paddlers_slim/models/ppseg/datasets/drive.py

# 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 paddlers_slim.models.ppseg.utils.download import download_file_and_uncompress
from paddlers_slim.models.ppseg.utils import seg_env
from paddlers_slim.models.ppseg.cvlibs import manager
from paddlers_slim.models.ppseg.transforms import Compose
from paddlers_slim.models.ppseg.datasets import Dataset

URL = 'https://bj.bcebos.com/paddleseg/dataset/drive/drive.zip'


@manager.DATASETS.add_component
class DRIVE(Dataset):
    """
    The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation.
    It consists of a total of JPEG 40 color fundus images which is of size (584, 565); including 7 abnormal pathology cases.
    (http://www.isi.uu.nl/Research/Databases/DRIVE/)

    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])