from dataclasses import dataclass import numpy as np from numpy import ndarray import os from typing import Union, List, Tuple from .exporter import Exporter from ..tokenizer.spec import DetokenzeOutput from .order import Order @dataclass(frozen=True) class RawData(Exporter): ''' Dataclass to handle data from processed model files. ''' # vertices of the mesh, shape (N, 3), float32 vertices: Union[ndarray, None] # normals of vertices, shape (N, 3), float32 vertex_normals: Union[ndarray, None] # faces of mesh, shape (F, 3), face id starts from 0 to F-1, int64 faces: Union[ndarray, None] # face normal of mesh, shape (F, 3), float32 face_normals: Union[ndarray, None] # joints of bones, shape (J, 3), float32 joints: Union[ndarray, None] # tails of joints, shape (J, 3), float32 tails: Union[ndarray, None] # skinning of joints, shape (N, J), float32 skin: Union[ndarray, None] # whether the joint has skin, bool no_skin: Union[ndarray, None] # parents of joints, None represents no parent(a root joint) # make sure parent[k] < k parents: Union[List[Union[int, None]], None] # names of joints names: Union[List[str], None] # local coordinate matrix_local: Union[ndarray, None] # path to data path: Union[str, None]=None # data cls cls: Union[str, None]=None @staticmethod def load(path: str) -> 'RawData': data = np.load(path, allow_pickle=True) d = {name: data[name][()] for name in data} d['path'] = path return RawData(**d) def save(self, path: str): os.makedirs(os.path.dirname(path), exist_ok=True) np.savez(file=path, **self.__dict__) @property def N(self): ''' number of vertices ''' return self.vertices.shape[0] @property def F(self): ''' number of faces ''' return self.faces.shape[0] @property def J(self): ''' number of joints ''' return self.joints.shape[0] def check(self): if self.names is not None and self.joints is not None: assert len(self.names) == self.J if self.names is not None and self.parents is not None: assert len(self.names) == len(self.parents) if self.parents is not None: for (i, pid) in enumerate(self.parents): if i==0: assert pid is None else: assert pid is not None assert pid < i def export_pc(self, path: str, with_normal: bool=True, normal_size=0.01): ''' export point cloud ''' if with_normal: self._export_pc(vertices=self.vertices, path=path, vertex_normals=self.vertex_normals, normal_size=normal_size) else: self._export_pc(vertices=self.vertices, path=path, vertex_normals=None, normal_size=normal_size) def export_mesh(self, path: str): ''' export mesh ''' self._export_mesh(vertices=self.vertices, faces=self.faces, path=path) def export_skeleton(self, path: str): ''' export spring ''' self._export_skeleton(joints=self.joints, parents=self.parents, path=path) def export_skeleton_sequence(self, path: str): ''' export spring ''' self._export_skeleton_sequence(joints=self.joints, parents=self.parents, path=path) def export_fbx( self, path: str, extrude_size: float=0.03, group_per_vertex: int=-1, add_root: bool=False, do_not_normalize: bool=False, use_extrude_bone: bool=True, use_connect_unique_child: bool=True, extrude_from_parent: bool=True, use_tail: bool=False, custom_vertex_group: Union[ndarray, None]=None, ): ''' export the whole model with skining ''' self._export_fbx( path=path, vertices=self.vertices, joints=self.joints, skin=self.skin if custom_vertex_group is None else custom_vertex_group, parents=self.parents, names=self.names, faces=self.faces, extrude_size=extrude_size, group_per_vertex=group_per_vertex, add_root=add_root, do_not_normalize=do_not_normalize, use_extrude_bone=use_extrude_bone, use_connect_unique_child=use_connect_unique_child, extrude_from_parent=extrude_from_parent, tails=self.tails if use_tail else None, ) def export_render(self, path: str, resolution: Tuple[int, int]=[256, 256]): self._export_render( path=path, vertices=self.vertices, faces=self.faces, bones=np.concatenate([self.joints, self.tails], axis=-1), resolution=resolution, ) @dataclass(frozen=True) class RawSkeleton(Exporter): ''' Dataclass to handle skeleton from AR. ''' # joints of bones, shape (J, 3), float32 joints: Union[ndarray, None] # tails of joints, shape (J, 3), float32 tails: Union[ndarray, None] # whether the joint has skin, bool no_skin: Union[ndarray, None] # parents of joints, None represents no parent(a root joint) # make sure parent[k] < k parents: Union[List[Union[int, None]], None] # names of joints names: Union[List[str], None] @staticmethod def load(path: str) -> 'RawSkeleton': data = np.load(path, allow_pickle=True) return RawSkeleton(**{name: data[name][()] for name in data}) def save(self, path: str): os.makedirs(os.path.dirname(path), exist_ok=True) np.savez(file=path, **self.__dict__) @staticmethod def from_detokenize_output(res: DetokenzeOutput, order: Union[Order, None]) -> 'RawSkeleton': J = len(res.bones) names = order.make_names(cls=res.cls, parts=res.parts, num_bones=J) joints = res.joints p_joints = res.p_joints parents = [] for (i, joint) in enumerate(joints): if i == 0: parents.append(None) continue p_joint = p_joints[i] dis = 999999 pid = None for j in reversed(range(i)): n_dis = ((joints[j] - p_joint)**2).sum() if n_dis < dis: pid = j dis = n_dis parents.append(pid) return RawSkeleton( joints=joints, tails=res.tails, no_skin=res.no_skin, parents=parents, names=names, ) def export_skeleton(self, path: str): ''' export spring ''' self._export_skeleton(joints=self.joints, parents=self.parents, path=path) def export_skeleton_sequence(self, path: str): ''' export spring ''' self._export_skeleton_sequence(joints=self.joints, parents=self.parents, path=path) def export_fbx( self, path: str, extrude_size: float=0.03, group_per_vertex: int=-1, add_root: bool=False, do_not_normalize: bool=False, use_extrude_bone: bool=True, use_connect_unique_child: bool=True, extrude_from_parent: bool=True, use_tail: bool=False, ): ''' export the whole model with skining ''' self._export_fbx( path=path, vertices=None, joints=self.joints, skin=None, parents=self.parents, names=self.names, faces=None, extrude_size=extrude_size, group_per_vertex=group_per_vertex, add_root=add_root, do_not_normalize=do_not_normalize, use_extrude_bone=use_extrude_bone, use_connect_unique_child=use_connect_unique_child, extrude_from_parent=extrude_from_parent, tails=self.tails if use_tail else None, ) def export_render(self, path: str, resolution: Tuple[int, int]=[256, 256]): self._export_render( path=path, vertices=None, faces=None, bones=np.concatenate([self.joints, self.tails], axis=-1), resolution=resolution, ) @dataclass class RawSkin(Exporter): ''' Dataclass to handle skeleton from AR. ''' # skin, shape (J, N) skin: ndarray # always sampled, shape (N, 3) vertices: Union[ndarray, None]=None # for future use, shape (J, 3) joints: Union[ndarray, None]=None @staticmethod def load(path: str) -> 'RawSkin': data = np.load(path, allow_pickle=True) return RawSkin(**{name: data[name][()] for name in data}) def save(self, path: str): os.makedirs(os.path.dirname(path), exist_ok=True) np.savez(file=path, **self.__dict__)