Spaces:
Running
on
Zero
Running
on
Zero
File size: 2,515 Bytes
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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):
@staticmethod
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 |