SDK Core ML Preprocessing API
Preprocessing functions in Pytorch They take (N,5) events in (b,x,y,p,t) format.
- metavision_core_ml.preprocessing.event_to_tensor_torch.event_cd_to_torch(events)
Converts Events to Torch array Note that Polarities are mapped from {0;1} to {-1;1}
- Parameters
events (EventCD) – structured array containing N events
- Returns
(Tensor) N,5 in batch_index, x, y, polarity, timestamp (micro-seconds) where the batch index value is 0.
- metavision_core_ml.preprocessing.event_to_tensor_torch.event_image(events, batch_size, height, width)
Densifies events into an image
- Parameters
events (tensor) – N,5 (b,x,y,p,t)
batch_size (int) – batch size
height (int) – height of output image
width (int) – width of output image
- metavision_core_ml.preprocessing.event_to_tensor_torch.event_volume(events, batch_size, height, width, start_times, durations, nbins, mode='bilinear', vol=None)
Densifies events into an volume (uniform cut)
- Parameters
events (tensor) – N,5 (b,x,y,p,t)
batch_size (int) – batch size
height (int) – height of output volume
width (int) – width of output volume
start_times – (B,) start times of each volume
durations – (B,) durations for each volume
nbins (int) – number of time bins for output volume
mode (str) – either “bilinear” or “nearest” interpolation of voxels.
- metavision_core_ml.preprocessing.event_to_tensor_torch.events_cd_list_to_torch(list_of_events_np_arrays)
Converts a list of Events CD numpy arrays into torch format (N,5) : batch_index, x, y, polarity_timestamp (micro-seconds) Note that Polarities are mapped from {0;1} to {-1;1}
- Parameters
list_of_events_np_arrays (list of np.array) – list of structured arrays of EventCD
- Returns
value of batch_index corresponds to the position of the event array in the input list
- Return type
(Tensor) N,5
- metavision_core_ml.preprocessing.event_to_tensor_torch.tensor_to_cd_events(events, batch_size, sort=True)
Convert the event produced by the GPU to our format for later reuse.
- Parameters
events (np.array) – (N,5) in batch_index, x, y, polarity, timestamp (micro-seconds)
batch_size (int) – size of the batch
sort (boolean) – whether to sort event chronologically. You might want to avoid doing it if the algorithm you are using doesn’t rely on them.
- Returns
list of EventCD arrays, of lenght batch_size
Creates a RGB frame representing the events given as input. :param events: structured array containing events :type events: np.ndarray :param height: Height of the sensor in pixels :type height: int :param width: width of the sensor in pixels :type width: int :param img: optional image of size (height, width, 3) and dtype unint8 to avoid reallocation :type img: np.ndarray
- returns
Array of shape (height, width, 3)
- rtype
output_array (np.ndarray)