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import random
import numpy as np
import torch.utils.data as data
import utils.utils_image as util
import os
from utils import utils_blindsr as blindsr
class DatasetBlindSR(data.Dataset):
'''
# -----------------------------------------
# dataset for BSRGAN
# -----------------------------------------
'''
def __init__(self, opt):
super(DatasetBlindSR, self).__init__()
self.opt = opt
self.n_channels = opt['n_channels'] if opt['n_channels'] else 3
self.sf = opt['scale'] if opt['scale'] else 4
self.shuffle_prob = opt['shuffle_prob'] if opt['shuffle_prob'] else 0.1
self.use_sharp = opt['use_sharp'] if opt['use_sharp'] else False
self.degradation_type = opt['degradation_type'] if opt['degradation_type'] else 'bsrgan'
self.lq_patchsize = self.opt['lq_patchsize'] if self.opt['lq_patchsize'] else 64
self.patch_size = self.opt['H_size'] if self.opt['H_size'] else self.lq_patchsize*self.sf
self.paths_H = util.get_image_paths(opt['dataroot_H'])
print(len(self.paths_H))
# for n, v in enumerate(self.paths_H):
# if 'face' in v:
# del self.paths_H[n]
# time.sleep(1)
assert self.paths_H, 'Error: H path is empty.'
def __getitem__(self, index):
L_path = None
# ------------------------------------
# get H image
# ------------------------------------
H_path = self.paths_H[index]
img_H = util.imread_uint(H_path, self.n_channels)
img_name, ext = os.path.splitext(os.path.basename(H_path))
H, W, C = img_H.shape
if H < self.patch_size or W < self.patch_size:
img_H = np.tile(np.random.randint(0, 256, size=[1, 1, self.n_channels], dtype=np.uint8), (self.patch_size, self.patch_size, 1))
# ------------------------------------
# if train, get L/H patch pair
# ------------------------------------
if self.opt['phase'] == 'train':
H, W, C = img_H.shape
rnd_h_H = random.randint(0, max(0, H - self.patch_size))
rnd_w_H = random.randint(0, max(0, W - self.patch_size))
img_H = img_H[rnd_h_H:rnd_h_H + self.patch_size, rnd_w_H:rnd_w_H + self.patch_size, :]
if 'face' in img_name:
mode = random.choice([0, 4])
img_H = util.augment_img(img_H, mode=mode)
else:
mode = random.randint(0, 7)
img_H = util.augment_img(img_H, mode=mode)
img_H = util.uint2single(img_H)
if self.degradation_type == 'bsrgan':
img_L, img_H = blindsr.degradation_bsrgan(img_H, self.sf, lq_patchsize=self.lq_patchsize, isp_model=None)
elif self.degradation_type == 'bsrgan_plus':
img_L, img_H = blindsr.degradation_bsrgan_plus(img_H, self.sf, shuffle_prob=self.shuffle_prob, use_sharp=self.use_sharp, lq_patchsize=self.lq_patchsize)
else:
img_H = util.uint2single(img_H)
if self.degradation_type == 'bsrgan':
img_L, img_H = blindsr.degradation_bsrgan(img_H, self.sf, lq_patchsize=self.lq_patchsize, isp_model=None)
elif self.degradation_type == 'bsrgan_plus':
img_L, img_H = blindsr.degradation_bsrgan_plus(img_H, self.sf, shuffle_prob=self.shuffle_prob, use_sharp=self.use_sharp, lq_patchsize=self.lq_patchsize)
# ------------------------------------
# L/H pairs, HWC to CHW, numpy to tensor
# ------------------------------------
img_H, img_L = util.single2tensor3(img_H), util.single2tensor3(img_L)
if L_path is None:
L_path = H_path
return {'L': img_L, 'H': img_H, 'L_path': L_path, 'H_path': H_path}
def __len__(self):
return len(self.paths_H)
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