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97 lines
4.4 KiB
97 lines
4.4 KiB
# Copyright (c) 2022 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) |