.pkl' % self.n_gaussians), 'rb') as f:
gmm = pickle.load(f, encoding="latin1")
precs = ch.asarray([np.linalg.inv(cov) for cov in gmm['covars']])
chols = ch.asarray([np.linalg.cholesky(prec) for prec in precs])
# The constant term:
sqrdets = np.array([(np.sqrt(np.linalg.det(c)))
for c in gmm['covars']])
const = (2 * np.pi)**(69 / 2.)
self.weights = ch.asarray(gmm['weights'] / (const *
(sqrdets / sqrdets.min())))
return (MaxMixtureCompleteWrapper(means=gmm['means'],
precs=chols,
weights=self.weights,
prefix=self.prefix))
def create_prior_from_cmu(self):
"""Load the gmm from the CMU motion database."""
from os.path import dirname
import cPickle as pickle
with open(
os.path.join(
dirname(dirname(__file__)), 'models', 'gmm_%02d.pkl' %
self.n_gaussians)) as f:
gmm = pickle.load(f)
precs = ch.asarray([np.linalg.inv(cov) for cov in gmm['covars']])
chols = ch.asarray([np.linalg.cholesky(prec) for prec in precs])
# The constant term:
sqrdets = np.array([(np.sqrt(np.linalg.det(c)))
for c in gmm['covars']])
const = (2 * np.pi)**(69 / 2.)
self.weights = ch.asarray(gmm['weights'] / (const *
(sqrdets / sqrdets.min())))
return (MaxMixtureCompleteWrapper(
means=gmm['means'],
precs=chols,
weights=self.weights,
prefix=self.prefix))
def create_prior_from_cmu(self):
"""Get the prior from the CMU motion database."""
from os.path import realpath
import cPickle as pickle
with open(os.path.join(os.path.dirname(__file__),
'..', 'models', '3D',
'gmm_best_%02d.pkl' % self.n_gaussians)) as f:
gmm = pickle.load(f)
precs = ch.asarray([np.linalg.inv(cov) for cov in gmm.covars_])
chols = ch.asarray([np.linalg.cholesky(prec) for prec in precs])
# The constant term:
sqrdets = np.array([(np.sqrt(np.linalg.det(c))) for c in gmm.covars_])
const = (2*np.pi)**(69/2.)
self.weights = ch.asarray(gmm.weights_ / (const * (sqrdets/sqrdets.min())))
return(MaxMixtureCompleteWrapper(means=gmm.means_, precs=chols,
weights=self.weights, prefix=self.prefix))
import chumpy as ch
import scipy.sparse as sp
from capsule_ch import Capsule
joint2name = ['pelvis', 'leftThigh', 'rightThigh', 'spine', 'leftCalf',
'rightCalf', 'spine1', 'leftFoot', 'rightFoot', 'spine2', 'neck',
'leftShoulder', 'rightShoulder', 'head', 'leftUpperArm',
'rightUpperArm', 'leftForeArm', 'rightForeArm', 'leftHand',
'rightHand']
# the orientation of each capsule
rots0 = ch.asarray(
[[0, 0, np.pi / 2], [0, 0, np.pi], [0, 0, np.pi], [0, 0, np.pi / 2],
[0, 0, np.pi], [0, 0, np.pi], [0, 0, np.pi / 2], [np.pi / 2, 0, 0],
[np.pi / 2, 0, 0], [0, 0, np.pi / 2], [0, 0, 0], [0, 0, -np.pi / 2],
[0, 0, np.pi / 2], [0, 0, 0], [0, 0, -np.pi / 2], [0, 0, np.pi / 2],
[0, 0, -np.pi / 2], [0, 0, np.pi / 2], [0, 0, -np.pi / 2],
[0, 0, np.pi / 2]])
# groups hands and fingers, feet and toes
# each comment line provides the body part corresonding to the capsule
# and the corresponding id
mujoco2segm = [[0], # hip 0
[1], # leftThigh 1
[2], # rightThigh 2
[3], # spine 3
[4], # leftCalf 4
[5], # rightCalf 5
[6], # spine1 6
[7, 10], # leftFoot + leftToes 7
import scipy.sparse as sp
from capsule_ch import Capsule
joint2name = [
'pelvis', 'leftThigh', 'rightThigh', 'spine', 'leftCalf', 'rightCalf',
'spine1', 'leftFoot', 'rightFoot', 'spine2', 'neck', 'leftShoulder',
'rightShoulder', 'head', 'leftUpperArm', 'rightUpperArm', 'leftForeArm',
'rightForeArm', 'leftHand', 'rightHand'
]
# the orientation of each capsule
rots0 = ch.asarray([[0, 0, np.pi / 2], [0, 0, np.pi], [0, 0, np.pi],
[0, 0, np.pi / 2], [0, 0, np.pi], [0, 0, np.pi],
[0, 0, np.pi / 2], [np.pi / 2, 0, 0], [np.pi / 2, 0, 0],
[0, 0, np.pi / 2], [0, 0, 0], [0, 0, -np.pi / 2],
[0, 0, np.pi / 2], [0, 0, 0], [0, 0, -np.pi / 2],
[0, 0, np.pi / 2], [0, 0, -np.pi / 2], [0, 0, np.pi / 2],
[0, 0, -np.pi / 2], [0, 0, np.pi / 2]])
# groups hands and fingers, feet and toes
# each comment line provides the body part corresonding to the capsule
# and the corresponding id
mujoco2segm = [
[0], # hip 0
[1], # leftThigh 1
[2], # rightThigh 2
[3], # spine 3
[4], # leftCalf 4
[5], # rightCalf 5
[6], # spine1 6