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| # -*- coding: utf-8 -*- | |
| # Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is | |
| # holder of all proprietary rights on this computer program. | |
| # You can only use this computer program if you have closed | |
| # a license agreement with MPG or you get the right to use the computer | |
| # program from someone who is authorized to grant you that right. | |
| # Any use of the computer program without a valid license is prohibited and | |
| # liable to prosecution. | |
| # | |
| # Copyright©2019 Max-Planck-Gesellschaft zur Förderung | |
| # der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute | |
| # for Intelligent Systems. All rights reserved. | |
| # | |
| # Contact: ps-license@tuebingen.mpg.de | |
| from dataclasses import asdict, dataclass, fields | |
| from typing import NewType, Optional, Union | |
| import numpy as np | |
| import torch | |
| Tensor = NewType("Tensor", torch.Tensor) | |
| Array = NewType("Array", np.ndarray) | |
| class ModelOutput: | |
| vertices: Optional[Tensor] = None | |
| joints: Optional[Tensor] = None | |
| full_pose: Optional[Tensor] = None | |
| global_orient: Optional[Tensor] = None | |
| transl: Optional[Tensor] = None | |
| def __getitem__(self, key): | |
| return getattr(self, key) | |
| def get(self, key, default=None): | |
| return getattr(self, key, default) | |
| def __iter__(self): | |
| return self.keys() | |
| def keys(self): | |
| keys = [t.name for t in fields(self)] | |
| return iter(keys) | |
| def values(self): | |
| values = [getattr(self, t.name) for t in fields(self)] | |
| return iter(values) | |
| def items(self): | |
| data = [(t.name, getattr(self, t.name)) for t in fields(self)] | |
| return iter(data) | |
| class SMPLOutput(ModelOutput): | |
| betas: Optional[Tensor] = None | |
| body_pose: Optional[Tensor] = None | |
| class SMPLHOutput(SMPLOutput): | |
| left_hand_pose: Optional[Tensor] = None | |
| right_hand_pose: Optional[Tensor] = None | |
| transl: Optional[Tensor] = None | |
| class SMPLXOutput(SMPLHOutput): | |
| expression: Optional[Tensor] = None | |
| jaw_pose: Optional[Tensor] = None | |
| joint_transformation: Optional[Tensor] = None | |
| vertex_transformation: Optional[Tensor] = None | |
| class MANOOutput(ModelOutput): | |
| betas: Optional[Tensor] = None | |
| hand_pose: Optional[Tensor] = None | |
| class FLAMEOutput(ModelOutput): | |
| betas: Optional[Tensor] = None | |
| expression: Optional[Tensor] = None | |
| jaw_pose: Optional[Tensor] = None | |
| neck_pose: Optional[Tensor] = None | |
| def find_joint_kin_chain(joint_id, kinematic_tree): | |
| kin_chain = [] | |
| curr_idx = joint_id | |
| while curr_idx != -1: | |
| kin_chain.append(curr_idx) | |
| curr_idx = kinematic_tree[curr_idx] | |
| return kin_chain | |
| def to_tensor(array: Union[Array, Tensor], dtype=torch.float32) -> Tensor: | |
| if torch.is_tensor(array): | |
| return array | |
| else: | |
| return torch.tensor(array, dtype=dtype) | |
| class Struct(object): | |
| def __init__(self, **kwargs): | |
| for key, val in kwargs.items(): | |
| setattr(self, key, val) | |
| def to_np(array, dtype=np.float32): | |
| if "scipy.sparse" in str(type(array)): | |
| array = array.todense() | |
| return np.array(array, dtype=dtype) | |
| def rot_mat_to_euler(rot_mats): | |
| # Calculates rotation matrix to euler angles | |
| # Careful for extreme cases of eular angles like [0.0, pi, 0.0] | |
| sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) | |
| return torch.atan2(-rot_mats[:, 2, 0], sy) | |