| from PIL import Image | |
| import numpy as np | |
| from skimage.io import imsave | |
| import glob | |
| import pdb | |
| import os | |
| import torchvision | |
| import torch | |
| from torchvision.models.feature_extraction import create_feature_extractor | |
| from torchvision import transforms | |
| from tqdm import tqdm | |
| from shutil import copyfile | |
| feat_extractor = torchvision.models.vgg16(pretrained=True).features.cuda() | |
| # pdb.set_trace() | |
| tsfm = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| lama_dir = '/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512' | |
| lama_feat_dir = '/mnt/session_space/home/lingzzha/EgoHOS/data/train/lama_512_feature' | |
| os.makedirs(lama_feat_dir, exist_ok = True) | |
| for file in tqdm(glob.glob(lama_dir + '/*')): | |
| fname = os.path.basename(file).split('.')[0] | |
| clean_back = tsfm(Image.open(file).convert('RGB').resize((480, 360))).cuda() | |
| clean_back_feat = feat_extractor(clean_back.unsqueeze(0)).reshape(-1) | |
| clean_back_feat_np = clean_back_feat.cpu().data.numpy() | |
| np.save(os.path.join(lama_feat_dir, fname + '.npy'), clean_back_feat_np) | |