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from .quaternion import *
import scipy.ndimage.filters as filters
class Skeleton(object):
def __init__(self, offset, kinematic_tree, device):
self.device = device
self._raw_offset_np = offset.numpy()
self._raw_offset = offset.clone().detach().to(device).float()
self._kinematic_tree = kinematic_tree
self._offset = None
self._parents = [0] * len(self._raw_offset)
self._parents[0] = -1
for chain in self._kinematic_tree:
for j in range(1, len(chain)):
self._parents[chain[j]] = chain[j-1]
def njoints(self):
return len(self._raw_offset)
def offset(self):
return self._offset
def set_offset(self, offsets):
self._offset = offsets.clone().detach().to(self.device).float()
def kinematic_tree(self):
return self._kinematic_tree
def parents(self):
return self._parents
# joints (batch_size, joints_num, 3)
def get_offsets_joints_batch(self, joints):
assert len(joints.shape) == 3
_offsets = self._raw_offset.expand(joints.shape[0], -1, -1).clone()
for i in range(1, self._raw_offset.shape[0]):
_offsets[:, i] = torch.norm(joints[:, i] - joints[:, self._parents[i]], p=2, dim=1)[:, None] * _offsets[:, i]
self._offset = _offsets.detach()
return _offsets
# joints (joints_num, 3)
def get_offsets_joints(self, joints):
assert len(joints.shape) == 2
_offsets = self._raw_offset.clone()
for i in range(1, self._raw_offset.shape[0]):
# print(joints.shape)
_offsets[i] = torch.norm(joints[i] - joints[self._parents[i]], p=2, dim=0) * _offsets[i]
self._offset = _offsets.detach()
return _offsets
# face_joint_idx should follow the order of right hip, left hip, right shoulder, left shoulder
# joints (batch_size, joints_num, 3)
def inverse_kinematics_np(self, joints, face_joint_idx, smooth_forward=False):
assert len(face_joint_idx) == 4
'''Get Forward Direction'''
l_hip, r_hip, sdr_r, sdr_l = face_joint_idx
across1 = joints[:, r_hip] - joints[:, l_hip]
across2 = joints[:, sdr_r] - joints[:, sdr_l]
across = across1 + across2
across = across / np.sqrt((across**2).sum(axis=-1))[:, np.newaxis]
# print(across1.shape, across2.shape)
# forward (batch_size, 3)
forward = np.cross(np.array([[0, 1, 0]]), across, axis=-1)
if smooth_forward:
forward = filters.gaussian_filter1d(forward, 20, axis=0, mode='nearest')
# forward (batch_size, 3)
forward = forward / np.sqrt((forward**2).sum(axis=-1))[..., np.newaxis]
'''Get Root Rotation'''
target = np.array([[0,0,1]]).repeat(len(forward), axis=0)
root_quat = qbetween_np(forward, target)
'''Inverse Kinematics'''
# quat_params (batch_size, joints_num, 4)
# print(joints.shape[:-1])
quat_params = np.zeros(joints.shape[:-1] + (4,))
# print(quat_params.shape)
root_quat[0] = np.array([[1.0, 0.0, 0.0, 0.0]])
quat_params[:, 0] = root_quat
# quat_params[0, 0] = np.array([[1.0, 0.0, 0.0, 0.0]])
for chain in self._kinematic_tree:
R = root_quat
for j in range(len(chain) - 1):
# (batch, 3)
u = self._raw_offset_np[chain[j+1]][np.newaxis,...].repeat(len(joints), axis=0)
# print(u.shape)
# (batch, 3)
v = joints[:, chain[j+1]] - joints[:, chain[j]]
v = v / np.sqrt((v**2).sum(axis=-1))[:, np.newaxis]
# print(u.shape, v.shape)
rot_u_v = qbetween_np(u, v)
R_loc = qmul_np(qinv_np(R), rot_u_v)
quat_params[:,chain[j + 1], :] = R_loc
R = qmul_np(R, R_loc)
return quat_params
# Be sure root joint is at the beginning of kinematic chains
def forward_kinematics(self, quat_params, root_pos, skel_joints=None, do_root_R=True):
# quat_params (batch_size, joints_num, 4)
# joints (batch_size, joints_num, 3)
# root_pos (batch_size, 3)
if skel_joints is not None:
offsets = self.get_offsets_joints_batch(skel_joints)
if len(self._offset.shape) == 2:
offsets = self._offset.expand(quat_params.shape[0], -1, -1)
joints = torch.zeros(quat_params.shape[:-1] + (3,)).to(self.device)
joints[:, 0] = root_pos
for chain in self._kinematic_tree:
if do_root_R:
R = quat_params[:, 0]
else:
R = torch.tensor([[1.0, 0.0, 0.0, 0.0]]).expand(len(quat_params), -1).detach().to(self.device)
for i in range(1, len(chain)):
R = qmul(R, quat_params[:, chain[i]])
offset_vec = offsets[:, chain[i]]
joints[:, chain[i]] = qrot(R, offset_vec) + joints[:, chain[i-1]]
return joints
# Be sure root joint is at the beginning of kinematic chains
def forward_kinematics_np(self, quat_params, root_pos, skel_joints=None, do_root_R=True):
# quat_params (batch_size, joints_num, 4)
# joints (batch_size, joints_num, 3)
# root_pos (batch_size, 3)
if skel_joints is not None:
skel_joints = torch.from_numpy(skel_joints)
offsets = self.get_offsets_joints_batch(skel_joints)
if len(self._offset.shape) == 2:
offsets = self._offset.expand(quat_params.shape[0], -1, -1)
offsets = offsets.numpy()
joints = np.zeros(quat_params.shape[:-1] + (3,))
joints[:, 0] = root_pos
for chain in self._kinematic_tree:
if do_root_R:
R = quat_params[:, 0]
else:
R = np.array([[1.0, 0.0, 0.0, 0.0]]).repeat(len(quat_params), axis=0)
for i in range(1, len(chain)):
R = qmul_np(R, quat_params[:, chain[i]])
offset_vec = offsets[:, chain[i]]
joints[:, chain[i]] = qrot_np(R, offset_vec) + joints[:, chain[i - 1]]
return joints
def forward_kinematics_cont6d_np(self, cont6d_params, root_pos, skel_joints=None, do_root_R=True):
# cont6d_params (batch_size, joints_num, 6)
# joints (batch_size, joints_num, 3)
# root_pos (batch_size, 3)
if skel_joints is not None:
skel_joints = torch.from_numpy(skel_joints)
offsets = self.get_offsets_joints_batch(skel_joints)
if len(self._offset.shape) == 2:
offsets = self._offset.expand(cont6d_params.shape[0], -1, -1)
offsets = offsets.numpy()
joints = np.zeros(cont6d_params.shape[:-1] + (3,))
joints[:, 0] = root_pos
for chain in self._kinematic_tree:
if do_root_R:
matR = cont6d_to_matrix_np(cont6d_params[:, 0])
else:
matR = np.eye(3)[np.newaxis, :].repeat(len(cont6d_params), axis=0)
for i in range(1, len(chain)):
matR = np.matmul(matR, cont6d_to_matrix_np(cont6d_params[:, chain[i]]))
offset_vec = offsets[:, chain[i]][..., np.newaxis]
# print(matR.shape, offset_vec.shape)
joints[:, chain[i]] = np.matmul(matR, offset_vec).squeeze(-1) + joints[:, chain[i-1]]
return joints
def forward_kinematics_cont6d(self, cont6d_params, root_pos, skel_joints=None, do_root_R=True):
# cont6d_params (batch_size, joints_num, 6)
# joints (batch_size, joints_num, 3)
# root_pos (batch_size, 3)
if skel_joints is not None:
# skel_joints = torch.from_numpy(skel_joints)
offsets = self.get_offsets_joints_batch(skel_joints)
if len(self._offset.shape) == 2:
offsets = self._offset.expand(cont6d_params.shape[0], -1, -1)
joints = torch.zeros(cont6d_params.shape[:-1] + (3,)).to(cont6d_params.device)
joints[..., 0, :] = root_pos
for chain in self._kinematic_tree:
if do_root_R:
matR = cont6d_to_matrix(cont6d_params[:, 0])
else:
matR = torch.eye(3).expand((len(cont6d_params), -1, -1)).detach().to(cont6d_params.device)
for i in range(1, len(chain)):
matR = torch.matmul(matR, cont6d_to_matrix(cont6d_params[:, chain[i]]))
offset_vec = offsets[:, chain[i]].unsqueeze(-1)
# print(matR.shape, offset_vec.shape)
joints[:, chain[i]] = torch.matmul(matR, offset_vec).squeeze(-1) + joints[:, chain[i-1]]
return joints
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