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- # Copyright (c) 2020 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 paddlex.paddleseg.datasets import Dataset
- from paddlex.paddleseg.utils.download import download_file_and_uncompress
- from paddlex.paddleseg.utils import seg_env
- from paddlex.paddleseg.cvlibs import manager
- from paddlex.paddleseg.transforms import Compose
- URL = "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar"
- @manager.DATASETS.add_component
- class PascalVOC(Dataset):
- """
- PascalVOC2012 dataset `http://host.robots.ox.ac.uk/pascal/VOC/`.
- If you want to augment the dataset, please run the voc_augment.py in tools.
- Args:
- transforms (list): Transforms for image.
- dataset_root (str): The dataset directory. Default: None
- mode (str, optional): Which part of dataset to use. it is one of ('train', 'trainval', 'trainaug', 'val').
- If you want to set mode to 'trainaug', please make sure the dataset have been augmented. Default: 'train'.
- edge (bool, optional): Whether to compute edge while training. Default: False
- """
- NUM_CLASSES = 21
- def __init__(self, transforms, dataset_root=None, mode='train',
- edge=False):
- self.dataset_root = dataset_root
- self.transforms = Compose(transforms)
- mode = mode.lower()
- self.mode = mode
- self.file_list = list()
- self.num_classes = self.NUM_CLASSES
- self.ignore_index = 255
- self.edge = edge
- if mode not in ['train', 'trainval', 'trainaug', 'val']:
- raise ValueError(
- "`mode` should be one of ('train', 'trainval', 'trainaug', 'val') in PascalVOC dataset, 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,
- extraname='VOCdevkit')
- 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)
- image_set_dir = os.path.join(self.dataset_root, 'VOC2012', 'ImageSets',
- 'Segmentation')
- if mode == 'train':
- file_path = os.path.join(image_set_dir, 'train.txt')
- elif mode == 'val':
- file_path = os.path.join(image_set_dir, 'val.txt')
- elif mode == 'trainval':
- file_path = os.path.join(image_set_dir, 'trainval.txt')
- elif mode == 'trainaug':
- file_path = os.path.join(image_set_dir, 'train.txt')
- file_path_aug = os.path.join(image_set_dir, 'aug.txt')
- if not os.path.exists(file_path_aug):
- raise RuntimeError(
- "When `mode` is 'trainaug', Pascal Voc dataset should be augmented, "
- "Please make sure voc_augment.py has been properly run when using this mode."
- )
- img_dir = os.path.join(self.dataset_root, 'VOC2012', 'JPEGImages')
- label_dir = os.path.join(self.dataset_root, 'VOC2012',
- 'SegmentationClass')
- label_dir_aug = os.path.join(self.dataset_root, 'VOC2012',
- 'SegmentationClassAug')
- with open(file_path, 'r') as f:
- for line in f:
- line = line.strip()
- image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
- label_path = os.path.join(label_dir, ''.join([line, '.png']))
- self.file_list.append([image_path, label_path])
- if mode == 'trainaug':
- with open(file_path_aug, 'r') as f:
- for line in f:
- line = line.strip()
- image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
- label_path = os.path.join(label_dir_aug,
- ''.join([line, '.png']))
- self.file_list.append([image_path, label_path])
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