def post_optim_step(self, optim_motions):
""" When each time an optimized motion arrive, we have to update the corresponding motion and predicted bbox location
"""
if len(optim_motions.shape) == 1:
optim_motions = optim_motions.reshape((-1, 4))
for i in range(1, self.size):
self.motion_buffer.set_val(i, optim_motions[i - 1])
prev_bbox = self.state_buffer.access(i - 1)
next_bbox = BBox.motion2bbox(prev_bbox, optim_motions[i - 1])
self.state_buffer.set_val(i, next_bbox)
return
def pre_frame_optim(self, pred_motion):
""" before optimizing each frame, motion model takes in a roughly predicted motion
Args:
pred_motion (np.ndarray): a rough prediction for the motion
"""
# When encountering frame 0
# push the start_bbox and the dummy pred_motion into the buffer
if self.size == 0:
self.state_buffer.push(self.start_state)
self.motion_buffer.push(pred_motion)
# At other frames, the predicted motion is right
else:
prev_bbox = self.state_buffer.last()
pred_bbox = BBox.motion2bbox(prev_bbox, pred_motion)
self.state_buffer.push(pred_bbox)
self.motion_buffer.push(pred_motion)
self.size = self.state_buffer.size
return