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from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
class SingleDataset(BaseDataset):
"""This dataset class can load a set of images specified by the path --dataroot /path/to/data.
It can be used for generating CycleGAN results only for one side with the model option '-model test'.
"""
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))
input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc
self.transform = get_transform(opt, grayscale=(input_nc == 1))
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns a dictionary that contains A and A_paths
A(tensor) - - an image in one domain
A_paths(str) - - the path of the image
"""
A_path = self.A_paths[index]
A_img = Image.open(A_path).convert('RGB')
A = self.transform(A_img)
return {'A': A, 'A_paths': A_path}
def __len__(self):
"""Return the total number of images in the dataset."""
return len(self.A_paths)
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