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datasets.py
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datasets.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 *
from PIL import Image, ImageOps, ImageFile
import random
from glob import glob
import torchvision.transforms as transforms
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
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):
(w, h) = img_in.size
new_size_in = tuple([int(scale*w), int(scale*h)])
img_in = img_in.resize(new_size_in, resample=Image.BICUBIC)
return img_in
def modcrop(im):
(w, h) = im.size
# new_h = h//modulo*modulo
# new_w = w//modulo*modulo
# ih = h - new_h
# iw = w - new_w
if w >= h:
dl = (w - h)//2
dr = w - h - dl
ims = im.crop((dl, 0, w - dr, h))
else:
dt = (h - w)//2
db = h - w - dt
ims = im.crop((0, dt, w, h - db))
return ims
def get_patch(img_in, img_tar, patch_size, scale, ix=-1, iy=-1):
(ih, iw) = img_in.size
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))
#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}
# color_factor = 1.2
# contrast_factor = 1.2
# bright_factor = 1.1
# sharp_factor = 1.1
# img_tar = ImageEnhance.Color(img_tar).enhance(color_factor)
# img_tar = ImageEnhance.Contrast(img_tar).enhance(contrast_factor)
# img_tar = ImageEnhance.Brightness(img_tar).enhance(bright_factor)
# img_tar = ImageEnhance.Sharpness(img_tar).enhance(sharp_factor)
if random.random() < 0.5 and flip_h:
img_in = ImageOps.flip(img_in)
img_tar = ImageOps.flip(img_tar)
info_aug['flip_h'] = True
if rot:
if random.random() < 0.5:
img_in = ImageOps.mirror(img_in)
img_tar = ImageOps.mirror(img_tar)
info_aug['flip_v'] = True
if random.random() < 0.5:
img_in = img_in.rotate(180)
img_tar = img_tar.rotate(180)
info_aug['trans'] = True
return img_in, img_tar, info_aug
class StaticRandomCrop(object):
def __init__(self, image_size, crop_size):
self.th, self.tw = crop_size
h, w = image_size
self.h1 = random.randint(0, h - self.th)
self.w1 = random.randint(0, w - self.tw)
def __call__(self, img):
return img[self.h1:(self.h1+self.th), self.w1:(self.w1+self.tw),:]
class StaticCenterCrop(object):
def __init__(self, image_size, crop_size):
self.th, self.tw = crop_size
self.h, self.w = image_size
def __call__(self, img):
return img[(self.h-self.th)//2:(self.h+self.th)//2, (self.w-self.tw)//2:(self.w+self.tw)//2,:]
class DatasetFromFolder(data.Dataset):
def __init__(self, data_dir, ref_dir, fineSize=256):
super(DatasetFromFolder, self).__init__()
self.data_dir = data_dir
self.ref_dir = ref_dir
self.transform = transforms.Compose([
transforms.Resize((288, 288)),
transforms.RandomCrop(fineSize),
transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),
transforms.ToTensor()])
self.style_transform = transforms.Compose([
transforms.Resize((fineSize, fineSize)),
# transforms.RandomCrop(fineSize),
transforms.RandomHorizontalFlip(),
# transforms.RandomVerticalFlip(),
transforms.ToTensor()])
self.input_filenames = sorted(glob(join(data_dir, '*.jpg')))
self.ref_filenames = sorted(glob(join(ref_dir, '*/*.jpg')))
self.ref_len = len(self.ref_filenames)
self.input_len = len(self.input_filenames)
def __getitem__(self, index):
input = load_img(self.input_filenames[index])
rand_no = torch.randint(0, self.ref_len, (1,)).item()
ref = load_img(self.ref_filenames[rand_no])
input = self.transform(input)
ref = self.style_transform(ref)
return input, ref
def __len__(self):
return self.input_len