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from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
import torch
IMG_FEAT_DIM = {
'resnet': 2048,
'vit': 1024
}
N_JOINTS = 17
root = 'dataset'
class PATHS:
# Raw data folders
PARSED_DATA = f'{root}/parsed_data'
AMASS_PTH = f'{root}/AMASS'
THREEDPW_PTH = f'{root}/3DPW'
HUMAN36M_PTH = f'{root}/Human36M'
RICH_PTH = f'{root}/RICH'
EMDB_PTH = f'{root}/EMDB'
# Processed labels
AMASS_LABEL = f'{root}/parsed_data/amass.pth'
THREEDPW_LABEL = f'{root}/parsed_data/3dpw_dset_backbone.pth'
MPII3D_LABEL = f'{root}/parsed_data/mpii3d_dset_backbone.pth'
HUMAN36M_LABEL = f'{root}/parsed_data/human36m_dset_backbone.pth'
INSTA_LABEL = f'{root}/parsed_data/insta_dset_backbone.pth'
BEDLAM_LABEL = f'{root}/parsed_data/bedlam_train_backbone.pth'
class KEYPOINTS:
NUM_JOINTS = N_JOINTS
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]
J17_TO_H36M = [14, 3, 4, 5, 2, 1, 0, 15, 12, 16, 13, 9, 10, 11, 8, 7, 6]
COCO_AUG_DICT = f'{root}/body_models/coco_aug_dict.pth'
TREE = [[5, 6], 0, 0, 1, 2, -1, -1, 5, 6, 7, 8, -1, -1, 11, 12, 13, 14, 15, 15, 15, 16, 16, 16]
# STD scale for video noise
S_BIAS = 1e-1
S_JITTERING = 5e-2
S_PEAK = 3e-1
S_PEAK_MASK = 5e-3
S_MASK = 0.03
class BMODEL:
MAIN_JOINTS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] # reduced_joints
FLDR = f'{root}/body_models/smpl/'
SMPLX2SMPL = f'{root}/body_models/smplx2smpl.pkl'
FACES = f'{root}/body_models/smpl_faces.npy'
MEAN_PARAMS = f'{root}/body_models/smpl_mean_params.npz'
JOINTS_REGRESSOR_WHAM = f'{root}/body_models/J_regressor_wham.npy'
JOINTS_REGRESSOR_H36M = f'{root}/body_models/J_regressor_h36m.npy'
JOINTS_REGRESSOR_EXTRA = f'{root}/body_models/J_regressor_extra.npy'
JOINTS_REGRESSOR_FEET = f'{root}/body_models/J_regressor_feet.npy'
PARENTS = torch.tensor([
-1, 0, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 9, 12, 13, 14, 16, 17, 18, 19, 20, 21]) |