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| #!/usr/bin/python | |
| # -*- encoding: utf-8 -*- | |
| import torch | |
| from torch.utils.data import Dataset | |
| import torchvision.transforms as transforms | |
| import os.path as osp | |
| import os | |
| from PIL import Image | |
| import numpy as np | |
| import json | |
| import cv2 | |
| from transform import * | |
| class FaceMask(Dataset): | |
| def __init__(self, rootpth, cropsize=(640, 480), mode='train', *args, **kwargs): | |
| super(FaceMask, self).__init__(*args, **kwargs) | |
| assert mode in ('train', 'val', 'test') | |
| self.mode = mode | |
| self.ignore_lb = 255 | |
| self.rootpth = rootpth | |
| self.imgs = os.listdir(os.path.join(self.rootpth, 'CelebA-HQ-img')) | |
| # pre-processing | |
| self.to_tensor = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), | |
| ]) | |
| self.trans_train = Compose([ | |
| ColorJitter( | |
| brightness=0.5, | |
| contrast=0.5, | |
| saturation=0.5), | |
| HorizontalFlip(), | |
| RandomScale((0.75, 1.0, 1.25, 1.5, 1.75, 2.0)), | |
| RandomCrop(cropsize) | |
| ]) | |
| def __getitem__(self, idx): | |
| impth = self.imgs[idx] | |
| img = Image.open(osp.join(self.rootpth, 'CelebA-HQ-img', impth)) | |
| img = img.resize((512, 512), Image.BILINEAR) | |
| label = Image.open(osp.join(self.rootpth, 'mask', impth[:-3]+'png')).convert('P') | |
| # print(np.unique(np.array(label))) | |
| if self.mode == 'train': | |
| im_lb = dict(im=img, lb=label) | |
| im_lb = self.trans_train(im_lb) | |
| img, label = im_lb['im'], im_lb['lb'] | |
| img = self.to_tensor(img) | |
| label = np.array(label).astype(np.int64)[np.newaxis, :] | |
| return img, label | |
| def __len__(self): | |
| return len(self.imgs) | |
| if __name__ == "__main__": | |
| face_data = '/home/zll/data/CelebAMask-HQ/CelebA-HQ-img' | |
| face_sep_mask = '/home/zll/data/CelebAMask-HQ/CelebAMask-HQ-mask-anno' | |
| mask_path = '/home/zll/data/CelebAMask-HQ/mask' | |
| counter = 0 | |
| total = 0 | |
| for i in range(15): | |
| # files = os.listdir(osp.join(face_sep_mask, str(i))) | |
| atts = ['skin', 'l_brow', 'r_brow', 'l_eye', 'r_eye', 'eye_g', 'l_ear', 'r_ear', 'ear_r', | |
| 'nose', 'mouth', 'u_lip', 'l_lip', 'neck', 'neck_l', 'cloth', 'hair', 'hat'] | |
| for j in range(i*2000, (i+1)*2000): | |
| mask = np.zeros((512, 512)) | |
| for l, att in enumerate(atts, 1): | |
| total += 1 | |
| file_name = ''.join([str(j).rjust(5, '0'), '_', att, '.png']) | |
| path = osp.join(face_sep_mask, str(i), file_name) | |
| if os.path.exists(path): | |
| counter += 1 | |
| sep_mask = np.array(Image.open(path).convert('P')) | |
| # print(np.unique(sep_mask)) | |
| mask[sep_mask == 225] = l | |
| cv2.imwrite('{}/{}.png'.format(mask_path, j), mask) | |
| print(j) | |
| print(counter, total) | |