Spaces:
Sleeping
Sleeping
File size: 10,990 Bytes
f561f8b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from configs import constants as _C
import torch
import numpy as np
from torch.nn import functional as F
from ...utils import transforms
__all__ = ['VideoAugmentor', 'SMPLAugmentor', 'SequenceAugmentor', 'CameraAugmentor']
num_joints = _C.KEYPOINTS.NUM_JOINTS
class VideoAugmentor():
def __init__(self, cfg, train=True):
self.train = train
self.l = cfg.DATASET.SEQLEN + 1
self.aug_dict = torch.load(_C.KEYPOINTS.COCO_AUG_DICT)
def get_jitter(self, ):
"""Guassian jitter modeling."""
jittering_noise = torch.normal(
mean=torch.zeros((self.l, num_joints, 3)),
std=self.aug_dict['jittering'].reshape(1, num_joints, 1).expand(self.l, -1, 3)
) * _C.KEYPOINTS.S_JITTERING
return jittering_noise
def get_lfhp(self, ):
"""Low-frequency high-peak noise modeling."""
def get_peak_noise_mask():
peak_noise_mask = torch.rand(self.l, num_joints).float() * self.aug_dict['pmask'].squeeze(0)
peak_noise_mask = peak_noise_mask < _C.KEYPOINTS.S_PEAK_MASK
return peak_noise_mask
peak_noise_mask = get_peak_noise_mask()
peak_noise = peak_noise_mask.float().unsqueeze(-1).repeat(1, 1, 3)
peak_noise = peak_noise * torch.randn(3) * self.aug_dict['peak'].reshape(1, -1, 1) * _C.KEYPOINTS.S_PEAK
return peak_noise
def get_bias(self, ):
"""Bias noise modeling."""
bias_noise = torch.normal(
mean=torch.zeros((num_joints, 3)), std=self.aug_dict['bias'].reshape(num_joints, 1)
).unsqueeze(0) * _C.KEYPOINTS.S_BIAS
return bias_noise
def get_mask(self, scale=None):
"""Mask modeling."""
if scale is None:
scale = _C.KEYPOINTS.S_MASK
# Per-frame and joint
mask = torch.rand(self.l, num_joints) < scale
visible = (~mask).clone()
for child in range(num_joints):
parent = _C.KEYPOINTS.TREE[child]
if parent == -1: continue
if isinstance(parent, list):
visible[:, child] *= (visible[:, parent[0]] * visible[:, parent[1]])
else:
visible[:, child] *= visible[:, parent]
mask = (~visible).clone()
return mask
def __call__(self, keypoints):
keypoints += self.get_bias() + self.get_jitter() + self.get_lfhp()
return keypoints
class SMPLAugmentor():
noise_scale = 1e-2
def __init__(self, cfg, augment=True):
self.n_frames = cfg.DATASET.SEQLEN
self.augment = augment
def __call__(self, target):
if not self.augment:
# Only add initial frame augmentation
if not 'init_pose' in target:
target['init_pose'] = target['pose'][:1] @ self.get_initial_pose_augmentation()
return target
n_frames = target['pose'].shape[0]
# Global rotation
rmat = self.get_global_augmentation()
target['pose'][:, 0] = rmat @ target['pose'][:, 0]
target['transl'] = (rmat.squeeze() @ target['transl'].T).T
# Shape
shape_noise = self.get_shape_augmentation(n_frames)
target['betas'] = target['betas'] + shape_noise
# Initial frames mis-prediction
target['init_pose'] = target['pose'][:1] @ self.get_initial_pose_augmentation()
return target
def get_global_augmentation(self, ):
"""Global coordinate augmentation. Random rotation around y-axis"""
angle_y = torch.rand(1) * 2 * np.pi * float(self.augment)
aa = torch.tensor([0.0, angle_y, 0.0]).float().unsqueeze(0)
rmat = transforms.axis_angle_to_matrix(aa)
return rmat
def get_shape_augmentation(self, n_frames):
"""Shape noise modeling."""
shape_noise = torch.normal(
mean=torch.zeros((1, 10)),
std=torch.ones((1, 10)) * 0.1 * float(self.augment)).expand(n_frames, 10)
return shape_noise
def get_initial_pose_augmentation(self, ):
"""Initial frame pose noise modeling. Random rotation around all joints."""
euler = torch.normal(
mean=torch.zeros((24, 3)),
std=torch.ones((24, 3))
) * self.noise_scale #* float(self.augment)
rmat = transforms.axis_angle_to_matrix(euler)
return rmat.unsqueeze(0)
class SequenceAugmentor:
"""Augment the play speed of the motion sequence"""
l_factor = 1.5
def __init__(self, l_default):
self.l_default = l_default
def __call__(self, target):
l = torch.randint(low=int(self.l_default / self.l_factor), high=int(self.l_default * self.l_factor), size=(1, ))
pose = transforms.matrix_to_rotation_6d(target['pose'])
resampled_pose = F.interpolate(
pose[:l].permute(1, 2, 0), self.l_default, mode='linear', align_corners=True
).permute(2, 0, 1)
resampled_pose = transforms.rotation_6d_to_matrix(resampled_pose)
transl = target['transl'].unsqueeze(1)
resampled_transl = F.interpolate(
transl[:l].permute(1, 2, 0), self.l_default, mode='linear', align_corners=True
).squeeze(0).T
target['pose'] = resampled_pose
target['transl'] = resampled_transl
target['betas'] = target['betas'][:self.l_default]
return target
class CameraAugmentor:
rx_factor = np.pi/8
ry_factor = np.pi/4
rz_factor = np.pi/8
pitch_std = np.pi/8
pitch_mean = np.pi/36
roll_std = np.pi/24
t_factor = 1
tz_scale = 10
tz_min = 2
motion_prob = 0.75
interp_noise = 0.2
def __init__(self, l, w, h, f):
self.l = l
self.w = w
self.h = h
self.f = f
self.fov_tol = 1.2 * (0.5 ** 0.5)
def __call__(self, target):
R, T = self.create_camera(target)
if np.random.rand() < self.motion_prob:
R = self.create_rotation_move(R)
T = self.create_translation_move(T)
return self.apply(target, R, T)
def create_camera(self, target):
"""Create the initial frame camera pose"""
yaw = np.random.rand() * 2 * np.pi
pitch = np.random.normal(scale=self.pitch_std) + self.pitch_mean
roll = np.random.normal(scale=self.roll_std)
yaw_rm = transforms.axis_angle_to_matrix(torch.tensor([[0, yaw, 0]]).float())
pitch_rm = transforms.axis_angle_to_matrix(torch.tensor([[pitch, 0, 0]]).float())
roll_rm = transforms.axis_angle_to_matrix(torch.tensor([[0, 0, roll]]).float())
R = (roll_rm @ pitch_rm @ yaw_rm)
# Place people in the scene
tz = np.random.rand() * self.tz_scale + self.tz_min
max_d = self.w * tz / self.f / 2
tx = np.random.normal(scale=0.25) * max_d
ty = np.random.normal(scale=0.25) * max_d
dist = torch.tensor([tx, ty, tz]).float()
T = dist - torch.matmul(R, target['transl'][0])
return R.repeat(self.l, 1, 1), T.repeat(self.l, 1)
def create_rotation_move(self, R):
"""Create rotational move for the camera"""
# Create final camera pose
rx = np.random.normal(scale=self.rx_factor)
ry = np.random.normal(scale=self.ry_factor)
rz = np.random.normal(scale=self.rz_factor)
Rf = R[0] @ transforms.axis_angle_to_matrix(torch.tensor([rx, ry, rz]).float())
# Inbetweening two poses
Rs = torch.stack((R[0], Rf))
rs = transforms.matrix_to_rotation_6d(Rs).numpy()
rs_move = self.noisy_interpolation(rs)
R_move = transforms.rotation_6d_to_matrix(torch.from_numpy(rs_move).float())
return R_move
def create_translation_move(self, T):
"""Create translational move for the camera"""
# Create final camera position
tx = np.random.normal(scale=self.t_factor)
ty = np.random.normal(scale=self.t_factor)
tz = np.random.normal(scale=self.t_factor)
Ts = np.array([[0, 0, 0], [tx, ty, tz]])
T_move = self.noisy_interpolation(Ts)
T_move = torch.from_numpy(T_move).float()
return T_move + T
def noisy_interpolation(self, data):
"""Non-linear interpolation with noise"""
dim = data.shape[-1]
output = np.zeros((self.l, dim))
linspace = np.stack([np.linspace(0, 1, self.l) for _ in range(dim)])
noise = (linspace[0, 1] - linspace[0, 0]) * self.interp_noise
space_noise = np.stack([np.random.uniform(-noise, noise, self.l - 2) for _ in range(dim)])
linspace[:, 1:-1] = linspace[:, 1:-1] + space_noise
for i in range(dim):
output[:, i] = np.interp(linspace[i], np.array([0., 1.,]), data[:, i])
return output
def apply(self, target, R, T):
target['R'] = R
target['T'] = T
# Recompute the translation
transl_cam = torch.matmul(R, target['transl'].unsqueeze(-1)).squeeze(-1)
transl_cam = transl_cam + T
if transl_cam[..., 2].min() < 0.5: # If the person is too close to the camera
transl_cam[..., 2] = transl_cam[..., 2] + (1.0 - transl_cam[..., 2].min())
# If the subject is away from the field of view, put the camera behind
fov = torch.div(transl_cam[..., :2], transl_cam[..., 2:]).abs()
if fov.max() > self.fov_tol:
t_max = transl_cam[fov.max(1)[0].max(0)[1].item()]
z_trg = t_max[:2].abs().max(0)[0] / self.fov_tol
pad = z_trg - t_max[2]
transl_cam[..., 2] = transl_cam[..., 2] + pad
target['transl_cam'] = transl_cam
# Transform world coordinate to camera coordinate
target['pose_root'] = target['pose'][:, 0].clone()
target['pose'][:, 0] = R @ target['pose'][:, 0] # pose
target['init_pose'][:, 0] = R[:1] @ target['init_pose'][:, 0] # init pose
# Compute angular velocity
cam_angvel = transforms.matrix_to_rotation_6d(R[:-1] @ R[1:].transpose(-1, -2))
cam_angvel = cam_angvel - torch.tensor([[1, 0, 0, 0, 1, 0]]).to(cam_angvel) # Normalize
target['cam_angvel'] = cam_angvel * 3e1 # assume 30-fps
if 'kp3d' in target:
target['kp3d'] = torch.matmul(R, target['kp3d'].transpose(1, 2)).transpose(1, 2) + target['transl_cam'].unsqueeze(1)
return target |