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RefineDNet/data/aligned_dataset.py

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+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]
+        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)