File size: 1,979 Bytes
6221b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 |
# utils/dataset.py
from torch.utils.data import Dataset
from PIL import Image
import torchvision.transforms as transforms
import os
class DIV2KDataset(Dataset):
def __init__(self, hr_dir, lr_dir, patch_size=96, upscale_factor=4):
self.hr_files = sorted(os.listdir(hr_dir))
self.lr_files = sorted(os.listdir(lr_dir))
self.hr_dir = hr_dir
self.lr_dir = lr_dir
self.patch_size = patch_size
self.upscale_factor = upscale_factor
# LR transform
self.lr_transform = transforms.Compose([
transforms.Resize((patch_size//upscale_factor, patch_size//upscale_factor),
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor()
])
# HR transform
self.hr_transform = transforms.Compose([
transforms.Resize((patch_size, patch_size),
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor()
])
# Upscale LR images to match HR size for SRCNN
self.lr_upscale = transforms.Compose([
transforms.Resize((patch_size, patch_size),
interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor()
])
def __getitem__(self, idx):
# Load images and convert to YCbCr
hr_img = Image.open(os.path.join(self.hr_dir, self.hr_files[idx])).convert('YCbCr')
lr_img = Image.open(os.path.join(self.lr_dir, self.lr_files[idx])).convert('YCbCr')
# Extract Y channel
hr_y, _, _ = hr_img.split()
lr_y, _, _ = lr_img.split()
# For SRCNN, we need to upscale LR images first
lr_y_upscaled = self.lr_upscale(lr_y)
hr_y_tensor = self.hr_transform(hr_y)
return lr_y_upscaled, hr_y_tensor
def __len__(self):
return len(self.hr_files) |