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Running
on
Zero
| import os.path | |
| from data.base_dataset import BaseDataset, get_params, get_transform | |
| from data.image_folder import make_dataset | |
| from PIL import Image | |
| class AlignedDataset(BaseDataset): | |
| """A dataset class for paired image dataset. | |
| It assumes that the directory '/path/to/data/train' contains image pairs in the form of {A,B}. | |
| During test time, you need to prepare a directory '/path/to/data/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.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory | |
| self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths | |
| assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image | |
| self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc | |
| self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc | |
| 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, B, A_paths and B_paths | |
| A (tensor) - - an image in the input domain | |
| B (tensor) - - its corresponding image in the target domain | |
| A_paths (str) - - image paths | |
| B_paths (str) - - image paths (same as A_paths) | |
| """ | |
| # read a image given a random integer index | |
| AB_path = self.AB_paths[index%len(self.AB_paths)] | |
| AB = Image.open(AB_path).convert('RGB') | |
| # split AB image into A and B | |
| w, h = AB.size | |
| w2 = int(w / 2) | |
| A = AB.crop((0, 0, w2, h)) | |
| B = AB.crop((w2, 0, w, h)) | |
| # apply the same transform to both A and B | |
| transform_params = get_params(self.opt, A.size) | |
| A_transform = get_transform(self.opt, transform_params, grayscale=(self.input_nc == 1)) | |
| B_transform = get_transform(self.opt, transform_params, grayscale=(self.output_nc == 1)) | |
| A = A_transform(A) | |
| B = B_transform(B) | |
| return {'A': A, 'B': B, 'A_paths': AB_path, 'B_paths': AB_path} | |
| def __len__(self): | |
| """Return the total number of images in the dataset.""" | |
| return len(self.AB_paths)*100 | |