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