def track(self, frame):
self._tracker = SiamRPN_track(self._tracker, frame)
target_pos = self._tracker['target_pos']
target_sz = self._tracker['target_sz']
bbox = (int(target_pos[0] - target_sz[0] / 2.0),
int(target_pos[0] + target_sz[0] / 2.0),
int(target_pos[1] - target_sz[1] / 2.0),
int(target_pos[1] + target_sz[1] / 2.0))
return bbox
def update(self, next_image):
"""
Update box position and size on next_image. Returns True
beacuse tracking is terminated based on number of frames predicted
in OpenLabeling, not based on feedback from tracking algorithm (unlike
the opencv tracking algorithms).
"""
self.state = SiamRPN_track(self.state, next_image)
target_pos = self.state["target_pos"]
target_sz = self.state["target_sz"]
bbox = self.pos_to_bbox(target_pos, target_sz)
return True, bbox
def track(self, frame):
""" Tracks obstacles in a frame.
Args:
frame (:py:class:`~pylot.perception.camera_frame.CameraFrame`):
Frame to track in.
"""
self._tracker = SiamRPN_track(self._tracker, frame.frame)
target_pos = self._tracker['target_pos']
target_sz = self._tracker['target_sz']
self.obstacle.bounding_box = BoundingBox2D(
int(target_pos[0] - target_sz[0] / 2.0),
int(target_pos[0] + target_sz[0] / 2.0),
int(target_pos[1] - target_sz[1] / 2.0),
int(target_pos[1] + target_sz[1] / 2.0))
return Obstacle(self.obstacle.bounding_box, self.obstacle.confidence,
self.obstacle.label, self.obstacle.id)