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dataset.py
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dataset.py
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import torch.utils.data as data
import torch
import numpy as np
import os
from os import listdir
from os.path import join
from PIL import Image, ImageOps
import random
from random import randrange
def is_image_file(filename):
return any(filename.endswith(extension) for extension in [".png", ".jpg", ".jpeg"])
def load_img(filepath):
img = Image.open(filepath).convert('RGB')
# y, _, _ = img.split()
return img
def rescale_img(img_in, scale):
size_in = img_in.size
new_size_in = tuple([int(x * scale) for x in size_in])
img_in = img_in.resize(new_size_in, resample=Image.BICUBIC)
return img_in
def get_patch(img_in, img_tar, patch_size, scale, ix=-1, iy=-1):
(ih, iw) = img_in.size
#(th, tw) = (scale * ih, scale * iw)
patch_mult = scale # if len(scale) > 1 else 1
tp = patch_mult * patch_size
ip = tp // scale
if ix == -1:
ix = random.randrange(0, iw - ip + 1)
if iy == -1:
iy = random.randrange(0, ih - ip + 1)
(tx, ty) = (scale * ix, scale * iy)
img_in = img_in.crop((iy, ix, iy + ip, ix + ip))
img_tar = img_tar.crop((ty, tx, ty + tp, tx + tp))
#img_bic = img_bic.crop((ty, tx, ty + tp, tx + tp))
#info_patch = {
# 'ix': ix, 'iy': iy, 'ip': ip, 'tx': tx, 'ty': ty, 'tp': tp}
return img_in, img_tar
def augment(img_in, img_tar, flip_h=True, rot=True):
info_aug = {'flip_h': False, 'flip_v': False, 'trans': False}
if random.random() < 0.5 and flip_h:
img_in = ImageOps.flip(img_in)
img_tar = ImageOps.flip(img_tar)
#img_bic = ImageOps.flip(img_bic)
info_aug['flip_h'] = True
if rot:
if random.random() < 0.5:
img_in = ImageOps.mirror(img_in)
img_tar = ImageOps.mirror(img_tar)
#img_bic = ImageOps.mirror(img_bic)
info_aug['flip_v'] = True
if random.random() < 0.5:
img_in = img_in.rotate(180)
img_tar = img_tar.rotate(180)
#img_bic = img_bic.rotate(180)
info_aug['trans'] = True
return img_in, img_tar, info_aug
class DatasetFromFolder(data.Dataset):
def __init__(self, HR_dir, LR_dir, patch_size, upscale_factor, data_augmentation,
transform=None):
super(DatasetFromFolder, self).__init__()
self.hr_image_filenames = [join(HR_dir, x) for x in listdir(HR_dir) if is_image_file(x)]
self.lr_image_filenames = [join(LR_dir, x) for x in listdir(LR_dir) if is_image_file(x)]
self.patch_size = patch_size
self.upscale_factor = upscale_factor
self.transform = transform
self.data_augmentation = data_augmentation
def __getitem__(self, index):
target = load_img(self.hr_image_filenames[index])
name = self.hr_image_filenames[index]
#lr_name = name[:39]+'LR/'+name[42:-4]+'x4.png'
#lr_name = name[:39]+'LR_16x/'+name[42:]
lr_name = name[:32]+'LR_16x/'+name[35:]
input = load_img(lr_name)
input, target, = get_patch(input, target, self.patch_size, self.upscale_factor)
if self.data_augmentation:
input, target, _ = augment(input, target)
if self.transform:
input = self.transform(input)
target = self.transform(target)
return input, target
def __len__(self):
return len(self.hr_image_filenames)
class DatasetFromFolderEval(data.Dataset):
def __init__(self, lr_dir, upscale_factor, transform=None):
super(DatasetFromFolderEval, self).__init__()
self.image_filenames = [join(lr_dir, x) for x in listdir(lr_dir) if is_image_file(x)]
self.upscale_factor = upscale_factor
self.transform = transform
def __getitem__(self, index):
input = load_img(self.image_filenames[index])
_, file = os.path.split(self.image_filenames[index])
bicubic = rescale_img(input, self.upscale_factor)
if self.transform:
#input = self.transform(input)
bicubic = self.transform(bicubic)
return bicubic, file
def __len__(self):
return len(self.image_filenames)