:py:mod:`tonic.datasets.tum_vie`
================================

.. py:module:: tonic.datasets.tum_vie


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   tonic.datasets.tum_vie.TUMVIE




.. py:class:: TUMVIE(save_to: str, recording: str | list[str], transform: collections.abc.Callable | None = None, target_transform: collections.abc.Callable | None = None, transforms: collections.abc.Callable | None = None)


   Bases: :py:obj:`tonic.dataset.Dataset`

   `TUM-VIE <https://vision.in.tum.de/data/datasets/visual-inertial-event-dataset>`_

   TUM-VIE is an event camera dataset for developing 3D perception and navigation algorithms. It contains
   handheld and head-mounted sequences in indoor and outdoor environments with rapid motion during sports
   and high dynamic range. TUM-VIE includes challenging sequences where state-of-the art VIO fails or
   results in large drift. Hence, it can help to push the boundary on event-based visual-inertial algorithms.

   The dataset contains:

   * Stereo event data Prophesee Gen4 HD (1280x720 pixels)
   * Stereo grayscale frames at 20Hz (1024x1024 pixels)
   * IMU data at 200Hz
   * 6dof motion capture data at 120Hz (beginning and end of each sequence)

   Timestamps between all sensors are synchronized in hardware.

   .. note:: To be able to read this dataset, you will need `hdf5plugin` and `PIL` packages installed.

   .. note:: Use `bike-night` instead of `bike-dark` in the list of recordings if you want that specific one.

   ::

       @string{iros="International Conference on Intelligent Robots and Systems (IROS)"}
       @inproceedings{klenk2021tumvie,
        author = {S Klenk and J Chui and N Demmel and D Cremers},
        title = {TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset},
        eprint = {2108.07329},
        eprinttype = {arXiv},
        eprintclass = {cs.CV},
        booktitle = {International Conference on Intelligent Robots and Systems (IROS)},
        year = {2021},
        keywords = {tumvie, event camera, dynamic vision sensor, SLAM, vslam},
       }

   :param save_to: Location to save files to on disk. Will save files in a sub folder 'davis_dataset'.
   :type save_to: string
   :param recording: Use the name of the recording or a list thereof to download it, for example 'skate-hard'
                     or ['skate-hard', 'bike-easy']. See project homepage for a list of available recordings.
                     Can use 'all' to download all available recordings.
   :type recording: string
   :param transform: A callable of transforms to apply to events and/or images.
   :type transform: callable, optional
   :param target_transform: A callable of transforms to apply to the targets/labels.
   :type target_transform: callable, optional
   :param transforms: A callable of transforms that is applied to both data and
                      labels at the same time.
   :type transforms: callable, optional

   .. py:attribute:: base_url
      :value: 'https://tumevent-vi.vision.in.tum.de/'

      

   .. py:attribute:: recordings
      :value: ['mocap-1d-trans', 'mocap-3d-trans', 'mocap-6dof', 'mocap-desk', 'mocap-desk2', 'mocap-shake',...

      

   .. py:attribute:: filenames
      :value: ['events_left.h5', 'events_right.h5', 'vi_gt_data.tar.gz']

      

   .. py:attribute:: sensor_size
      :value: (1280, 720, 2)

      

   .. py:attribute:: dtype

      

   .. py:attribute:: ordering

      

   .. py:attribute:: folder_name
      :value: ''

      

   .. py:method:: __getitem__(index)

      :returns: tuple of (data, target), where data is a dictionary of (events_left, events_right, imu)
                and targets is a dictionary of (images_left, images_right, mocap).


   .. py:method:: __len__()