import cv2 import paddle import os.path from .base_dataset import BaseDataset, get_params, get_transform from .image_folder import make_dataset from .builder import DATASETS from .transforms.builder import build_transforms @DATASETS.register() class PairedDataset(BaseDataset): """A dataset class for paired image dataset. """ def __init__(self, cfg): """Initialize this dataset class. Args: cfg (dict) -- stores all the experiment flags """ BaseDataset.__init__(self, cfg) self.dir_AB = os.path.join(cfg.dataroot, cfg.phase) # get the image directory self.AB_paths = sorted(make_dataset( self.dir_AB, cfg.max_dataset_size)) # get image paths self.input_nc = self.cfg.output_nc if self.cfg.direction == 'BtoA' else self.cfg.input_nc self.output_nc = self.cfg.input_nc if self.cfg.direction == 'BtoA' else self.cfg.output_nc self.transforms = build_transforms(cfg.transforms) 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] AB = cv2.imread(AB_path) # split AB image into A and B h, w = AB.shape[:2] # w, h = AB.size w2 = int(w / 2) A = AB[:h, :w2, :] B = AB[:h, w2:, :] # apply the same transform to both A and B A, B = self.transforms((A, 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)