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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"
@manager.DATASETS.add_component
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])
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