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_A = get_transform(self.opt, grayscale=(input_nc == 1))
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 - - 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 / (self.targets_no + 1))
A = AB.crop((0, 0, w2, h))
# apply the same transform to both A and B
transform_params = get_params(self.preprocess, self.load_size,
self.crop_size, A.size)
A_transform = get_transform(self.preprocess,
self.load_size,
self.crop_size,
self.no_flip,
transform_params,
grayscale=(self.input_nc == 1))
B_transform = get_transform(self.preprocess,
self.load_size,
self.crop_size,
self.no_flip,
transform_params,
grayscale=(self.output_nc == 1))
A = A_transform(A)
B_Array = []
for i in range(1, self.targets_no + 1):
B = AB.crop((w2 * i, 0, w2 * (i + 1), h))
B = B_transform(B)
B_Array.append(B)
return {'A': A, 'B': B_Array, 'A_paths': AB_path, 'B_paths': AB_path}
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.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))
input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc
self.transform = get_transform(opt, grayscale=(input_nc == 1))
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 = os.path.join(opt.dataroot, opt.phase)
self.AB_paths = sorted(make_dataset(self.dir, opt.max_dataset_size))
assert (opt.input_nc == 1 and opt.output_nc == 2
and opt.direction == 'AtoB')
self.transform = get_transform(self.opt, convert=False)
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths and meta information of the dataset.
- define the image transformation.
"""
# save the option and dataset root
BaseDataset.__init__(self, opt)
# get the image paths of your dataset;
self.image_paths = [
] # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root
# define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function
self.transform = get_transform(opt)