ToAveragedTimesurface#

class tonic.transforms.ToAveragedTimesurface(sensor_size: Tuple[int, int, int], surface_size: int = 5, cell_size: int = 10, time_window: float = 1000.0, tau: float = 100, decay: str = 'exp')[source]#

Create averaged timesurfaces for each event. Taken from the paper Sironi et al. 2018, HATS: Histograms of averaged time surfaces for robust event-based object classification https://opena ccess.thecvf.com/content_cvpr_2018/papers/Sironi_HATS_Histograms_of_CVPR_2018_paper.pdf.

Parameters:

  • sensor_size (Tuple[int, int, int]) – a 3-tuple of x,y,p for sensor_size

  • cell_size (int) – size of each square in the grid

  • surface_size (int) – has to be odd

  • time_window (float) – how far back to look for past events for the time averaging

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

  • decay (str) – can be either ‘lin’ or ‘exp’, corresponding to linear or exponential decay.