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