| import sys |
| sys.path.append('/mnt/shenzhen2cephfs/capybarali/codes/humanoid') |
|
|
| import torch, yaml, os |
| from tqdm import tqdm |
| from smplx import SMPLX |
| from src.utils.rotation_conversions import quaternion_to_matrix, matrix_to_rotation_6d, axis_angle_to_6d |
| import numpy as np |
| from argparse import ArgumentParser |
| import joblib |
| from data.vis import vis_3d_motion |
| from data.vis_g1 import vis_3d_g1 |
| from copy import deepcopy |
|
|
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
|
|
| parser = ArgumentParser(description="Launch MoCap processing") |
| parser.add_argument('--save_root', type=str, default="data/smplx_data") |
| parser.add_argument('--start_idx', type=int, default=0) |
| parser.add_argument('--interval', type=int, default=1) |
| args = parser.parse_args() |
|
|
| os.makedirs(args.save_root, exist_ok=True) |
| os.makedirs(os.path.join(args.save_root, 'motions'), exist_ok=True) |
|
|
| smplx_model = SMPLX( |
| model_path='checkpoints/human_model/SMPLX_NEUTRAL.npz', |
| use_pca=False, num_expression_coeffs=100, num_betas=10, ext='npz' |
| ).to(device) |
|
|
| def get_smplx_motion(data_path): |
| data = dict(np.load(data_path)) |
| import ipdb; ipdb.set_trace() |
| smplx_data = dict( |
| smplx_transl=data["transl"], |
| smplx_global_orient=data["global_orient"], |
| smplx_body_pose=data["body_pose"], |
| ) |
|
|
| transl = torch.from_numpy(smplx_data['smplx_transl']) |
| betas = torch.from_numpy(np.load('checkpoints/humanoid_model/g1/betas.npy')) |
| global_orient = torch.from_numpy(smplx_data['smplx_global_orient']) |
| body_pose = torch.from_numpy(smplx_data['smplx_body_pose']) |
|
|
| N = transl.shape[0] |
| motion_params = dict( |
| transl=transl, |
| global_orient=global_orient, |
| body_pose=body_pose, |
| betas=betas.unsqueeze(0).repeat(N, 1).float() |
| ) |
| |
| frame_params = {k: v.to(device) for k, v in motion_params.items()} |
| frame_params['leye_pose'] = torch.zeros((N, 3)).to(device) |
| frame_params['reye_pose'] = torch.zeros((N, 3)).to(device) |
| frame_params['left_hand_pose'] = torch.zeros((N, 45)).to(device) |
| frame_params['right_hand_pose'] = torch.zeros((N, 45)).to(device) |
| frame_params['jaw_pose'] = torch.zeros((N, 3)).to(device) |
| frame_params['expression'] = torch.zeros((N, 100)).to(device) |
| output = smplx_model(**frame_params) |
| position_data = output.joints.detach().cpu()[:, :22] |
| position_val_data = position_data[1:] - position_data[:-1] |
|
|
| root_idx = 0 |
| |
| |
| ori = deepcopy(position_data[0, root_idx]) |
| y_min = torch.min(position_data[:, :, 1]) |
| ori[1] = y_min |
| position_data = position_data - ori |
| velocities_root = position_data[1:, root_idx, :] - position_data[:-1, root_idx, :] |
| |
| position_data_cp = deepcopy(position_data) |
| position_data[:,:,0] -= position_data_cp[:,0:1,0] |
| position_data[:,:,2] -= position_data_cp[:,0:1,2] |
|
|
| T, njoint, _ = position_data.shape |
| final_x = torch.zeros((T, 2 + 6 + njoint * 3 + njoint * 3)) |
| final_x[1:, 0] = velocities_root[:, 0] |
| final_x[1:, 1] = velocities_root[:, 1] |
| final_x[:, 2:2+6] = axis_angle_to_6d(global_orient) |
| final_x[:, 8:8+njoint*3] = position_data.flatten(1, 2) |
| final_x[1:, 8+njoint*3:8+njoint*6] = position_val_data.flatten(1, 2) |
|
|
| |
| |
| return final_x |
|
|
| |
| if __name__ == '__main__': |
| with open('data/smplx_path.txt', 'r') as f: |
| paths = f.readlines() |
|
|
| for line in tqdm(paths[args.start_idx::args.interval]): |
| data_path = line.strip() |
| save_path = data_path.replace('.npz', '.npy').replace('/mnt/shenzhen2cephfs/capybarali/codes/neobot/data/motionmillion/1_filtered_smplx/', '') |
| save_path = args.save_root + '/' + save_path |
| if os.path.exists(save_path): |
| continue |
| smplx_motion = get_smplx_motion(data_path) |
| os.makedirs(os.path.dirname(save_path), exist_ok=True) |
| np.save(save_path, smplx_motion) |
| |
|
|
