| # 读取并且保存smplx路径 | |
| # import os | |
| # import joblib | |
| # import random | |
| # from tqdm import tqdm | |
| # data_root = "data/smplx_data" | |
| # all_data = [] | |
| # for root, dirs, files in tqdm(os.walk(data_root)): | |
| # for file in files: | |
| # if file.endswith('.npy'): | |
| # path = os.path.join(root, file) | |
| # if os.path.exists(path.replace('smplx_data', 'gmr_data')): | |
| # all_data.append(path) | |
| # if len(all_data) % 10000 == 0: | |
| # print(len(all_data)) | |
| # joblib.dump(all_data, 'data/smplx_path.pkl') | |
| import joblib | |
| import numpy as np | |
| from joblib import Parallel, delayed | |
| from tqdm import tqdm | |
| import random | |
| data_paths = joblib.load('data/smplx_data/smplx_path.pkl') | |
| all_data = Parallel(n_jobs=24)(delayed(np.load)(file_path) for file_path in tqdm(data_paths)) | |
| random.shuffle(all_data) | |
| import ipdb; ipdb.set_trace() | |
| joblib.dump(all_data[:-20000], 'data/train_smplx_400k.pkl') | |
| # joblib.dump(all_data[:100_000], 'data/train_smplx_100k.pkl') | |
| joblib.dump(all_data[-20000:], 'data/test_smplx_20k.pkl') | |
| # joblib.dump(all_data[-5000:], 'data/test_smplx_5k.pkl') | |
| smplx_data = np.concatenate(all_data[::4], axis=0) | |
| Mean = smplx_data.mean(axis=0) | |
| # Std = smplx_data.std(axis=0) | |
| # joints_num = 30 | |
| # Std[:2] = Std[:2].mean() / 1.0 | |
| # Std[2:8] = Std[2:8].mean() / 1.0 | |
| # Std[8:8+3*joints_num] = Std[8:8+3*joints_num].mean() / 1.0 | |
| # Std[8+3*joints_num:8+6*joints_num] = Std[8+3*joints_num:8+6*joints_num].mean() / 1.0 | |
| # Std[8+6*joints_num:] = Std[8+6*joints_num:].mean() / 1.0 | |
| # np.save(f'data/gmr_mean.npy', Mean) | |
| # np.save(f'data/gmr_std.npy', Std) |