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86 lines
3.5 KiB
86 lines
3.5 KiB
# Copyright (c) 2020 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 PIL import Image |
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from paddlers_slim.models.ppseg.datasets import Dataset |
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from paddlers_slim.models.ppseg.cvlibs import manager |
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from paddlers_slim.models.ppseg.transforms import Compose |
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@manager.DATASETS.add_component |
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class PascalContext(Dataset): |
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""" |
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PascalVOC2010 dataset `http://host.robots.ox.ac.uk/pascal/VOC/`. |
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If you want to use pascal context dataset, please run the convert_voc2010.py in tools firstly. |
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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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mode (str): Which part of dataset to use. it is one of ('train', 'trainval', 'context', 'val'). |
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If you want to set mode to 'context', please make sure the dataset have been augmented. Default: 'train'. |
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edge (bool, optional): Whether to compute edge while training. Default: False |
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""" |
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NUM_CLASSES = 60 |
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def __init__(self, |
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transforms=None, |
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dataset_root=None, |
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mode='train', |
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edge=False): |
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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.file_list = list() |
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self.num_classes = self.NUM_CLASSES |
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self.ignore_index = 255 |
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self.edge = edge |
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if mode not in ['train', 'trainval', 'val']: |
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raise ValueError( |
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"`mode` should be one of ('train', 'trainval', 'val') in PascalContext dataset, but got {}." |
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.format(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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raise ValueError( |
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"The dataset is not Found or the folder structure is nonconfoumance." |
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) |
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image_set_dir = os.path.join(self.dataset_root, 'ImageSets', |
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'Segmentation') |
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if mode == 'train': |
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file_path = os.path.join(image_set_dir, 'train_context.txt') |
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elif mode == 'val': |
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file_path = os.path.join(image_set_dir, 'val_context.txt') |
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elif mode == 'trainval': |
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file_path = os.path.join(image_set_dir, 'trainval_context.txt') |
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if not os.path.exists(file_path): |
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raise RuntimeError( |
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"PASCAL-Context annotations are not ready, " |
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"Please make sure voc_context.py has been properly run.") |
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img_dir = os.path.join(self.dataset_root, 'JPEGImages') |
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label_dir = os.path.join(self.dataset_root, 'Context') |
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with open(file_path, 'r') as f: |
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for line in f: |
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line = line.strip() |
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image_path = os.path.join(img_dir, ''.join([line, '.jpg'])) |
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label_path = os.path.join(label_dir, ''.join([line, '.png'])) |
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self.file_list.append([image_path, label_path])
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