44 lines
2.1 KiB
Python
44 lines
2.1 KiB
Python
import torch
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import numpy as np
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import pickle
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from typing import Optional
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from body_model import MANOLayer
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from smplx.lbs import vertices2joints
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from smplx.utils import MANOOutput, to_tensor
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from smplx.vertex_ids import vertex_ids
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class MANO(MANOLayer):
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def __init__(self, *args, joint_regressor_extra: Optional[str] = None, **kwargs):
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"""
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Extension of the official MANO implementation to support more joints.
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Args:
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Same as MANOLayer.
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joint_regressor_extra (str): Path to extra joint regressor.
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"""
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super(MANO, self).__init__(*args, **kwargs)
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mano_to_openpose = [0, 13, 14, 15, 16, 1, 2, 3, 17, 4, 5, 6, 18, 10, 11, 12, 19, 7, 8, 9, 20]
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#2, 3, 5, 4, 1
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if joint_regressor_extra is not None:
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self.register_buffer('joint_regressor_extra', torch.tensor(pickle.load(open(joint_regressor_extra, 'rb'), encoding='latin1'), dtype=torch.float32))
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self.register_buffer('extra_joints_idxs', to_tensor(list(vertex_ids['mano'].values()), dtype=torch.long))
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self.register_buffer('joint_map', torch.tensor(mano_to_openpose, dtype=torch.long))
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self.pose2rot = kwargs.get('pose2rot')
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# print([attr for attr in dir(self) if not callable(getattr(self, attr)) and not attr.startswith("__")])
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# print(self.hand_mean, self.flat_hand_mean, self.num_pca_comps, self.use_pca)
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# exit()
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def forward(self, *args, **kwargs) -> MANOOutput:
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"""
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Run forward pass. Same as MANO and also append an extra set of joints if joint_regressor_extra is specified.
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"""
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mano_output = super(MANO, self).forward(*args, **kwargs)
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extra_joints = torch.index_select(mano_output.vertices, 1, self.extra_joints_idxs)
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joints = torch.cat([mano_output.joints, extra_joints], dim=1)
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joints = joints[:, self.joint_map, :]
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if hasattr(self, 'joint_regressor_extra'):
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extra_joints = vertices2joints(self.joint_regressor_extra, mano_output.vertices)
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joints = torch.cat([joints, extra_joints], dim=1)
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mano_output.joints = joints
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return mano_output |