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import os
import random
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from sam_diffsr.tasks.srdiff import SRDiffTrainer
from sam_diffsr.utils_sr.dataset import SRDataSet
from sam_diffsr.utils_sr.hparams import hparams
from sam_diffsr.utils_sr.matlab_resize import imresize
class InferDataSet(Dataset):
def __init__(self, img_dir):
super().__init__()
self.img_path_list = [os.path.join(img_dir, img_name) for img_name in os.listdir(img_dir)]
self.to_tensor_norm = transforms.Compose([
transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
def __getitem__(self, index):
sr_scale = hparams['sr_scale']
img_path = self.img_path_list[index]
img_name = os.path.basename(img_path)
img_lr = Image.open(img_path).convert('RGB')
img_lr = np.uint8(np.asarray(img_lr))
h, w, c = img_lr.shape
h, w = h * sr_scale, w * sr_scale
h = h - h % (sr_scale * 2)
w = w - w % (sr_scale * 2)
h_l = h // sr_scale
w_l = w // sr_scale
img_lr = img_lr[:h_l, :w_l]
img_lr_up = imresize(img_lr / 256, hparams['sr_scale']) # np.float [H, W, C]
img_lr, img_lr_up = [self.to_tensor_norm(x).float() for x in [img_lr, img_lr_up]]
return img_lr, img_lr_up, img_name
def __len__(self):
return len(self.img_path_list)
class Df2kDataSet(SRDataSet):
def __init__(self, prefix='train'):
if prefix == 'valid':
_prefix = 'test'
else:
_prefix = prefix
super().__init__(_prefix)
self.patch_size = hparams['patch_size']
self.patch_size_lr = hparams['patch_size'] // hparams['sr_scale']
if prefix == 'valid':
self.len = hparams['eval_batch_size'] * hparams['valid_steps']
self.data_aug_transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomRotation(20, resample=Image.BICUBIC),
transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
])
def __getitem__(self, index):
item = self._get_item(index)
hparams = self.hparams
sr_scale = hparams['sr_scale']
img_hr = np.uint8(item['img'])
img_lr = np.uint8(item['img_lr'])
# TODO: clip for SRFlow
h, w, c = img_hr.shape
h = h - h % (sr_scale * 2)
w = w - w % (sr_scale * 2)
h_l = h // sr_scale
w_l = w // sr_scale
img_hr = img_hr[:h, :w]
img_lr = img_lr[:h_l, :w_l]
# random crop
if self.prefix == 'train':
if self.data_augmentation and random.random() < 0.5:
img_hr, img_lr = self.data_augment(img_hr, img_lr)
i = random.randint(0, h - self.patch_size) // sr_scale * sr_scale
i_lr = i // sr_scale
j = random.randint(0, w - self.patch_size) // sr_scale * sr_scale
j_lr = j // sr_scale
img_hr = img_hr[i:i + self.patch_size, j:j + self.patch_size]
img_lr = img_lr[i_lr:i_lr + self.patch_size_lr, j_lr:j_lr + self.patch_size_lr]
img_lr_up = imresize(img_lr / 256, hparams['sr_scale']) # np.float [H, W, C]
img_hr, img_lr, img_lr_up = [self.to_tensor_norm(x).float() for x in [img_hr, img_lr, img_lr_up]]
return {
'img_hr': img_hr, 'img_lr': img_lr,
'img_lr_up': img_lr_up, 'item_name': item['item_name'],
'loc': np.array(item['loc']), 'loc_bdr': np.array(item['loc_bdr'])
}
def __len__(self):
return self.len
def data_augment(self, img_hr, img_lr):
sr_scale = self.hparams['sr_scale']
img_hr = Image.fromarray(img_hr)
img_hr = self.data_aug_transforms(img_hr)
img_hr = np.asarray(img_hr) # np.uint8 [H, W, C]
img_lr = imresize(img_hr, 1 / sr_scale)
return img_hr, img_lr
class SRDiffDf2k(SRDiffTrainer):
def __init__(self):
super().__init__()
self.dataset_cls = Df2kDataSet
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