tonic.datasets.dvs_lips#

Module Contents#

Classes#

DVSLip

DVS-Lip
class tonic.datasets.dvs_lips.DVSLip(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#

Bases: tonic.dataset.Dataset

DVS-Lip

@inproceedings{tan2022multi,
    title={Multi-Grained Spatio-Temporal Features Perceived Network for Event-Based Lip-Reading},
    author={Tan, Ganchao and Wang, Yang and Han, Han and Cao, Yang and Wu, Feng and Zha, Zheng-Jun},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    pages={20094--20103},
    year={2022}
}

Implementation inspired from original script: https://github.com/tgc1997/event-based-lip-reading/blob/main/utils/dataset.py
Parameters:

  • save_to (string) – Location to save files to on disk.

  • train (bool) – If True, uses training subset, otherwise testing subset.

  • transform (callable, optional) – A callable of transforms to apply to the data.

  • target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.

  • transforms (callable, optional) – A callable of transforms that is applied to both data and labels at the same time.

base_url = 'https://drive.google.com/file/d/1dBEgtmctTTWJlWnuWxFtk8gfOdVVpkQ0/view'#
filename = 'DVS-Lip.zip'#
base_folder = 'DVS-Lip'#
file_md5 = '2dcb959255122d4cdeb6094ca282494b'#
sensor_size = (128, 128, 2)#
dtype#
ordering#
classes = ['accused', 'action', 'allow', 'allowed', 'america', 'american', 'another', 'around', 'attacks',...#
ambiguous_classes = ['action', 'allow', 'allowed', 'america', 'american', 'around', 'being', 'benefit', 'benefits',...#
__getitem__(index)[source]#
Returns:

a tuple of (events, target) where target is the index of the target class.

__len__()[source]#