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# Copyright (c) 2022 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 copy
from enum import IntEnum
import os.path as osp
from .base import BaseDataset
from paddlers.utils import logging, get_encoding, norm_path, is_pic
class CDDataset(BaseDataset):
"""
Dataset for change detection tasks.
Args:
data_dir (str): Root directory of the dataset.
file_list (str): Path of the file that contains relative paths of images and annotation files. When
`with_seg_labels` False, each line in the file contains the paths of the bi-temporal images and
the change mask. When `with_seg_labels` is True, each line in the file contains the paths of the
bi-temporal images, the path of the change mask, and the paths of the segmentation masks in both
temporal phases.
transforms (paddlers.transforms.Compose): Data preprocessing and data augmentation operators to apply.
label_list (str|None, optional): Path of the file that contains the category names. Defaults to None.
num_workers (int|str, optional): Number of processes used for data loading. If `num_workers` is 'auto',
the number of workers will be automatically determined according to the number of CPU cores: If
there are more than 16 cores8 workers will be used. Otherwise, the number of workers will be half
the number of CPU cores. Defaults: 'auto'.
shuffle (bool, optional): Whether to shuffle the samples. Defaults to False.
with_seg_labels (bool, optional): Set `with_seg_labels` to True if the datasets provides segmentation
masks (e.g., building masks in each temporal phase). Defaults to False.
binarize_labels (bool, optional): Whether to binarize change masks and segmentation masks.
Defaults to False.
"""
def __init__(self,
data_dir,
file_list,
transforms,
label_list=None,
num_workers='auto',
shuffle=False,
with_seg_labels=False,
binarize_labels=False):
super(CDDataset, self).__init__(data_dir, label_list, transforms,
num_workers, shuffle)
DELIMETER = ' '
# TODO: batch padding
self.batch_transforms = None
self.file_list = list()
self.labels = list()
self.with_seg_labels = with_seg_labels
if self.with_seg_labels:
num_items = 5 # RGB1, RGB2, CD, Seg1, Seg2
else:
num_items = 3 # RGB1, RGB2, CD
self.binarize_labels = binarize_labels
# TODO: If `label_list` is not None, let the user parse `label_list`.
if label_list is not None:
with open(label_list, encoding=get_encoding(label_list)) as f:
for line in f:
item = line.strip()
self.labels.append(item)
with open(file_list, encoding=get_encoding(file_list)) as f:
for line in f:
items = line.strip().split(DELIMETER)
if len(items) != num_items:
raise ValueError(
"Line[{}] in file_list[{}] has an incorrect number of file paths.".
format(line.strip(), file_list))
items = list(map(norm_path, items))
full_path_im_t1 = osp.join(data_dir, items[0])
full_path_im_t2 = osp.join(data_dir, items[1])
full_path_label = osp.join(data_dir, items[2])
if not all(
map(is_pic, (full_path_im_t1, full_path_im_t2,
full_path_label))):
continue
if not osp.exists(full_path_im_t1):
raise IOError("Image file {} does not exist!".format(
full_path_im_t1))
if not osp.exists(full_path_im_t2):
raise IOError("Image file {} does not exist!".format(
full_path_im_t2))
if not osp.exists(full_path_label):
raise IOError("Label file {} does not exist!".format(
full_path_label))
if with_seg_labels:
full_path_seg_label_t1 = osp.join(data_dir, items[3])
full_path_seg_label_t2 = osp.join(data_dir, items[4])
if not osp.exists(full_path_seg_label_t1):
raise IOError("Label file {} does not exist!".format(
full_path_seg_label_t1))
if not osp.exists(full_path_seg_label_t2):
raise IOError("Label file {} does not exist!".format(
full_path_seg_label_t2))
item_dict = dict(
image_t1=full_path_im_t1,
image_t2=full_path_im_t2,
mask=full_path_label)
if with_seg_labels:
item_dict['aux_masks'] = [
full_path_seg_label_t1, full_path_seg_label_t2
]
self.file_list.append(item_dict)
self.num_samples = len(self.file_list)
logging.info("{} samples in file {}".format(
len(self.file_list), file_list))
def __getitem__(self, idx):
sample = copy.deepcopy(self.file_list[idx])
sample = self.transforms.apply_transforms(sample)
if self.binarize_labels:
# Requires 'mask' to exist
sample['mask'] = self._binarize(sample['mask'])
if 'aux_masks' in sample:
sample['aux_masks'] = list(
map(self._binarize, sample['aux_masks']))
outputs = self.transforms.arrange_outputs(sample)
return outputs
def __len__(self):
return len(self.file_list)
def _binarize(self, mask, threshold=127):
return (mask > threshold).astype('int64')
class MaskType(IntEnum):
"""
Enumeration of the mask types used in the change detection task.
"""
CD = 0
SEG_T1 = 1
SEG_T2 = 2