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| # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import os | |
| from .dataset import Dataset | |
| from paddleseg.utils.download import download_file_and_uncompress | |
| from paddleseg.utils import seg_env | |
| from paddleseg.cvlibs import manager | |
| from paddleseg.transforms import Compose | |
| URL = "https://paddleseg.bj.bcebos.com/dataset/MiniDeepGlobeRoadExtraction.zip" | |
| class MiniDeepGlobeRoadExtraction(Dataset): | |
| """ | |
| MiniDeepGlobeRoadExtraction dataset is extraced from DeepGlobe CVPR2018 challenge (http://deepglobe.org/) | |
| There are 800 images in the training set and 200 images in the validation set. | |
| Args: | |
| dataset_root (str, optional): The dataset directory. Default: None. | |
| transforms (list, optional): Transforms for image. Default: None. | |
| mode (str, optional): Which part of dataset to use. It is one of ('train', 'val'). Default: 'train'. | |
| edge (bool, optional): Whether to compute edge while training. Default: False. | |
| """ | |
| NUM_CLASSES = 2 | |
| def __init__(self, | |
| dataset_root=None, | |
| transforms=None, | |
| mode='train', | |
| edge=False): | |
| self.dataset_root = dataset_root | |
| self.transforms = Compose(transforms) | |
| mode = mode.lower() | |
| self.mode = mode | |
| self.file_list = list() | |
| self.num_classes = self.NUM_CLASSES | |
| self.ignore_index = 255 | |
| self.edge = edge | |
| if mode not in ['train', 'val']: | |
| raise ValueError( | |
| "`mode` should be 'train' or 'val', but got {}.".format(mode)) | |
| if self.transforms is None: | |
| raise ValueError("`transforms` is necessary, but it is None.") | |
| if self.dataset_root is None: | |
| self.dataset_root = download_file_and_uncompress( | |
| url=URL, | |
| savepath=seg_env.DATA_HOME, | |
| extrapath=seg_env.DATA_HOME) | |
| elif not os.path.exists(self.dataset_root): | |
| self.dataset_root = os.path.normpath(self.dataset_root) | |
| savepath, extraname = self.dataset_root.rsplit( | |
| sep=os.path.sep, maxsplit=1) | |
| self.dataset_root = download_file_and_uncompress( | |
| url=URL, | |
| savepath=savepath, | |
| extrapath=savepath, | |
| extraname=extraname) | |
| if mode == 'train': | |
| file_path = os.path.join(self.dataset_root, 'train.txt') | |
| else: | |
| file_path = os.path.join(self.dataset_root, 'val.txt') | |
| with open(file_path, 'r') as f: | |
| for line in f: | |
| items = line.strip().split('|') | |
| if len(items) != 2: | |
| if mode == 'train' or mode == 'val': | |
| raise Exception( | |
| "File list format incorrect! It should be" | |
| " image_name|label_name\\n") | |
| image_path = os.path.join(self.dataset_root, items[0]) | |
| grt_path = None | |
| else: | |
| image_path = os.path.join(self.dataset_root, items[0]) | |
| grt_path = os.path.join(self.dataset_root, items[1]) | |
| self.file_list.append([image_path, grt_path]) | |