124 lines
5.6 KiB
Python
124 lines
5.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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from typing import List
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import mmengine.fileio as fileio
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from mmseg.registry import DATASETS
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from .basesegdataset import BaseSegDataset
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@DATASETS.register_module()
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class NYUDataset(BaseSegDataset):
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"""NYU depth estimation dataset. The file structure should be.
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.. code-block:: none
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├── data
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│ ├── nyu
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│ │ ├── images
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│ │ │ ├── train
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│ │ │ │ ├── scene_xxx.jpg
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│ │ │ │ ├── ...
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│ │ │ ├── test
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│ │ ├── annotations
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│ │ │ ├── train
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│ │ │ │ ├── scene_xxx.png
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│ │ │ │ ├── ...
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│ │ │ ├── test
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Args:
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ann_file (str): Annotation file path. Defaults to ''.
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metainfo (dict, optional): Meta information for dataset, such as
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specify classes to load. Defaults to None.
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data_root (str, optional): The root directory for ``data_prefix`` and
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``ann_file``. Defaults to None.
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data_prefix (dict, optional): Prefix for training data. Defaults to
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dict(img_path='images', depth_map_path='annotations').
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img_suffix (str): Suffix of images. Default: '.jpg'
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seg_map_suffix (str): Suffix of segmentation maps. Default: '.png'
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filter_cfg (dict, optional): Config for filter data. Defaults to None.
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indices (int or Sequence[int], optional): Support using first few
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data in annotation file to facilitate training/testing on a smaller
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dataset. Defaults to None which means using all ``data_infos``.
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serialize_data (bool, optional): Whether to hold memory using
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serialized objects, when enabled, data loader workers can use
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shared RAM from master process instead of making a copy. Defaults
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to True.
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pipeline (list, optional): Processing pipeline. Defaults to [].
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test_mode (bool, optional): ``test_mode=True`` means in test phase.
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Defaults to False.
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lazy_init (bool, optional): Whether to load annotation during
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instantiation. In some cases, such as visualization, only the meta
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information of the dataset is needed, which is not necessary to
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load annotation file. ``Basedataset`` can skip load annotations to
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save time by set ``lazy_init=True``. Defaults to False.
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max_refetch (int, optional): If ``Basedataset.prepare_data`` get a
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None img. The maximum extra number of cycles to get a valid
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image. Defaults to 1000.
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ignore_index (int): The label index to be ignored. Default: 255
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reduce_zero_label (bool): Whether to mark label zero as ignored.
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Default to False.
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backend_args (dict, Optional): Arguments to instantiate a file backend.
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See https://mmengine.readthedocs.io/en/latest/api/fileio.htm
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for details. Defaults to None.
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Notes: mmcv>=2.0.0rc4, mmengine>=0.2.0 required.
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"""
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METAINFO = dict(
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classes=('printer_room', 'bathroom', 'living_room', 'study',
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'conference_room', 'study_room', 'kitchen', 'home_office',
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'bedroom', 'dinette', 'playroom', 'indoor_balcony',
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'laundry_room', 'basement', 'excercise_room', 'foyer',
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'home_storage', 'cafe', 'furniture_store', 'office_kitchen',
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'student_lounge', 'dining_room', 'reception_room',
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'computer_lab', 'classroom', 'office', 'bookstore'))
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def __init__(self,
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data_prefix=dict(
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img_path='images', depth_map_path='annotations'),
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img_suffix='.jpg',
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depth_map_suffix='.png',
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**kwargs) -> None:
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super().__init__(
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data_prefix=data_prefix,
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img_suffix=img_suffix,
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seg_map_suffix=depth_map_suffix,
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**kwargs)
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def _get_category_id_from_filename(self, image_fname: str) -> int:
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"""Retrieve the category ID from the given image filename."""
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image_fname = osp.basename(image_fname)
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position = image_fname.find(next(filter(str.isdigit, image_fname)), 0)
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categoty_name = image_fname[:position - 1]
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if categoty_name not in self._metainfo['classes']:
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return -1
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else:
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return self._metainfo['classes'].index(categoty_name)
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def load_data_list(self) -> List[dict]:
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"""Load annotation from directory or annotation file.
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Returns:
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list[dict]: All data info of dataset.
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"""
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data_list = []
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img_dir = self.data_prefix.get('img_path', None)
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ann_dir = self.data_prefix.get('depth_map_path', None)
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_suffix_len = len(self.img_suffix)
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for img in fileio.list_dir_or_file(
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dir_path=img_dir,
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list_dir=False,
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suffix=self.img_suffix,
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recursive=True,
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backend_args=self.backend_args):
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data_info = dict(img_path=osp.join(img_dir, img))
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if ann_dir is not None:
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depth_map = img[:-_suffix_len] + self.seg_map_suffix
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data_info['depth_map_path'] = osp.join(ann_dir, depth_map)
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data_info['seg_fields'] = []
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data_info['category_id'] = self._get_category_id_from_filename(img)
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data_list.append(data_info)
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data_list = sorted(data_list, key=lambda x: x['img_path'])
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return data_list
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