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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/optic_disc_seg.zip"
-
-
- @manager.DATASETS.add_component
- class OpticDiscSeg(Dataset):
- """
- OpticDiscSeg dataset is extraced from iChallenge-AMD
- (https://ai.baidu.com/broad/subordinate?dataset=amd).
-
- Args:
- transforms (list): Transforms for image.
- dataset_root (str): The dataset directory. Default: None
- mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). 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', 'test']:
- raise ValueError(
- "`mode` should be 'train', 'val' or 'test', 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_list.txt')
- elif mode == 'val':
- file_path = os.path.join(self.dataset_root, 'val_list.txt')
- else:
- file_path = os.path.join(self.dataset_root, 'test_list.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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