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import torch
import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
import random
from PIL import Image
import PIL
from pdb import set_trace as st
class UnalignedDataset(BaseDataset):
def initialize(self, opt):
self.opt = opt
self.root = opt.dataroot
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A')
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')
self.A_paths = make_dataset(self.dir_A)
self.B_paths = make_dataset(self.dir_B)
self.A_paths = sorted(self.A_paths)
self.B_paths = sorted(self.B_paths)
self.A_size = len(self.A_paths)
self.B_size = len(self.B_paths)
transform_list = [transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
self.transform = transforms.Compose(transform_list)
# self.transform = get_transform(opt)
def __getitem__(self, index):
A_path = self.A_paths[index % self.A_size]
B_path = self.B_paths[index % self.B_size]
A_img = Image.open(A_path).convert('RGB')
B_img = Image.open(B_path).convert('RGB')
A_size = A_img.size
B_size = B_img.size
A_size = A_size = (A_size[0]//16*16, A_size[1]//16*16)
B_size = B_size = (B_size[0]//16*16, B_size[1]//16*16)
A_img = A_img.resize(A_size, Image.BICUBIC)
B_img = B_img.resize(B_size, Image.BICUBIC)
A_img = self.transform(A_img)
B_img = self.transform(B_img)
if self.opt.resize_or_crop == 'no':
pass
else:
w = A_img.size(2)
h = A_img.size(1)
size = [8,16,22]
from random import randint
size_index = randint(0,2)
Cropsize = size[size_index]*16
w_offset = random.randint(0, max(0, w - Cropsize - 1))
h_offset = random.randint(0, max(0, h - Cropsize - 1))
A_img = A_img[:, h_offset:h_offset + Cropsize,
w_offset:w_offset + Cropsize]
if (not self.opt.no_flip) and random.random() < 0.5:
idx = [i for i in range(A_img.size(2) - 1, -1, -1)]
idx = torch.LongTensor(idx)
A_img = A_img.index_select(2, idx)
B_img = B_img.index_select(2, idx)
if (not self.opt.no_flip) and random.random() < 0.5:
idx = [i for i in range(A_img.size(1) - 1, -1, -1)]
idx = torch.LongTensor(idx)
A_img = A_img.index_select(1, idx)
B_img = B_img.index_select(1, idx)
return {'A': A_img, 'B': B_img,
'A_paths': A_path, 'B_paths': B_path}
def __len__(self):
return max(self.A_size, self.B_size)
def name(self):
return 'UnalignedDataset'
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