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import os.path as osp
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from typing import Callable, List, Optional, Union
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from mmengine.fileio import exists, list_from_file
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from mmaction.registry import DATASETS
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from mmaction.utils import ConfigType
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from .base import BaseActionDataset
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@DATASETS.register_module()
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class VideoDataset(BaseActionDataset):
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"""Video dataset for action recognition.
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The dataset loads raw videos and apply specified transforms to return a
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dict containing the frame tensors and other information.
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The ann_file is a text file with multiple lines, and each line indicates
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a sample video with the filepath and label, which are split with a
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whitespace. Example of a annotation file:
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.. code-block:: txt
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some/path/000.mp4 1
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some/path/001.mp4 1
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some/path/002.mp4 2
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some/path/003.mp4 2
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some/path/004.mp4 3
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some/path/005.mp4 3
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Args:
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ann_file (str): Path to the annotation file.
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pipeline (List[Union[dict, ConfigDict, Callable]]): A sequence of
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data transforms.
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data_prefix (dict or ConfigDict): Path to a directory where videos
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are held. Defaults to ``dict(video='')``.
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multi_class (bool): Determines whether the dataset is a multi-class
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dataset. Defaults to False.
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num_classes (int, optional): Number of classes of the dataset, used in
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multi-class datasets. Defaults to None.
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start_index (int): Specify a start index for frames in consideration of
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different filename format. However, when taking videos as input,
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it should be set to 0, since frames loaded from videos count
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from 0. Defaults to 0.
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modality (str): Modality of data. Support ``'RGB'``, ``'Flow'``.
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Defaults to ``'RGB'``.
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test_mode (bool): Store True when building test or validation dataset.
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Defaults to False.
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delimiter (str): Delimiter for the annotation file.
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Defaults to ``' '`` (whitespace).
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"""
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def __init__(self,
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ann_file: str,
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pipeline: List[Union[dict, Callable]],
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data_prefix: ConfigType = dict(video=''),
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multi_class: bool = False,
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num_classes: Optional[int] = None,
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start_index: int = 0,
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modality: str = 'RGB',
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test_mode: bool = False,
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delimiter: str = ' ',
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**kwargs) -> None:
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self.delimiter = delimiter
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super().__init__(
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ann_file,
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pipeline=pipeline,
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data_prefix=data_prefix,
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multi_class=multi_class,
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num_classes=num_classes,
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start_index=start_index,
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modality=modality,
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test_mode=test_mode,
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**kwargs)
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def load_data_list(self) -> List[dict]:
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"""Load annotation file to get video information."""
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exists(self.ann_file)
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data_list = []
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fin = list_from_file(self.ann_file)
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for line in fin:
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line_split = line.strip().split(self.delimiter)
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if self.multi_class:
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assert self.num_classes is not None
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filename, label = line_split[0], line_split[1:]
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label = list(map(int, label))
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elif len(line_split) == 1:
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filename, label = line_split[0], -1
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else:
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filename, label = line_split
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label = int(label)
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if self.data_prefix['video'] is not None:
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filename = osp.join(self.data_prefix['video'], filename)
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data_list.append(dict(filename=filename, label=label))
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return data_list
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