tonic.functional.to_timesurface#

Module Contents#

Functions#

to_timesurface_numpy(events, sensor_size, dt, tau[, ...])

Representation that creates timesurfaces for each event in the recording. Modeled after the
tonic.functional.to_timesurface.to_timesurface_numpy(events, sensor_size: Tuple[int, int, int], dt: float, tau: float, overlap: int = 0, include_incomplete: bool = False)[source]#

Representation that creates timesurfaces for each event in the recording. Modeled after the paper Lagorce et al. 2016, Hots: a hierarchy of event-based time-surfaces for pattern recognition https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7508476. Unlike the paper, surfaces are always generated across the whole sensor, not just around the event.

Parameters:

  • sensor_size (Tuple[int, int, int]) – x/y/p dimensions of the sensor

  • dt (float) – time interval at which the time-surfaces are accumulated

  • tau (float) – time constant to decay events around occuring event with.

  • overlap (int) –

  • include_incomplete (bool) –

Returns:

array of timesurfaces with dimensions (n_events//dt, p, h , w)