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import torch
from hand_utils.mano_wrapper import MANO
from hand_utils.geometry_utils import aa_to_rotmat
import numpy as np
def run_mano(trans, root_orient, hand_pose, is_right=None, betas=None, use_cuda=True):
"""
Forward pass of the SMPL model and populates pred_data accordingly with
joints3d, verts3d, points3d.
trans : B x T x 3
root_orient : B x T x 3
body_pose : B x T x J*3
betas : (optional) B x D
"""
MANO_cfg = {
'DATA_DIR': '_DATA/data/',
'MODEL_PATH': '_DATA/data/mano',
'GENDER': 'neutral',
'NUM_HAND_JOINTS': 15,
'CREATE_BODY_POSE': False
}
mano_cfg = {k.lower(): v for k,v in MANO_cfg.items()}
mano = MANO(**mano_cfg)
if use_cuda:
mano = mano.cuda()
B, T, _ = root_orient.shape
NUM_JOINTS = 15
mano_params = {
'global_orient': root_orient.reshape(B*T, -1),
'hand_pose': hand_pose.reshape(B*T*NUM_JOINTS, 3),
'betas': betas.reshape(B*T, -1),
}
rotmat_mano_params = mano_params
rotmat_mano_params['global_orient'] = aa_to_rotmat(mano_params['global_orient']).view(B*T, 1, 3, 3)
rotmat_mano_params['hand_pose'] = aa_to_rotmat(mano_params['hand_pose']).view(B*T, NUM_JOINTS, 3, 3)
rotmat_mano_params['transl'] = trans.reshape(B*T, 3)
if use_cuda:
mano_output = mano(**{k: v.float().cuda() for k,v in rotmat_mano_params.items()}, pose2rot=False)
else:
mano_output = mano(**{k: v.float() for k,v in rotmat_mano_params.items()}, pose2rot=False)
faces_right = mano.faces
faces_new = np.array([[92, 38, 234],
[234, 38, 239],
[38, 122, 239],
[239, 122, 279],
[122, 118, 279],
[279, 118, 215],
[118, 117, 215],
[215, 117, 214],
[117, 119, 214],
[214, 119, 121],
[119, 120, 121],
[121, 120, 78],
[120, 108, 78],
[78, 108, 79]])
faces_right = np.concatenate([faces_right, faces_new], axis=0)
faces_n = len(faces_right)
faces_left = faces_right[:,[0,2,1]]
outputs = {
"joints": mano_output.joints.reshape(B, T, -1, 3),
"vertices": mano_output.vertices.reshape(B, T, -1, 3),
}
if not is_right is None:
# outputs["vertices"][..., 0] = (2*is_right-1)*outputs["vertices"][..., 0]
# outputs["joints"][..., 0] = (2*is_right-1)*outputs["joints"][..., 0]
is_right = (is_right[:, :, 0].cpu().numpy() > 0)
faces_result = np.zeros((B, T, faces_n, 3))
faces_right_expanded = np.expand_dims(np.expand_dims(faces_right, axis=0), axis=0)
faces_left_expanded = np.expand_dims(np.expand_dims(faces_left, axis=0), axis=0)
faces_result = np.where(is_right[..., np.newaxis, np.newaxis], faces_right_expanded, faces_left_expanded)
outputs["faces"] = torch.from_numpy(faces_result.astype(np.int32))
return outputs
def run_mano_left(trans, root_orient, hand_pose, is_right=None, betas=None, use_cuda=True, fix_shapedirs=True):
"""
Forward pass of the SMPL model and populates pred_data accordingly with
joints3d, verts3d, points3d.
trans : B x T x 3
root_orient : B x T x 3
body_pose : B x T x J*3
betas : (optional) B x D
"""
MANO_cfg = {
'DATA_DIR': '_DATA/data_left/',
'MODEL_PATH': '_DATA/data_left/mano_left',
'GENDER': 'neutral',
'NUM_HAND_JOINTS': 15,
'CREATE_BODY_POSE': False,
'is_rhand': False
}
mano_cfg = {k.lower(): v for k,v in MANO_cfg.items()}
mano = MANO(**mano_cfg)
if use_cuda:
mano = mano.cuda()
# fix MANO shapedirs of the left hand bug (https://github.com/vchoutas/smplx/issues/48)
if fix_shapedirs:
mano.shapedirs[:, 0, :] *= -1
B, T, _ = root_orient.shape
NUM_JOINTS = 15
mano_params = {
'global_orient': root_orient.reshape(B*T, -1),
'hand_pose': hand_pose.reshape(B*T*NUM_JOINTS, 3),
'betas': betas.reshape(B*T, -1),
}
rotmat_mano_params = mano_params
rotmat_mano_params['global_orient'] = aa_to_rotmat(mano_params['global_orient']).view(B*T, 1, 3, 3)
rotmat_mano_params['hand_pose'] = aa_to_rotmat(mano_params['hand_pose']).view(B*T, NUM_JOINTS, 3, 3)
rotmat_mano_params['transl'] = trans.reshape(B*T, 3)
if use_cuda:
mano_output = mano(**{k: v.float().cuda() for k,v in rotmat_mano_params.items()}, pose2rot=False)
else:
mano_output = mano(**{k: v.float() for k,v in rotmat_mano_params.items()}, pose2rot=False)
faces_right = mano.faces
faces_new = np.array([[92, 38, 234],
[234, 38, 239],
[38, 122, 239],
[239, 122, 279],
[122, 118, 279],
[279, 118, 215],
[118, 117, 215],
[215, 117, 214],
[117, 119, 214],
[214, 119, 121],
[119, 120, 121],
[121, 120, 78],
[120, 108, 78],
[78, 108, 79]])
faces_right = np.concatenate([faces_right, faces_new], axis=0)
faces_n = len(faces_right)
faces_left = faces_right[:,[0,2,1]]
outputs = {
"joints": mano_output.joints.reshape(B, T, -1, 3),
"vertices": mano_output.vertices.reshape(B, T, -1, 3),
}
if not is_right is None:
# outputs["vertices"][..., 0] = (2*is_right-1)*outputs["vertices"][..., 0]
# outputs["joints"][..., 0] = (2*is_right-1)*outputs["joints"][..., 0]
is_right = (is_right[:, :, 0].cpu().numpy() > 0)
faces_result = np.zeros((B, T, faces_n, 3))
faces_right_expanded = np.expand_dims(np.expand_dims(faces_right, axis=0), axis=0)
faces_left_expanded = np.expand_dims(np.expand_dims(faces_left, axis=0), axis=0)
faces_result = np.where(is_right[..., np.newaxis, np.newaxis], faces_right_expanded, faces_left_expanded)
outputs["faces"] = torch.from_numpy(faces_result.astype(np.int32))
return outputs
def run_mano_twohands(init_trans, init_rot, init_hand_pose, is_right, init_betas, use_cuda=True, fix_shapedirs=True):
outputs_left = run_mano_left(init_trans[0:1], init_rot[0:1], init_hand_pose[0:1], None, init_betas[0:1], use_cuda=use_cuda, fix_shapedirs=fix_shapedirs)
outputs_right = run_mano(init_trans[1:2], init_rot[1:2], init_hand_pose[1:2], None, init_betas[1:2], use_cuda=use_cuda)
outputs_two = {
"vertices": torch.cat((outputs_left["vertices"], outputs_right["vertices"]), dim=0),
"joints": torch.cat((outputs_left["joints"], outputs_right["joints"]), dim=0)
}
return outputs_two |