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97 lines
4.4 KiB
97 lines
4.4 KiB
3 years ago
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# Copyright (c) 2021 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.path as osp
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import copy
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from paddle.io import Dataset
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from paddlers.utils import logging, get_num_workers, get_encoding, path_normalization, is_pic
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class CDDataset(Dataset):
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"""读取变化检测任务数据集,并对样本进行相应的处理(来自SegDataset,图像标签需要两个)。
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Args:
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data_dir (str): 数据集所在的目录路径。
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file_list (str): 描述数据集图片文件和对应标注文件的文件路径(文本内每行路径为相对data_dir的相对路)。
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label_list (str): 描述数据集包含的类别信息文件路径。默认值为None。
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transforms (paddlers.transforms): 数据集中每个样本的预处理/增强算子。
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num_workers (int|str): 数据集中样本在预处理过程中的线程或进程数。默认为'auto'。
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shuffle (bool): 是否需要对数据集中样本打乱顺序。默认为False。
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"""
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def __init__(self,
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data_dir,
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file_list,
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label_list=None,
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transforms=None,
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num_workers='auto',
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shuffle=False):
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super(CDDataset, self).__init__()
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self.transforms = copy.deepcopy(transforms)
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# TODO batch padding
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self.batch_transforms = None
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self.num_workers = get_num_workers(num_workers)
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self.shuffle = shuffle
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self.file_list = list()
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self.labels = list()
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# TODO:非None时,让用户跳转数据集分析生成label_list
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# 不要在此处分析label file
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if label_list is not None:
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with open(label_list, encoding=get_encoding(label_list)) as f:
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for line in f:
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item = line.strip()
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self.labels.append(item)
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with open(file_list, encoding=get_encoding(file_list)) as f:
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for line in f:
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items = line.strip().split()
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if len(items) > 3:
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raise Exception(
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"A space is defined as the delimiter to separate the image and label path, " \
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"so the space cannot be in the image or label path, but the line[{}] of " \
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" file_list[{}] has a space in the image or label path.".format(line, file_list))
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items[0] = path_normalization(items[0])
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items[1] = path_normalization(items[1])
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items[2] = path_normalization(items[2])
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if not is_pic(items[0]) or not is_pic(items[1]) or not is_pic(items[2]):
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continue
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full_path_im_t1 = osp.join(data_dir, items[0])
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full_path_im_t2 = osp.join(data_dir, items[1])
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full_path_label = osp.join(data_dir, items[2])
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if not osp.exists(full_path_im_t1):
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raise IOError('Image file {} does not exist!'.format(
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full_path_im_t1))
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if not osp.exists(full_path_im_t2):
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raise IOError('Image file {} does not exist!'.format(
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full_path_im_t2))
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if not osp.exists(full_path_label):
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raise IOError('Label file {} does not exist!'.format(
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full_path_label))
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self.file_list.append({
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'image_t1': full_path_im_t1,
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'image_t2': full_path_im_t2,
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'mask': full_path_label
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})
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self.num_samples = len(self.file_list)
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logging.info("{} samples in file {}".format(
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len(self.file_list), file_list))
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def __getitem__(self, idx):
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sample = copy.deepcopy(self.file_list[idx])
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outputs = self.transforms(sample)
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return outputs
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def __len__(self):
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return len(self.file_list)
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