Spaces:
Sleeping
Sleeping
File size: 7,570 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 |
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
import joblib
import numpy as np
from .._dataset import BaseDataset
from ..utils.augmentor import *
from ...utils import data_utils as d_utils
from ...utils import transforms
from ...models import build_body_model
from ...utils.kp_utils import convert_kps, root_centering
class Dataset3D(BaseDataset):
def __init__(self, cfg, fname, training):
super(Dataset3D, self).__init__(cfg, training)
self.epoch = 0
self.labels = joblib.load(fname)
self.n_frames = cfg.DATASET.SEQLEN + 1
if self.training:
self.prepare_video_batch()
self.smpl = build_body_model('cpu', self.n_frames)
self.SMPLAugmentor = SMPLAugmentor(cfg, False)
self.VideoAugmentor = VideoAugmentor(cfg)
def __getitem__(self, index):
return self.get_single_sequence(index)
def get_inputs(self, index, target, vis_thr=0.6):
start_index, end_index = self.video_indices[index]
# 2D keypoints detection
kp2d = self.labels['kp2d'][start_index:end_index+1][..., :2].clone()
bbox = self.labels['bbox'][start_index:end_index+1][..., [0, 1, -1]].clone()
bbox[:, 2] = bbox[:, 2] / 200
kp2d, bbox = self.keypoints_normalizer(kp2d, target['res'], self.cam_intrinsics, 224, 224, bbox)
target['bbox'] = bbox[1:]
target['kp2d'] = kp2d
target['mask'] = self.labels['kp2d'][start_index+1:end_index+1][..., -1] < vis_thr
# Image features
target['features'] = self.labels['features'][start_index+1:end_index+1].clone()
return target
def get_labels(self, index, target):
start_index, end_index = self.video_indices[index]
# SMPL parameters
# NOTE: We use NeuralAnnot labels for Human36m and MPII3D only for the 0th frame input.
# We do not supervise the network on SMPL parameters.
target['pose'] = transforms.axis_angle_to_matrix(
self.labels['pose'][start_index:end_index+1].clone().reshape(-1, 24, 3))
target['betas'] = self.labels['betas'][start_index:end_index+1].clone() # No t
# Apply SMPL augmentor (y-axis rotation and initial frame noise)
target = self.SMPLAugmentor(target)
# 3D and 2D keypoints
if self.__name__ == 'ThreeDPW': # 3DPW has SMPL labels
gt_kp3d = self.labels['joints3D'][start_index:end_index+1].clone()
gt_kp2d = self.labels['joints2D'][start_index+1:end_index+1, ..., :2].clone()
gt_kp3d = root_centering(gt_kp3d.clone())
else: # Human36m and MPII do not have SMPL labels
gt_kp3d = torch.zeros((self.n_frames, self.n_joints + 14, 3))
gt_kp3d[:, self.n_joints:] = convert_kps(self.labels['joints3D'][start_index:end_index+1], 'spin', 'common')
gt_kp2d = torch.zeros((self.n_frames - 1, self.n_joints + 14, 2))
gt_kp2d[:, self.n_joints:] = convert_kps(self.labels['joints2D'][start_index+1:end_index+1, ..., :2], 'spin', 'common')
conf = self.mask.repeat(self.n_frames, 1).unsqueeze(-1)
gt_kp2d = torch.cat((gt_kp2d, conf[1:]), dim=-1)
gt_kp3d = torch.cat((gt_kp3d, conf), dim=-1)
target['kp3d'] = gt_kp3d
target['full_kp2d'] = gt_kp2d
target['weak_kp2d'] = torch.zeros_like(gt_kp2d)
if self.__name__ != 'ThreeDPW': # 3DPW does not contain world-coordinate motion
# Foot ground contact labels for Human36M and MPII3D
target['contact'] = self.labels['stationaries'][start_index+1:end_index+1].clone()
else:
# No foot ground contact label available for 3DPW
target['contact'] = torch.ones((self.n_frames - 1, 4)) * (-1)
if self.has_verts:
# SMPL vertices available for 3DPW
with torch.no_grad():
start_index, end_index = self.video_indices[index]
gender = self.labels['gender'][start_index].item()
output = self.smpl_gender[gender](
body_pose=target['pose'][1:, 1:],
global_orient=target['pose'][1:, :1],
betas=target['betas'][1:],
pose2rot=False,
)
target['verts'] = output.vertices.clone()
else:
# No SMPL vertices available
target['verts'] = torch.zeros((self.n_frames - 1, 6890, 3)).float()
return target
def get_init_frame(self, target):
# Prepare initial frame
output = self.smpl.get_output(
body_pose=target['init_pose'][:, 1:],
global_orient=target['init_pose'][:, :1],
betas=target['betas'][:1],
pose2rot=False
)
target['init_kp3d'] = root_centering(output.joints[:1, :self.n_joints]).reshape(1, -1)
return target
def get_camera_info(self, index, target):
start_index, end_index = self.video_indices[index]
# Intrinsics
target['res'] = self.labels['res'][start_index:end_index+1][0].clone()
self.get_naive_intrinsics(target['res'])
target['cam_intrinsics'] = self.cam_intrinsics.clone()
# Extrinsics pose
R = self.labels['cam_poses'][start_index:end_index+1, :3, :3].clone().float()
yaw = transforms.axis_angle_to_matrix(torch.tensor([[0, 2 * np.pi * np.random.uniform(), 0]])).float()
if self.__name__ == 'Human36M':
# Map Z-up to Y-down coordinate
zup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[-np.pi/2, 0, 0]])).float()
zup2ydown = torch.matmul(yaw, zup2ydown)
R = torch.matmul(R, zup2ydown)
elif self.__name__ == 'MPII3D':
# Map Y-up to Y-down coordinate
yup2ydown = transforms.axis_angle_to_matrix(torch.tensor([[np.pi, 0, 0]])).float()
yup2ydown = torch.matmul(yaw, yup2ydown)
R = torch.matmul(R, yup2ydown)
return target
def get_single_sequence(self, index):
# Universal target
target = {'has_full_screen': torch.tensor(True),
'has_smpl': torch.tensor(self.has_smpl),
'has_traj': torch.tensor(self.has_traj),
'has_verts': torch.tensor(self.has_verts),
'transl': torch.zeros((self.n_frames, 3)),
# Null camera motion
'R': torch.eye(3).repeat(self.n_frames, 1, 1),
'cam_angvel': torch.zeros((self.n_frames - 1, 6)),
# Null root orientation and velocity
'pose_root': torch.zeros((self.n_frames, 6)),
'vel_root': torch.zeros((self.n_frames - 1, 3)),
'init_root': torch.zeros((1, 6)),
}
self.get_camera_info(index, target)
self.get_inputs(index, target)
self.get_labels(index, target)
self.get_init_frame(target)
target = d_utils.prepare_keypoints_data(target)
target = d_utils.prepare_smpl_data(target)
return target |