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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,277 @@ | ||
| from collections import namedtuple | ||
|
|
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| import numpy as np | ||
| import torch | ||
| import torch.nn as nn | ||
|
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| from .lbs import lbs, hybrik, rotmat_to_quat, quat_to_rotmat | ||
|
|
||
| try: | ||
| import cPickle as pk | ||
| except ImportError: | ||
| import pickle as pk | ||
|
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|
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| ModelOutput = namedtuple('ModelOutput', | ||
| ['vertices', 'joints', 'joints_from_verts', | ||
| 'rot_mats']) | ||
| ModelOutput.__new__.__defaults__ = (None,) * len(ModelOutput._fields) | ||
|
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||
|
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| def to_tensor(array, dtype=torch.float32): | ||
| if 'torch.tensor' not in str(type(array)): | ||
| return torch.tensor(array, dtype=dtype) | ||
|
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||
|
|
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| class Struct(object): | ||
| def __init__(self, **kwargs): | ||
| for key, val in kwargs.items(): | ||
| setattr(self, key, val) | ||
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||
|
|
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| def to_np(array, dtype=np.float32): | ||
| if 'scipy.sparse' in str(type(array)): | ||
| array = array.todense() | ||
| return np.array(array, dtype=dtype) | ||
|
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||
|
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| class SMPL_layer(nn.Module): | ||
| NUM_JOINTS = 23 | ||
| NUM_BODY_JOINTS = 23 | ||
| NUM_BETAS = 10 | ||
| JOINT_NAMES = [ | ||
| 'pelvis', 'left_hip', 'right_hip', # 2 | ||
| 'spine1', 'left_knee', 'right_knee', # 5 | ||
| 'spine2', 'left_ankle', 'right_ankle', # 8 | ||
| 'spine3', 'left_foot', 'right_foot', # 11 | ||
| 'neck', 'left_collar', 'right_collar', # 14 | ||
| 'jaw', # 15 | ||
| 'left_shoulder', 'right_shoulder', # 17 | ||
| 'left_elbow', 'right_elbow', # 19 | ||
| 'left_wrist', 'right_wrist', # 21 | ||
| 'left_thumb', 'right_thumb', # 23 | ||
| 'head', 'left_middle', 'right_middle', # 26 | ||
| 'left_bigtoe', 'right_bigtoe' # 28 | ||
| ] | ||
| LEAF_NAMES = [ | ||
| 'head', 'left_middle', 'right_middle', 'left_bigtoe', 'right_bigtoe' | ||
| ] | ||
| root_idx_17 = 0 | ||
| root_idx_smpl = 0 | ||
|
|
||
| def __init__(self, | ||
| model_path, | ||
| h36m_jregressor, | ||
| gender='neutral', | ||
| dtype=torch.float32, | ||
| num_joints=29): | ||
| ''' SMPL model layers | ||
| Parameters: | ||
| ---------- | ||
| model_path: str | ||
| The path to the folder or to the file where the model | ||
| parameters are stored | ||
| gender: str, optional | ||
| Which gender to load | ||
| ''' | ||
| super(SMPL_layer, self).__init__() | ||
|
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||
| self.ROOT_IDX = self.JOINT_NAMES.index('pelvis') | ||
| self.LEAF_IDX = [self.JOINT_NAMES.index(name) for name in self.LEAF_NAMES] | ||
| self.SPINE3_IDX = 9 | ||
|
|
||
| with open(model_path, 'rb') as smpl_file: | ||
| self.smpl_data = Struct(**pk.load(smpl_file, encoding='latin1')) | ||
|
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| self.gender = gender | ||
|
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| self.dtype = dtype | ||
|
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| self.faces = self.smpl_data.f | ||
|
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| ''' Register Buffer ''' | ||
| # Faces | ||
| self.register_buffer('faces_tensor', | ||
| to_tensor(to_np(self.smpl_data.f, dtype=np.int64), dtype=torch.long)) | ||
|
|
||
| # The vertices of the template model, (6890, 3) | ||
| self.register_buffer('v_template', | ||
| to_tensor(to_np(self.smpl_data.v_template), dtype=dtype)) | ||
|
|
||
| # The shape components | ||
| # Shape blend shapes basis, (6890, 3, 10) | ||
| self.register_buffer( | ||
| 'shapedirs', | ||
| to_tensor(to_np(self.smpl_data.shapedirs), dtype=dtype)) | ||
|
|
||
| # Pose blend shape basis: 6890 x 3 x 23*9, reshaped to 6890*3 x 23*9 | ||
| num_pose_basis = self.smpl_data.posedirs.shape[-1] | ||
| # 23*9 x 6890*3 | ||
| posedirs = np.reshape(self.smpl_data.posedirs, [-1, num_pose_basis]).T | ||
| self.register_buffer('posedirs', | ||
| to_tensor(to_np(posedirs), dtype=dtype)) | ||
|
|
||
| # Vertices to Joints location (23 + 1, 6890) | ||
| self.register_buffer( | ||
| 'J_regressor', | ||
| to_tensor(to_np(self.smpl_data.J_regressor), dtype=dtype)) | ||
| # Vertices to Human3.6M Joints location (17, 6890) | ||
| self.register_buffer( | ||
| 'J_regressor_h36m', | ||
| to_tensor(to_np(h36m_jregressor), dtype=dtype)) | ||
|
|
||
| self.num_joints = num_joints | ||
|
|
||
| # indices of parents for each joints | ||
| parents = torch.zeros(len(self.JOINT_NAMES), dtype=torch.long) | ||
| parents[:(self.NUM_JOINTS + 1)] = to_tensor(to_np(self.smpl_data.kintree_table[0])).long() | ||
| parents[0] = -1 | ||
| # extend kinematic tree | ||
| parents[24] = 15 | ||
| parents[25] = 22 | ||
| parents[26] = 23 | ||
| parents[27] = 10 | ||
| parents[28] = 11 | ||
| if parents.shape[0] > self.num_joints: | ||
| parents = parents[:24] | ||
|
|
||
| self.register_buffer( | ||
| 'children_map', | ||
| self._parents_to_children(parents)) | ||
| # (24,) | ||
| self.register_buffer('parents', parents) | ||
|
|
||
| # (6890, 23 + 1) | ||
| self.register_buffer('lbs_weights', | ||
| to_tensor(to_np(self.smpl_data.weights), dtype=dtype)) | ||
|
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||
| def _parents_to_children(self, parents): | ||
| children = torch.ones_like(parents) * -1 | ||
| for i in range(self.num_joints): | ||
| if children[parents[i]] < 0: | ||
| children[parents[i]] = i | ||
| for i in self.LEAF_IDX: | ||
| if i < children.shape[0]: | ||
| children[i] = -1 | ||
|
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||
| children[self.SPINE3_IDX] = -3 | ||
| children[0] = 3 | ||
| children[self.SPINE3_IDX] = self.JOINT_NAMES.index('neck') | ||
|
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| return children | ||
|
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||
| def forward(self, | ||
| pose_axis_angle, | ||
| betas, | ||
| global_orient, | ||
| transl=None, | ||
| return_verts=True): | ||
| ''' Forward pass for the SMPL model | ||
| Parameters | ||
| ---------- | ||
| pose_axis_angle: torch.tensor, optional, shape Bx(J*3) | ||
| It should be a tensor that contains joint rotations in | ||
| axis-angle format. (default=None) | ||
| betas: torch.tensor, optional, shape Bx10 | ||
| It can used if shape parameters | ||
| `betas` are predicted from some external model. | ||
| (default=None) | ||
| global_orient: torch.tensor, optional, shape Bx3 | ||
| Global Orientations. | ||
| transl: torch.tensor, optional, shape Bx3 | ||
| Global Translations. | ||
| return_verts: bool, optional | ||
| Return the vertices. (default=True) | ||
| Returns | ||
| ------- | ||
| ''' | ||
| # batch_size = pose_axis_angle.shape[0] | ||
|
|
||
| # concate root orientation with thetas | ||
| if global_orient is not None: | ||
| full_pose = torch.cat([global_orient, pose_axis_angle], dim=1) | ||
| else: | ||
| full_pose = pose_axis_angle | ||
|
|
||
| # Translate thetas to rotation matrics | ||
| pose2rot = True | ||
| # vertices: (B, N, 3), joints: (B, K, 3) | ||
| vertices, joints, rot_mats, joints_from_verts_h36m = lbs(betas, full_pose, self.v_template, | ||
| self.shapedirs, self.posedirs, | ||
| self.J_regressor, self.J_regressor_h36m, self.parents, | ||
| self.lbs_weights, pose2rot=pose2rot, dtype=self.dtype) | ||
|
|
||
| if transl is not None: | ||
| # apply translations | ||
| joints += transl.unsqueeze(dim=1) | ||
| vertices += transl.unsqueeze(dim=1) | ||
| joints_from_verts_h36m += transl.unsqueeze(dim=1) | ||
| else: | ||
| vertices = vertices - joints_from_verts_h36m[:, self.root_idx_17, :].unsqueeze(1).detach() | ||
| joints = joints - joints[:, self.root_idx_smpl, :].unsqueeze(1).detach() | ||
| joints_from_verts_h36m = joints_from_verts_h36m - joints_from_verts_h36m[:, self.root_idx_17, :].unsqueeze(1).detach() | ||
|
|
||
| output = ModelOutput( | ||
| vertices=vertices, joints=joints, rot_mats=rot_mats, joints_from_verts=joints_from_verts_h36m) | ||
| return output | ||
|
|
||
| def hybrik(self, | ||
| pose_skeleton, | ||
| betas, | ||
| phis, | ||
| global_orient, | ||
| transl=None, | ||
| return_verts=True, | ||
| leaf_thetas=None): | ||
| ''' Inverse pass for the SMPL model | ||
| Parameters | ||
| ---------- | ||
| pose_skeleton: torch.tensor, optional, shape Bx(J*3) | ||
| It should be a tensor that contains joint locations in | ||
| (X, Y, Z) format. (default=None) | ||
| betas: torch.tensor, optional, shape Bx10 | ||
| It can used if shape parameters | ||
| `betas` are predicted from some external model. | ||
| (default=None) | ||
| global_orient: torch.tensor, optional, shape Bx3 | ||
| Global Orientations. | ||
| transl: torch.tensor, optional, shape Bx3 | ||
| Global Translations. | ||
| return_verts: bool, optional | ||
| Return the vertices. (default=True) | ||
| Returns | ||
| ------- | ||
| ''' | ||
| batch_size = pose_skeleton.shape[0] | ||
|
|
||
| if leaf_thetas is not None: | ||
| leaf_thetas = leaf_thetas.reshape(batch_size * 5, 4) | ||
| leaf_thetas = quat_to_rotmat(leaf_thetas) | ||
|
|
||
| vertices, new_joints, rot_mats, joints_from_verts = hybrik( | ||
| betas, global_orient, pose_skeleton, phis, | ||
| self.v_template, self.shapedirs, self.posedirs, | ||
| self.J_regressor, self.J_regressor_h36m, self.parents, self.children_map, | ||
| self.lbs_weights, dtype=self.dtype, train=self.training, | ||
| leaf_thetas=leaf_thetas) | ||
|
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| rot_mats = rot_mats.reshape(batch_size * 24, 3, 3) | ||
| rot_mats = rotmat_to_quat(rot_mats).reshape(batch_size, 24 * 4) | ||
|
|
||
| if transl is not None: | ||
| new_joints += transl.unsqueeze(dim=1) | ||
| vertices += transl.unsqueeze(dim=1) | ||
| joints_from_verts += transl.unsqueeze(dim=1) | ||
| else: | ||
| vertices = vertices - joints_from_verts[:, self.root_idx_17, :].unsqueeze(1).detach() | ||
| new_joints = new_joints - new_joints[:, self.root_idx_smpl, :].unsqueeze(1).detach() | ||
| joints_from_verts = joints_from_verts - joints_from_verts[:, self.root_idx_17, :].unsqueeze(1).detach() | ||
|
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||
| output = ModelOutput( | ||
| vertices=vertices, joints=new_joints, rot_mats=rot_mats, joints_from_verts=joints_from_verts) | ||
| return output |
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