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- import os.path
- from data.base_dataset import BaseDataset, get_params, get_transform, normalize
- from data.image_folder import make_dataset
- from PIL import Image
- class AlignedDataset(BaseDataset):
- def initialize(self, opt):
- self.opt = opt
- self.root = opt.dataroot
- ### input A (label maps)
- dir_A = '_A' if self.opt.label_nc == 0 else '_label'
- self.dir_A = os.path.join(opt.dataroot, opt.phase + dir_A)
- self.A_paths = sorted(make_dataset(self.dir_A))
- ### input B (real images)
- if opt.isTrain or opt.use_encoded_image:
- dir_B = '_B' if self.opt.label_nc == 0 else '_img'
- self.dir_B = os.path.join(opt.dataroot, opt.phase + dir_B)
- self.B_paths = sorted(make_dataset(self.dir_B))
- ### instance maps
- if not opt.no_instance:
- self.dir_inst = os.path.join(opt.dataroot, opt.phase + '_inst')
- self.inst_paths = sorted(make_dataset(self.dir_inst))
- ### load precomputed instance-wise encoded features
- if opt.load_features:
- self.dir_feat = os.path.join(opt.dataroot, opt.phase + '_feat')
- print('----------- loading features from %s ----------' % self.dir_feat)
- self.feat_paths = sorted(make_dataset(self.dir_feat))
- self.dataset_size = len(self.A_paths)
-
- def __getitem__(self, index):
- ### input A (label maps)
- A_path = self.A_paths[index]
- A = Image.open(A_path)
- params = get_params(self.opt, A.size)
- if self.opt.label_nc == 0:
- transform_A = get_transform(self.opt, params)
- A_tensor = transform_A(A.convert('RGB'))
- else:
- transform_A = get_transform(self.opt, params, method=Image.NEAREST, normalize=False)
- A_tensor = transform_A(A) * 255.0
- B_tensor = inst_tensor = feat_tensor = 0
- ### input B (real images)
- if self.opt.isTrain or self.opt.use_encoded_image:
- B_path = self.B_paths[index]
- B = Image.open(B_path).convert('RGB')
- transform_B = get_transform(self.opt, params)
- B_tensor = transform_B(B)
- ### if using instance maps
- if not self.opt.no_instance:
- inst_path = self.inst_paths[index]
- inst = Image.open(inst_path)
- inst_tensor = transform_A(inst)
- if self.opt.load_features:
- feat_path = self.feat_paths[index]
- feat = Image.open(feat_path).convert('RGB')
- norm = normalize()
- feat_tensor = norm(transform_A(feat))
- input_dict = {'label': A_tensor, 'inst': inst_tensor, 'image': B_tensor,
- 'feat': feat_tensor, 'path': A_path}
- return input_dict
- def __len__(self):
- return len(self.A_paths) // self.opt.batchSize * self.opt.batchSize
- def name(self):
- return 'AlignedDataset'
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