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