img = np.zeros([260, 346])
img[img_on > 0] = 255
img[img_off > 0] = 255
print('*************', img.shape)
# image = cv2.cvtColor(img[None,:], cv2.COLOR_GRAY2RGB)
# print('+++++++++++', image.shape)
image = np.vstack((img, img, img))
print(image.shape)
img = np.reshape(image, (3, 260, 346))
img = img.transpose(1, 2, 0)
print(img.shape)
cv2.imshow("image", img / float(clip_value * 2))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
pass
except KeyboardInterrupt:
device.shutdown()
break
num_pol_event,
special_events,
num_special_event,
frames_ts,
frames,
imu_events,
num_imu_event,
) = data
if pol_events is not None:
event_batch = np.concatenate(
(event_batch, pol_events))
event_batch_size += num_pol_event
except KeyboardInterrupt:
dvs_device.shutdown()
break
event_batch = event_batch[1:, 0:4]
event_tensor_pd = pd.DataFrame(data=event_batch,
columns=["t", "x", "y", "pol"])
event_tensor_pd["t"] *= 1e-6
event_tensor_pd["x"].astype("int16")
event_tensor_pd["y"].astype("int16")
event_tensor_pd["pol"].astype("int16")
last_timestamp = event_tensor_pd.values[-1, 0]
with Timer("Building event tensor"):
if args.compute_voxel_grid_on_cpu: