| import os.path |
| from data.base_dataset import BaseDataset, get_params, get_transform |
| from PIL import Image |
| import linecache |
|
|
| class AlignedDataset(BaseDataset): |
| def initialize(self, opt): |
| self.opt = opt |
| self.root = opt.dataroot |
|
|
| self.fine_height=256 |
| self.fine_width=192 |
|
|
| self.dataset_size = len(open('demo.txt').readlines()) |
|
|
| dir_I = '_img' |
| self.dir_I = os.path.join(opt.dataroot, opt.phase + dir_I) |
|
|
| dir_C = '_clothes' |
| self.dir_C = os.path.join(opt.dataroot, opt.phase + dir_C) |
|
|
| dir_E = '_edge' |
| self.dir_E = os.path.join(opt.dataroot, opt.phase + dir_E) |
|
|
| def __getitem__(self, index): |
|
|
| file_path ='demo.txt' |
| im_name, c_name = linecache.getline(file_path, index+1).strip().split() |
|
|
| I_path = os.path.join(self.dir_I,im_name) |
| I = Image.open(I_path).convert('RGB') |
| |
| I = I.resize((self.fine_width, self.fine_height)) |
|
|
| params = get_params(self.opt, I.size) |
| transform = get_transform(self.opt, params) |
| transform_E = get_transform(self.opt, params, method=Image.NEAREST, normalize=False) |
|
|
| I_tensor = transform(I) |
|
|
| C_path = os.path.join(self.dir_C,c_name) |
| C = Image.open(C_path).convert('RGB') |
| C_tensor = transform(C) |
|
|
| E_path = os.path.join(self.dir_E,c_name) |
| E = Image.open(E_path).convert('L') |
| E_tensor = transform_E(E) |
|
|
| input_dict = { 'image': I_tensor,'clothes': C_tensor, 'edge': E_tensor} |
| return input_dict |
|
|
| def __len__(self): |
| return self.dataset_size |
|
|
| def name(self): |
| return 'AlignedDataset' |
|
|
