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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
import glob
from paddleseg.datasets import Dataset
from paddleseg.cvlibs import manager
from paddleseg.transforms import Compose
@manager.DATASETS.add_component
class Cityscapes(Dataset):
"""
Cityscapes dataset `https://www.cityscapes-dataset.com/`.
The folder structure is as follow:
cityscapes
|
|--leftImg8bit
| |--train
| |--val
| |--test
|
|--gtFine
| |--train
| |--val
| |--test
Make sure there are **labelTrainIds.png in gtFine directory. If not, please run the conver_cityscapes.py in tools.
Args:
transforms (list): Transforms for image.
dataset_root (str): Cityscapes dataset directory.
mode (str, optional): Which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
edge (bool, optional): Whether to compute edge while training. Default: False
"""
NUM_CLASSES = 19
def __init__(self, transforms, dataset_root, mode='train', edge=False):
self.dataset_root = dataset_root
self.transforms = Compose(transforms)
self.file_list = list()
mode = mode.lower()
self.mode = mode
self.num_classes = self.NUM_CLASSES
self.ignore_index = 255
self.edge = edge
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.")
img_dir = os.path.join(self.dataset_root, 'leftImg8bit')
label_dir = os.path.join(self.dataset_root, 'gtFine')
if self.dataset_root is None or not os.path.isdir(
self.dataset_root) or not os.path.isdir(
img_dir) or not os.path.isdir(label_dir):
raise ValueError(
"The dataset is not Found or the folder structure is nonconfoumance."
)
label_files = sorted(
glob.glob(
os.path.join(label_dir, mode, '*',
'*_gtFine_labelTrainIds.png')))
img_files = sorted(
glob.glob(os.path.join(img_dir, mode, '*', '*_leftImg8bit.png')))
self.file_list = [
[img_path, label_path]
for img_path, label_path in zip(img_files, label_files)
]
|