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Running
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Zero
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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__) |