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95 lines
3.9 KiB
95 lines
3.9 KiB
# Copyright (c) 2021 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 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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from paddlers.models.ppseg.datasets import Dataset |
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URL = 'https://bj.bcebos.com/paddleseg/dataset/hrf/hrf.zip' |
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@manager.DATASETS.add_component |
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class HRF(Dataset): |
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""" |
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The HRF dataset is a dataset for retinal vessel segmentation which comprises 45 images and is organized as 15 subsets. Each subset contains one healthy fundus image, one image of patient with diabetic retinopathy and one glaucoma image. The image sizes are 3,304 x 2,336, with a training/testing image split of 21/24. |
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(https://doi.org/10.1155/2013/154860) |
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Args: |
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transforms (list): Transforms for image. |
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dataset_root (str): The dataset directory. Default: None |
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edge (bool): whether extract edge infor in the output |
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mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'. |
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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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edge=False, |
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mode='train'): |
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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.edge = edge |
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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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if mode not in ['train', 'val', 'test']: |
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raise ValueError( |
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"`mode` should be 'train', 'val' or 'test', but got {}.".format( |
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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_list.txt') |
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elif mode == 'val': |
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file_path = os.path.join(self.dataset_root, 'val_list.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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