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98 lines
3.9 KiB
98 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 paddleseg.utils.download import download_file_and_uncompress |
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from paddleseg.utils import seg_env |
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from paddleseg.cvlibs import manager |
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from paddleseg.transforms import Compose |
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from paddleseg.datasets import Dataset |
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URL = 'https://bj.bcebos.com/paddleseg/dataset/chase_db1/chase_db1.zip' |
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@manager.DATASETS.add_component |
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class CHASEDB1(Dataset): |
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""" |
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CHASE_DB1 dataset is a dataset for retinal vessel segmentation |
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which contains 28 color retina images with the size of 999×960 pixels. |
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It is collected from both left and right eyes of 14 school children. |
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Each image is annotated by two independent human experts, and we choose the labels from 1st expert. |
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(https://blogs.kingston.ac.uk/retinal/chasedb1/) |
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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 # labels only have 1/0, thus ignore_index is not necessary |
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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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