ucf101.py

import glob
import os

from .video_utils import VideoClips
from .utils import list_dir
from .folder import make_dataset
from .vision import VisionDataset


class UCF101(VisionDataset):
    """
    UCF101 <https://www.crcv.ucf.edu/data/UCF101.php>`_ dataset.

    UCF101 is an action recognition video dataset.
    This dataset consider every video as a collection of video clips of fixed size, specified
    by ``frames_per_clip``, where the step in frames between each clip is given by
    ``step_between_clips``.

    To give an example, for 2 videos with 10 and 15 frames respectively, if ``frames_per_clip=5``
    and ``step_between_clips=5``, the dataset size will be (2 + 3) = 5, where the first two
    elements will come from video 1, and the next three elements from video 2.
    Note that we drop clips which do not have exactly ``frames_per_clip`` elements, so not all
    frames in a video might be present.

    Internally, it uses a VideoClips object to handle clip creation.

    Args:
        root (string): Root directory of the UCF101 Dataset.
        annotation_path (str): path to the folder containing the split files
        frames_per_clip (int): number of frames in a clip.
        step_between_clips (int, optional): number of frames between each clip.
        fold (int, optional): which fold to use. Should be between 1 and 3.
        train (bool, optional): if ``True``, creates a dataset from the train split,
            otherwise from the ``test`` split.
        transform (callable, optional): A function/transform that  takes in a TxHxWxC video
            and returns a transformed version.

    Returns:
        video (Tensor[T, H, W, C]): the `T` video frames
        audio(Tensor[K, L]): the audio frames, where `K` is the number of channels
            and `L` is the number of points
        label (int): class of the video clip
    """

    def __init__(self, root, annotation_path, frames_per_clip, step_between_clips=1,
                 fold=1, train=True, transform=None):
        super(UCF101, self).__init__(root)
        if not 1 <= fold <= 3:
            raise ValueError("fold should be between 1 and 3, got {}".format(fold))

        extensions = ('avi',)
        self.fold = fold
        self.train = train

        classes = list(sorted(list_dir(root)))
        class_to_idx = {classes[i]: i for i in range(len(classes))}
        self.samples = make_dataset(self.root, class_to_idx, extensions, is_valid_file=None)
        self.classes = classes
        video_list = [x[0] for x in self.samples]
        video_clips = VideoClips(video_list, frames_per_clip, step_between_clips)
        self.indices = self._select_fold(video_list, annotation_path, fold, train)
        self.video_clips = video_clips.subset(self.indices)
        self.transform = transform

    def _select_fold(self, video_list, annotation_path, fold, train):
        name = "train" if train else "test"
        name = "{}list{:02d}.txt".format(name, fold)
        f = os.path.join(annotation_path, name)
        selected_files = []
        with open(f, "r") as fid:
            data = fid.readlines()
            data = [x.strip().split(" ") for x in data]
            data = [x[0] for x in data]
            selected_files.extend(data)
        selected_files = set(selected_files)
        indices = [i for i in range(len(video_list)) if video_list[i][len(self.root) + 1:] in selected_files]
        return indices

    def __len__(self):
        return self.video_clips.num_clips()

    def __getitem__(self, idx):
        video, audio, info, video_idx = self.video_clips.get_clip(idx)
        label = self.samples[self.indices[video_idx]][1]

        if self.transform is not None:
            video = self.transform(video)

        return video, audio, label