73 lines
3.1 KiB
Python
73 lines
3.1 KiB
Python
import os.path
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from data.base_dataset import BaseDataset, get_transform, get_params
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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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class UnpairedDataset(BaseDataset):
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"""
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This dataset class can load unpaired datasets for dehazing.
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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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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_I = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'
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self.dir_J = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'
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self.I_paths = sorted(make_dataset(self.dir_I, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
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self.J_paths = sorted(make_dataset(self.dir_J, opt.max_dataset_size)) # load images from '/path/to/data/trainB'
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self.I_size = len(self.I_paths) # get the size of dataset A
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self.J_size = len(self.J_paths) # get the size of dataset B
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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 haze, clear, paths and J_paths
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haze (tensor) -- hazy image
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clear (tensor) -- clear image
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paths (str) -- image paths
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J_paths (str) -- image paths
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"""
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I_path = self.I_paths[index % self.I_size] # make sure index is within then range
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if self.opt.serial_batches: # make sure index is within then range
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index_J = index % self.J_size
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else: # randomize the index for domain B to avoid fixed pairs.
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index_J = random.randint(0, self.J_size - 1)
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J_path = self.J_paths[index_J]
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I_img = Image.open(I_path).convert('RGB')
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J_img = Image.open(J_path).convert('RGB')
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params_I = get_params(self.opt, I_img.size)
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params_J = get_params(self.opt, J_img.size)
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transform_I = get_transform(self.opt, params=params_I, grayscale=(self.opt.input_nc == 1))
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transform_J = get_transform(self.opt, params=params_J, grayscale=(self.opt.output_nc == 1))
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# apply image transformation
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real_I = transform_I(I_img)
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real_J = transform_J(J_img)
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return {'haze': real_I, 'clear': real_J, 'paths': I_path, 'J_paths': J_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 max(self.I_size, self.J_size)
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