|
|
import os |
|
|
|
|
|
from data import common |
|
|
|
|
|
import numpy as np |
|
|
import imageio |
|
|
|
|
|
import torch |
|
|
import torch.utils.data as data |
|
|
|
|
|
class Demo(data.Dataset): |
|
|
def __init__(self, args, name='Demo', train=False, benchmark=False): |
|
|
self.args = args |
|
|
self.name = name |
|
|
self.scale = args.scale |
|
|
self.idx_scale = 0 |
|
|
self.train = False |
|
|
self.benchmark = benchmark |
|
|
|
|
|
self.filelist = [] |
|
|
for f in os.listdir(args.dir_demo): |
|
|
|
|
|
if f.find('.tif') >= 0 or f.find('.jp') >= 0: |
|
|
self.filelist.append(os.path.join(args.dir_demo, f)) |
|
|
self.filelist.sort() |
|
|
|
|
|
def __getitem__(self, idx): |
|
|
filename = os.path.splitext(os.path.basename(self.filelist[idx]))[0] |
|
|
lr = imageio.imread(self.filelist[idx]) |
|
|
lr, = common.set_channel(lr, n_channels=self.args.n_colors) |
|
|
lr_t, = common.np2Tensor(lr, rgb_range=self.args.rgb_range) |
|
|
|
|
|
return lr_t, -1, filename |
|
|
|
|
|
def __len__(self): |
|
|
return len(self.filelist) |
|
|
|
|
|
def set_scale(self, idx_scale): |
|
|
self.idx_scale = idx_scale |
|
|
|
|
|
|
