### Copyright (C) 2017 NVIDIA Corporation. All rights reserved. 
### Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
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 SequenceDataset(BaseDataset):
    def initialize(self, opt):
        self.opt = opt
        self.root = opt.dataroot

        ### input A (label maps)
        self.dir_A = opt.dataroot
        self.A_paths = sorted(make_dataset(self.dir_A))

        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_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)

    def name(self):
        return 'SequenceDataset'