Spaces:
Starting
on
L40S
Starting
on
L40S
File size: 17,829 Bytes
d7e58f0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 |
import os
import os.path
import pickle
from collections import OrderedDict
from typing import List, Optional, Union
import numpy as np
import torch
from detrsmpl.core.conventions.keypoints_mapping import (
get_keypoint_idx,
get_keypoint_idxs_by_part,
)
from detrsmpl.core.evaluation import fg_vertices_to_mesh_distance
from detrsmpl.utils.transforms import aa_to_rotmat
from .builder import DATASETS
from .human_image_dataset import HumanImageDataset
@DATASETS.register_module()
class HumanImageSMPLXDataset(HumanImageDataset):
# metric
ALLOWED_METRICS = {
'mpjpe', 'pa-mpjpe', 'pve', '3dpck', 'pa-3dpck', '3dauc', 'pa-3dauc',
'3DRMSE', 'pa-pve'
}
def __init__(
self,
data_prefix: str,
pipeline: list,
dataset_name: str,
body_model: Optional[Union[dict, None]] = None,
ann_file: Optional[Union[str, None]] = None,
convention: Optional[str] = 'human_data',
cache_data_path: Optional[Union[str, None]] = None,
test_mode: Optional[bool] = False,
num_betas: Optional[int] = 10,
num_expression: Optional[int] = 10,
face_vertex_ids_path: Optional[str] = None,
hand_vertex_ids_path: Optional[str] = None,
):
super().__init__(data_prefix, pipeline, dataset_name, body_model,
ann_file, convention, cache_data_path, test_mode)
self.num_betas = num_betas
self.num_expression = num_expression
if face_vertex_ids_path is not None:
if os.path.exists(face_vertex_ids_path):
self.face_vertex_ids = np.load(face_vertex_ids_path).astype(
np.int32)
if hand_vertex_ids_path is not None:
if os.path.exists(hand_vertex_ids_path):
with open(hand_vertex_ids_path, 'rb') as f:
vertex_idxs_data = pickle.load(f, encoding='latin1')
self.left_hand_vertex_ids = vertex_idxs_data['left_hand']
self.right_hand_vertex_ids = vertex_idxs_data['right_hand']
def prepare_raw_data(self, idx: int):
"""Get item from self.human_data."""
info = super().prepare_raw_data(idx)
if self.cache_reader is not None:
self.human_data = self.cache_reader.get_item(idx)
idx = idx % self.cache_reader.slice_size
if 'smplx' in self.human_data:
smplx_dict = self.human_data['smplx']
info['has_smplx'] = 1
else:
smplx_dict = {}
info['has_smplx'] = 0
if 'global_orient' in smplx_dict:
info['smplx_global_orient'] = smplx_dict['global_orient'][idx]
info['has_smplx_global_orient'] = 1
else:
info['smplx_global_orient'] = np.zeros((3), dtype=np.float32)
info['has_smplx_global_orient'] = 0
if 'body_pose' in smplx_dict:
info['smplx_body_pose'] = smplx_dict['body_pose'][idx]
info['has_smplx_body_pose'] = 1
else:
info['smplx_body_pose'] = np.zeros((21, 3), dtype=np.float32)
info['has_smplx_body_pose'] = 0
if 'right_hand_pose' in smplx_dict:
info['smplx_right_hand_pose'] = smplx_dict['right_hand_pose'][idx]
info['has_smplx_right_hand_pose'] = 1
else:
info['smplx_right_hand_pose'] = np.zeros((15, 3), dtype=np.float32)
info['has_smplx_right_hand_pose'] = 0
if 'left_hand_pose' in smplx_dict:
info['smplx_left_hand_pose'] = smplx_dict['left_hand_pose'][idx]
info['has_smplx_left_hand_pose'] = 1
else:
info['smplx_left_hand_pose'] = np.zeros((15, 3), dtype=np.float32)
info['has_smplx_left_hand_pose'] = 0
if 'jaw_pose' in smplx_dict:
info['smplx_jaw_pose'] = smplx_dict['jaw_pose'][idx]
info['has_smplx_jaw_pose'] = 1
else:
info['smplx_jaw_pose'] = np.zeros((3), dtype=np.float32)
info['has_smplx_jaw_pose'] = 0
if 'betas' in smplx_dict:
info['smplx_betas'] = smplx_dict['betas'][idx]
info['has_smplx_betas'] = 1
else:
info['smplx_betas'] = np.zeros((self.num_betas), dtype=np.float32)
info['has_smplx_betas'] = 0
if 'expression' in smplx_dict:
info['smplx_expression'] = smplx_dict['expression'][idx]
info['has_smplx_expression'] = 1
else:
info['smplx_expression'] = np.zeros((self.num_expression),
dtype=np.float32)
info['has_smplx_expression'] = 0
return info
def _parse_result(self, res, mode='keypoint', body_part=''):
if mode == 'vertice':
# pred
pred_vertices = res['vertices'] * 1000.
# gt
if 'vertices' in self.human_data: # stirling or ehf
gt_vertices = self.human_data['vertices'].copy()
if self.dataset_name == 'EHF':
gt_vertices = gt_vertices * 1000.
else:
gt_param_dict = self.human_data['smplx'].copy()
for key, value in gt_param_dict.items():
new_value = torch.FloatTensor(value)
if ('pose' in key or key
== 'global_orient') and value.shape[-2] != 3:
new_value = aa_to_rotmat(new_value)
gt_param_dict[key] = new_value
gt_output = self.body_model(**gt_param_dict)
gt_vertices = gt_output['vertices'].detach().cpu().numpy(
) * 1000.
if body_part == 'right_hand':
pred_vertices = pred_vertices[:, self.right_hand_vertex_ids]
gt_vertices = gt_vertices[:, self.right_hand_vertex_ids]
elif body_part == 'left_hand':
pred_vertices = pred_vertices[:, self.left_hand_vertex_ids]
gt_vertices = gt_vertices[:, self.left_hand_vertex_ids]
elif body_part == 'face':
pred_vertices = pred_vertices[:, self.face_vertex_ids]
gt_vertices = gt_vertices[:, self.face_vertex_ids]
gt_mask = np.ones(gt_vertices.shape[:-1])
assert len(pred_vertices) == self.num_data
return pred_vertices, gt_vertices, gt_mask
elif mode == 'keypoint':
pred_keypoints3d = res['keypoints']
assert len(pred_keypoints3d) == self.num_data
if self.dataset_name in {'pw3d', '3DPW', '3dpw'}:
betas = []
body_pose = []
global_orient = []
gender = []
smpl_dict = self.human_data['smpl']
for idx in range(self.num_data):
betas.append(smpl_dict['betas'][idx])
body_pose.append(smpl_dict['body_pose'][idx])
global_orient.append(smpl_dict['global_orient'][idx])
if self.human_data['meta']['gender'][idx] == 'm':
gender.append(0)
else:
gender.append(1)
betas = torch.FloatTensor(betas)
body_pose = torch.FloatTensor(body_pose).view(-1, 69)
global_orient = torch.FloatTensor(global_orient)
gender = torch.Tensor(gender)
gt_output = self.body_model(betas=betas,
body_pose=body_pose,
global_orient=global_orient,
gender=gender)
gt_keypoints3d = gt_output['joints'].detach().cpu().numpy()
gt_keypoints3d_mask = np.ones(
(len(pred_keypoints3d), gt_keypoints3d.shape[1]))
elif self.dataset_name == 'EHF':
gt_vertices = self.human_data['vertices'].copy()
if body_part == 'J14':
gt_keypoints3d = torch.einsum('bik,ji->bjk', [
torch.from_numpy(gt_vertices).float(),
self.body_model.joints_regressor
]).numpy()
pred_vertices = res['vertices']
pred_keypoints3d = torch.einsum('bik,ji->bjk', [
torch.from_numpy(pred_vertices).float(),
self.body_model.joints_regressor
]).numpy()
gt_keypoints3d_mask = np.ones(
(len(pred_keypoints3d), gt_keypoints3d.shape[1]))
else:
gt_keypoints3d = torch.einsum('bik,ji->bjk', [
torch.from_numpy(gt_vertices).float(),
self.body_model.J_regressor
]).numpy()
extra_joints_idxs = np.array([
9120, 9929, 9448, 616, 6, 5770, 5780, 8846, 8463, 8474,
8635, 5361, 4933, 5058, 5169, 5286, 8079, 7669, 7794,
7905, 8022
])
gt_keypoints3d = np.concatenate(
(gt_keypoints3d, gt_vertices[:, extra_joints_idxs]),
axis=1)
pred_vertices = res['vertices']
pred_keypoints3d = torch.einsum('bik,ji->bjk', [
torch.from_numpy(pred_vertices).float(),
self.body_model.J_regressor
]).numpy()
pred_keypoints3d = np.concatenate(
(pred_keypoints3d, pred_vertices[:,
extra_joints_idxs]),
axis=1)
idxs = list(range(0, gt_keypoints3d.shape[1]))
if body_part == 'right_hand':
idxs = get_keypoint_idxs_by_part(
'right_hand', self.convention)
idxs.append(
get_keypoint_idx('right_wrist', self.convention))
elif body_part == 'left_hand':
idxs = get_keypoint_idxs_by_part(
'left_hand', self.convention)
idxs.append(
get_keypoint_idx('left_wrist', self.convention))
elif body_part == 'body':
idxs = get_keypoint_idxs_by_part(
'body', self.convention)
gt_keypoints3d = gt_keypoints3d[:, idxs]
pred_keypoints3d = pred_keypoints3d[:, idxs]
gt_keypoints3d_mask = np.ones(
(len(pred_keypoints3d), gt_keypoints3d.shape[1]))
else:
gt_keypoints3d = self.human_data['keypoints3d'][:, :, :3]
gt_keypoints3d_mask = np.ones(
(len(pred_keypoints3d), gt_keypoints3d.shape[1]))
if gt_keypoints3d.shape[1] == 17:
# SMPLX_to_J14
assert pred_keypoints3d.shape[1] == 14
H36M_TO_J17 = [
6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10, 0, 7, 9
]
H36M_TO_J14 = H36M_TO_J17[:14]
joint_mapper = H36M_TO_J14
gt_keypoints3d = gt_keypoints3d[:, joint_mapper, :]
pred_pelvis = pred_keypoints3d[:,
[2, 3], :].mean(axis=1,
keepdims=True)
gt_pelvis = gt_keypoints3d[:, [2, 3], :].mean(axis=1,
keepdims=True)
gt_keypoints3d_mask = gt_keypoints3d_mask[:, joint_mapper]
pred_keypoints3d = pred_keypoints3d - pred_pelvis
gt_keypoints3d = gt_keypoints3d - gt_pelvis
elif gt_keypoints3d.shape[1] == 14:
assert pred_keypoints3d.shape[1] == 14
pred_pelvis = pred_keypoints3d[:,
[2, 3], :].mean(axis=1,
keepdims=True)
gt_pelvis = gt_keypoints3d[:, [2, 3], :].mean(axis=1,
keepdims=True)
pred_keypoints3d = pred_keypoints3d - pred_pelvis
gt_keypoints3d = gt_keypoints3d - gt_pelvis
elif gt_keypoints3d.shape[1] == 21:
pred_pelvis = pred_keypoints3d[:, :1, :]
gt_pelvis = gt_keypoints3d[:, :1, :]
pred_keypoints3d = pred_keypoints3d - pred_pelvis
gt_keypoints3d = gt_keypoints3d - gt_pelvis
else:
pass
pred_keypoints3d = pred_keypoints3d * 1000
if self.dataset_name != 'stirling':
gt_keypoints3d = gt_keypoints3d * 1000
gt_keypoints3d_mask = gt_keypoints3d_mask > 0
return pred_keypoints3d, gt_keypoints3d, gt_keypoints3d_mask
def _report_3d_rmse(self, res_file):
"""compute the 3DRMSE between a predicted 3D face shape and the 3D
ground truth scan."""
pred_vertices, gt_vertices, _ = self._parse_result(res_file,
mode='vertice')
pred_keypoints3d, gt_keypoints3d, _ = self._parse_result(
res_file, mode='keypoint')
errors = []
for pred_vertice, gt_vertice, pred_points, gt_points in zip(
pred_vertices, gt_vertices, pred_keypoints3d, gt_keypoints3d):
error = fg_vertices_to_mesh_distance(gt_vertice, gt_points,
pred_vertice,
self.body_model.faces,
pred_points)
errors.append(error)
error = np.array(errors).mean()
name_value_tuples = [('3DRMSE', error)]
return name_value_tuples
def evaluate(self,
outputs: list,
res_folder: str,
metric: Optional[Union[str, List[str]]] = 'pa-mpjpe',
**kwargs: dict):
"""Evaluate 3D keypoint results.
Args:
outputs (list): results from model inference.
res_folder (str): path to store results.
metric (Optional[Union[str, List(str)]]):
the type of metric. Default: 'pa-mpjpe'
kwargs (dict): other arguments.
Returns:
dict:
A dict of all evaluation results.
"""
metrics = metric if isinstance(metric, list) else [metric]
for metric in metrics:
if metric not in self.ALLOWED_METRICS:
raise KeyError(f'metric {metric} is not supported')
# for keeping correctness during multi-gpu test, we sort all results
res_dict = {}
for out in outputs:
target_id = out['image_idx']
batch_size = len(out['keypoints_3d'])
for i in range(batch_size):
res_dict[int(target_id[i])] = dict(
keypoints=out['keypoints_3d'][i],
vertices=out['vertices'][i],
)
keypoints, vertices = [], []
for i in range(self.num_data):
keypoints.append(res_dict[i]['keypoints'])
vertices.append(res_dict[i]['vertices'])
keypoints = np.stack(keypoints)
vertices = np.stack(vertices)
res = dict(keypoints=keypoints, vertices=vertices)
name_value_tuples = []
for index, _metric in enumerate(metrics):
if 'body_part' in kwargs:
body_parts = kwargs['body_part'][index]
for body_part in body_parts:
if _metric == 'pa-mpjpe':
_nv_tuples = self._report_mpjpe(res,
metric='pa-mpjpe',
body_part=body_part)
elif _metric == 'pa-pve':
_nv_tuples = self._report_pve(res,
metric='pa-pve',
body_part=body_part)
else:
raise NotImplementedError
name_value_tuples.extend(_nv_tuples)
else:
if _metric == 'mpjpe':
_nv_tuples = self._report_mpjpe(res)
elif _metric == 'pa-mpjpe':
_nv_tuples = self._report_mpjpe(res, metric='pa-mpjpe')
elif _metric == '3dpck':
_nv_tuples = self._report_3d_pck(res)
elif _metric == 'pa-3dpck':
_nv_tuples = self._report_3d_pck(res, metric='pa-3dpck')
elif _metric == '3dauc':
_nv_tuples = self._report_3d_auc(res)
elif _metric == 'pa-3dauc':
_nv_tuples = self._report_3d_auc(res, metric='pa-3dauc')
elif _metric == 'pve':
_nv_tuples = self._report_pve(res)
elif _metric == 'pa-pve':
_nv_tuples = self._report_pve(res, metric='pa-pve')
elif _metric == '3DRMSE':
_nv_tuples = self._report_3d_rmse(res)
else:
raise NotImplementedError
name_value_tuples.extend(_nv_tuples)
name_value = OrderedDict(name_value_tuples)
return name_value
|