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Running
on
Zero
Running
on
Zero
| import os.path | |
| from data.base_dataset import BaseDataset, get_params, get_transform | |
| from data.image_folder import make_dataset | |
| from PIL import Image | |
| import random | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| class SingleSrDataset(BaseDataset): | |
| def modify_commandline_options(parser, is_train): | |
| return parser | |
| def __init__(self, opt): | |
| self.opt = opt | |
| self.root = opt.dataroot | |
| self.dir_B = os.path.join(opt.dataroot, opt.phase, opt.folder, 'imgs') | |
| # self.dir_B = os.path.join(opt.dataroot, opt.phase, 'test/imgs', opt.folder) | |
| self.B_paths = make_dataset(self.dir_B) | |
| self.B_paths = sorted(self.B_paths) | |
| self.B_size = len(self.B_paths) | |
| # self.transform = get_transform(opt) | |
| # print(self.B_size) | |
| def __getitem__(self, index): | |
| B_path = self.B_paths[index] | |
| B_img = Image.open(B_path).convert('RGB') | |
| if os.path.exists(B_path.replace('imgs','line').replace('.jpg','.png')): | |
| L_img = Image.open(B_path.replace('imgs','line').replace('.jpg','.png'))#.convert('RGB') | |
| else: | |
| L_img = Image.open(B_path.replace('imgs','line').replace('.png','.jpg'))#.convert('RGB') | |
| B_img = B_img.resize(L_img.size, Image.ANTIALIAS) | |
| ow, oh = B_img.size | |
| transform_params = get_params(self.opt, B_img.size) | |
| B_transform = get_transform(self.opt, transform_params, grayscale=True) | |
| B = B_transform(B_img) | |
| L = B_transform(L_img) | |
| # base = 2**8 | |
| # h = int((oh+base-1) // base * base) | |
| # w = int((ow+base-1) // base * base) | |
| # B = F.pad(B.unsqueeze(0), (0,w-ow, 0,h-oh), 'replicate').squeeze(0) | |
| # L = F.pad(L.unsqueeze(0), (0,w-ow, 0,h-oh), 'replicate').squeeze(0) | |
| return {'B': B, 'Bs': B, 'Bi': B, 'Bl': L, | |
| 'A': torch.zeros(1), 'Ai': torch.zeros(1), 'L': torch.zeros(1), | |
| 'A_paths': B_path, 'h': oh, 'w': ow} | |
| def __len__(self): | |
| return self.B_size | |
| def name(self): | |
| return 'SingleSrDataset' | |
| def M_transform(feat, opt, params=None): | |
| outfeat = feat.copy() | |
| if params is not None: | |
| oh,ow = feat.shape[1:] | |
| x1, y1 = params['crop_pos'] | |
| tw = th = opt.crop_size | |
| if (ow > tw or oh > th): | |
| outfeat = outfeat[:,y1:y1+th,x1:x1+tw] | |
| if params['flip']: | |
| outfeat = np.flip(outfeat, 2).copy()#outfeat[:,:,::-1] | |
| return torch.from_numpy(outfeat).float()*2-1.0 |