import os.path
import torchvision.transforms as transforms
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
import PIL
import random
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)
self.transform = get_transform(opt)
def __getitem__(self, index):
A_path = self.A_paths[index % self.A_size]
index_A = index % self.A_size
index_B = random.randint(0, self.B_size - 1)
B_path = self.B_paths[index_B]
# print('(A, B) = (%d, %d)' % (index_A, index_B))
A_img = Image.open(A_path).convert('RGB')
B_img = Image.open(B_path).convert('RGB')
A = self.transform(A_img)
B = self.transform(B_img)
if self.opt.which_direction == 'BtoA':
input_nc = self.opt.output_nc
output_nc = self.opt.input_nc
else:
input_nc = self.opt.input_nc
output_nc = self.opt.output_nc
if input_nc == 1: # RGB to gray
tmp = A[0, ...] * 0.299 + A[1, ...] * 0.587 + A[2, ...] * 0.114
A = tmp.unsqueeze(0)
if output_nc == 1: # RGB to gray
tmp = B[0, ...] * 0.299 + B[1, ...] * 0.587 + B[2, ...] * 0.114
B = tmp.unsqueeze(0)
return {'A': A, 'B': B,
'A_paths': A_path, 'B_paths': B_path}
def __len__(self):
return max(self.A_size, self.B_size)
def name(self):
return 'UnalignedDataset'
