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| # Copyright (c) 2021 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 paddleseg.utils.download import download_file_and_uncompress | |
| from paddleseg.utils import seg_env | |
| from paddleseg.cvlibs import manager | |
| from paddleseg.transforms import Compose | |
| from paddleseg.datasets import Dataset | |
| URL = 'https://bj.bcebos.com/paddleseg/dataset/chase_db1/chase_db1.zip' | |
| class CHASEDB1(Dataset): | |
| """ | |
| CHASE_DB1 dataset is a dataset for retinal vessel segmentation | |
| which contains 28 color retina images with the size of 999×960 pixels. | |
| It is collected from both left and right eyes of 14 school children. | |
| Each image is annotated by two independent human experts, and we choose the labels from 1st expert. | |
| (https://blogs.kingston.ac.uk/retinal/chasedb1/) | |
| Args: | |
| transforms (list): Transforms for image. | |
| dataset_root (str): The dataset directory. Default: None | |
| edge (bool): whether extract edge infor in the output | |
| mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'. | |
| """ | |
| NUM_CLASSES = 2 | |
| def __init__(self, | |
| dataset_root=None, | |
| transforms=None, | |
| edge=False, | |
| mode='train'): | |
| self.dataset_root = dataset_root | |
| self.transforms = Compose(transforms) | |
| mode = mode.lower() | |
| self.mode = mode | |
| self.edge = edge | |
| self.file_list = list() | |
| self.num_classes = self.NUM_CLASSES | |
| self.ignore_index = 255 # labels only have 1/0, thus ignore_index is not necessary | |
| 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') | |
| 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]) | |