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99 lines
4.0 KiB
99 lines
4.0 KiB
3 years ago
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# 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/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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