|
import os |
|
import glob |
|
import random |
|
import pickle |
|
from data import common |
|
import imageio |
|
import torch.utils.data as data |
|
|
|
class SRData(data.Dataset): |
|
def __init__(self, args, name='', benchmark=False): |
|
self.args = args |
|
self.name = name |
|
self.split = 'test' |
|
self.do_eval = True |
|
self.benchmark = benchmark |
|
self.input_large = False |
|
self.scale = args.scale |
|
self.idx_scale = 0 |
|
self._set_filesystem(args.dir_data) |
|
list_hr, list_lr = self._scan() |
|
self.images_hr, self.images_lr = list_hr, list_lr |
|
|
|
|
|
def _scan(self): |
|
names_hr = sorted( |
|
glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0])) |
|
) |
|
names_lr = [[] for _ in self.scale] |
|
for f in names_hr: |
|
filename, _ = os.path.splitext(os.path.basename(f)) |
|
for si, s in enumerate(self.scale): |
|
names_lr[si].append(os.path.join( |
|
self.dir_lr, 'X{}/{}x{}{}'.format( |
|
s, filename, s, self.ext[1] |
|
) |
|
)) |
|
|
|
return names_hr, names_lr |
|
|
|
def _set_filesystem(self, dir_data): |
|
self.apath = os.path.join(dir_data, self.name) |
|
self.dir_hr = os.path.join(self.apath, 'HR') |
|
self.dir_lr = os.path.join(self.apath, 'LR_bicubic') |
|
self.ext = ('.png', '.png') |
|
|
|
def __getitem__(self, idx): |
|
lr, hr, filename = self._load_file(idx) |
|
pair = self.get_patch(lr, hr) |
|
pair = common.set_channel(*pair, n_channels=3) |
|
pair_t = common.np2Tensor(*pair, rgb_range=255) |
|
|
|
return pair_t[0], pair_t[1], filename |
|
|
|
def __len__(self): |
|
return len(self.images_hr) |
|
|
|
def _get_index(self, idx): |
|
return idx |
|
|
|
def _load_file(self, idx): |
|
idx = self._get_index(idx) |
|
f_hr = self.images_hr[idx] |
|
f_lr = self.images_lr[self.idx_scale][idx] |
|
|
|
filename, _ = os.path.splitext(os.path.basename(f_hr)) |
|
hr = imageio.imread(f_hr) |
|
lr = imageio.imread(f_lr) |
|
return lr, hr, filename |
|
|
|
def get_patch(self, lr, hr): |
|
scale = self.scale[self.idx_scale] |
|
ih, iw = lr.shape[:2] |
|
hr = hr[0:ih * scale, 0:iw * scale] |
|
return lr, hr |
|
|
|
def set_scale(self, idx_scale): |
|
if not self.input_large: |
|
self.idx_scale = idx_scale |
|
else: |
|
self.idx_scale = random.randint(0, len(self.scale) - 1) |
|
|
|
|
