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RefineDNet/data/paired_dataset.py

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+import os
+import ntpath
+from data.base_dataset import BaseDataset, get_transform
+from data.image_folder import make_dataset
+from PIL import Image
+import random
+
+import torch.nn.functional as F
+import torchvision.transforms as transforms
+import numpy as np
+
+class PairedDataset(BaseDataset):
+    """
+    This dataset class can load paired datasets.
+
+    It requires two directories to host training images from domain A '/path/to/data/trainA'
+    and from domain B '/path/to/data/trainB' respectively.
+    You can train the model with the dataset flag '--dataroot /path/to/data'.
+    Similarly, you need to prepare two directories:
+    '/path/to/data/testA' and '/path/to/data/testB' during test time.
+    """
+    @staticmethod
+    def modify_commandline_options(parser, is_train):
+        """Add new dataset-specific options, and rewrite default values for existing options.
+
+        Parameters:
+            parser          -- original option parser
+            is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
+
+        Returns:
+            the modified parser.
+        """
+        parser.add_argument('--gt_prefix', type=str, default='', help='name of the used prior')
+        return parser
+
+    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_A = os.path.join(opt.dataroot, opt.phase + 'A')  # create a path '/path/to/data/trainA'
+        self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B')  # create a path '/path/to/data/trainB'
+
+        self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size))   # load images from '/path/to/data/trainA'
+        # self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size))    # load images from '/path/to/data/trainB'
+        self.A_size = len(self.A_paths)  # get the size of dataset A
+        # self.B_size = len(self.B_paths)  # get the size of dataset B
+
+        # btoA = self.opt.direction == 'BtoA'
+        # input_nc = self.opt.output_nc if btoA else self.opt.input_nc       # get the number of channels of input image
+        # output_nc = self.opt.input_nc if btoA else self.opt.output_nc      # get the number of channels of output image
+        self.transform = get_transform(self.opt, grayscale=(self.opt.input_nc == 1))
+        self.toTensor = transforms.ToTensor()
+        # self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))
+
+    def __getitem__(self, index):
+        """Return a data point and its metadata information.
+
+        Parameters:
+            index (int)      -- 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
+        """
+        A_path = self.A_paths[index]  # make sure index is within then range
+
+        A_name = os.path.splitext(ntpath.basename(A_path))[0]
+        B_shortPath = '%s%s.png'%('_'.join(A_name.split('_')[:-1]), self.opt.gt_prefix) # '%s.png'%A_name.split('_')[0]
+        B_path = os.path.join(self.dir_B, B_shortPath)
+
+        A_img = Image.open(A_path).convert('RGB')
+
+        if os.path.exists(B_path):
+            B_img = Image.open(B_path).convert('RGB')
+        else:
+            print('file [%s] not exist!'%B_path)
+            B_img = A_img
+
+        if A_img.size != B_img.size:
+            B_img = self.cropImage(B_img, A_img.size)
+
+        # apply image transformation
+        A = self.transform(A_img)
+        # B = self.toTensor(B_img)
+        B = self.transform(B_img)
+
+        # return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path}
+        return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path, 
+                'clear': B, 'paths': A_path}
+
+    def __len__(self):
+        """Return the total number of images in the dataset.
+
+        As we have two datasets with potentially different number of images,
+        we take a maximum of
+        """
+        return self.A_size
+
+    def cropImage(self, img, target_size):
+        ow, oh = img.size
+        tw, th = target_size
+
+        if (ow > tw or oh > th):
+            x1 = np.floor((ow - tw)/2)
+            y1 = np.floor((oh - th)/2)
+            return img.crop((x1, y1, x1 + tw, y1 + th))
+        return img