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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 paddleseg.datasets import Dataset | |
| 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 | |
| URL = "http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar" | |
| 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]) | |