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