Spaces:
Running
Running
File size: 7,320 Bytes
27763e5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import os
from os.path import join as pjoin
import numpy as np
import copy
import torch
import torch.nn.functional as F
from utils.transforms import quat2repr6d, quat2euler, repr6d2quat
class TracksParser():
def __init__(self, tracks_json, scale=1.0, requires_contact=False, joint_reduction=False):
assert requires_contact==False, 'contact is not implemented for tracks data yet!!!'
self.tracks_json = tracks_json
self.scale = scale
self.requires_contact = requires_contact
self.joint_reduction = joint_reduction
self.skeleton_names = []
self.rotations = []
for i, track in enumerate(self.tracks_json):
# print(i, track['name'])
self.skeleton_names.append(track['name'])
if i == 0:
assert track['type'] == 'vector'
self.position = np.array(track['values']).reshape(-1, 3) * self.scale
self.num_frames = self.position.shape[0]
else:
assert track['type'] == 'quaternion' # DEAFULT: quaternion
rotation = np.array(track['values']).reshape(-1, 4)
if rotation.shape[0] == 0:
rotation = np.zeros((self.num_frames, 4))
elif rotation.shape[0] < self.num_frames:
rotation = np.repeat(rotation, self.num_frames // rotation.shape[0], axis=0)
elif rotation.shape[0] > self.num_frames:
rotation = rotation[:self.num_frames]
self.rotations += [rotation]
self.rotations = np.array(self.rotations, dtype=np.float32)
def to_tensor(self, repr='euler', rot_only=False):
if repr not in ['euler', 'quat', 'quaternion', 'repr6d']:
raise Exception('Unknown rotation representation')
rotations = self.get_rotation(repr=repr)
positions = self.get_position()
if rot_only:
return rotations.reshape(rotations.shape[0], -1)
if self.requires_contact:
virtual_contact = torch.zeros_like(rotations[:, :len(self.skeleton.contact_id)])
virtual_contact[..., 0] = self.contact_label
rotations = torch.cat([rotations, virtual_contact], dim=1)
rotations = rotations.reshape(rotations.shape[0], -1)
return torch.cat((rotations, positions), dim=-1)
def get_rotation(self, repr='quat'):
if repr == 'quaternion' or repr == 'quat' or repr == 'repr6d':
rotations = torch.tensor(self.rotations, dtype=torch.float).transpose(0, 1)
if repr == 'repr6d':
rotations = quat2repr6d(rotations)
if repr == 'euler':
rotations = quat2euler(rotations)
return rotations
def get_position(self):
return torch.tensor(self.position, dtype=torch.float32)
class TracksMotion:
def __init__(self, tracks_json, scale=1.0, repr='repr6d', padding=False,
use_velo=True, contact=False, keep_y_pos=True, joint_reduction=False):
self.scale = scale
self.tracks = TracksParser(tracks_json, scale, requires_contact=contact, joint_reduction=joint_reduction)
self.raw_motion = self.tracks.to_tensor(repr=repr)
self.extra = {
}
self.repr = repr
if repr == 'quat':
self.n_rot = 4
elif repr == 'repr6d':
self.n_rot = 6
elif repr == 'euler':
self.n_rot = 3
self.padding = padding
self.use_velo = use_velo
self.contact = contact
self.keep_y_pos = keep_y_pos
self.joint_reduction = joint_reduction
self.raw_motion = self.raw_motion.permute(1, 0).unsqueeze_(0) # Shape = (1, n_channel, n_frames)
self.extra['global_pos'] = self.raw_motion[:, -3:, :]
if padding:
self.n_pad = self.n_rot - 3 # pad position channels
paddings = torch.zeros_like(self.raw_motion[:, :self.n_pad])
self.raw_motion = torch.cat((self.raw_motion, paddings), dim=1)
else:
self.n_pad = 0
self.raw_motion = torch.cat((self.raw_motion[:, :-3-self.n_pad], self.raw_motion[:, -3-self.n_pad:]), dim=1)
if self.use_velo:
self.msk = [-3, -2, -1] if not keep_y_pos else [-3, -1]
self.raw_motion = self.pos2velo(self.raw_motion)
self.n_contact = len(self.tracks.skeleton.contact_id) if contact else 0
@property
def n_channels(self):
return self.raw_motion.shape[1]
def __len__(self):
return self.raw_motion.shape[-1]
def pos2velo(self, pos):
msk = [i - self.n_pad for i in self.msk]
velo = pos.detach().clone().to(pos.device)
velo[:, msk, 1:] = pos[:, msk, 1:] - pos[:, msk, :-1]
self.begin_pos = pos[:, msk, 0].clone()
velo[:, msk, 0] = pos[:, msk, 1]
return velo
def velo2pos(self, velo):
msk = [i - self.n_pad for i in self.msk]
pos = velo.detach().clone().to(velo.device)
pos[:, msk, 0] = self.begin_pos.to(velo.device)
pos[:, msk] = torch.cumsum(velo[:, msk], dim=-1)
return pos
def motion2pos(self, motion):
if not self.use_velo:
return motion
else:
self.velo2pos(motion.clone())
def sample(self, size=None, slerp=False, align_corners=False):
if size is None:
return {'motion': self.raw_motion, 'extra': self.extra}
else:
if slerp:
raise NotImplementedError('slerp is not not implemented yet!!!')
else:
motion = F.interpolate(self.raw_motion, size=size, mode='linear', align_corners=align_corners)
extra = {}
if 'global_pos' in self.extra.keys():
extra['global_pos'] = F.interpolate(self.extra['global_pos'], size=size, mode='linear', align_corners=align_corners)
return motion
# return {'motion': motion, 'extra': extra}
def parse(self, motion, keep_velo=False,):
"""
No batch support here!!!
:returns tracks_json
"""
motion = motion.clone()
if self.use_velo and not keep_velo:
motion = self.velo2pos(motion)
if self.n_pad:
motion = motion[:, :-self.n_pad]
if self.contact:
raise NotImplementedError('contact is not implemented yet!!!')
motion = motion.squeeze().permute(1, 0)
pos = motion[..., -3:] / self.scale
rot = motion[..., :-3].reshape(motion.shape[0], -1, self.n_rot)
if self.repr == 'repr6d':
rot = repr6d2quat(rot)
elif self.repr == 'euler':
raise NotImplementedError('parse "euler is not implemented yet!!!')
times = []
out_tracks_json = copy.deepcopy(self.tracks.tracks_json)
for i, _track in enumerate(out_tracks_json):
if i == 0:
times = [ j * out_tracks_json[i]['times'][1] for j in range(motion.shape[0])]
out_tracks_json[i]['values'] = pos.flatten().detach().cpu().numpy().tolist()
else:
out_tracks_json[i]['values'] = rot[:, i-1, :].flatten().detach().cpu().numpy().tolist()
out_tracks_json[i]['times'] = times
return out_tracks_json
|