tonic.functional.to_averaged_timesurface#

Module Contents#

Functions#

to_averaged_timesurface_numpy(events, sensor_size, ...)

Representation that creates averaged timesurfaces for each event for one recording.
tonic.functional.to_averaged_timesurface.to_averaged_timesurface_numpy(events, sensor_size, cell_size, surface_size, time_window, tau, decay)[source]#

Representation that creates averaged timesurfaces for each event for one recording.

Taken from the paper Sironi et al. 2018, HATS: Histograms of averaged time surfaces for robust event-based object classification https://openaccess.thecvf.com/content_cvpr_2018/papers/Sironi_HATS_Histograms_of_CVPR_2018_paper.pdf :param cell_size: size of each square in the grid :type cell_size: int :param surface_size: has to be odd :type surface_size: int :param time_window: how far back to look for past events for the time averaging. Expressed in microseconds. :type time_window: int :param tau: time constant to decay events around occuring event with. Expressed in microseconds. :type tau: int :param decay: can be either ‘lin’ or ‘exp’, corresponding to linear or exponential decay. :type decay: str

Returns:

array of histograms (numpy.Array with shape (n_cells, n_pols, surface_size, surface_size))