def classify(self, *args, **kwargs):
"""
classify eye movement
"""
if self.g_pool.app == "exporter":
return
utils.logger.info("Gaze postions changed. Recalculating.")
if self.eye_movement_task and self.eye_movement_task.running:
self.eye_movement_task.kill(grace_period=1)
capture = model.Immutable_Capture(self.g_pool.capture)
min_data_confidence = self.min_data_confidence
gaze_data: utils.Gaze_Data = [
gp.serialized for gp in self.g_pool.gaze_positions
if gp["confidence"] > min_data_confidence
]
self.eye_movement_task = worker.Offline_Detection_Task(
args=(capture, gaze_data))
self.task_manager.add_task(self.eye_movement_task)
self.eye_movement_task.add_observers(
on_started=self._on_task_started,
on_yield=self._on_task_yield,
on_completed=self._on_task_completed,
on_ended=self._on_task_ended,
on_exception=self._on_task_exception,
on_canceled_or_killed=self._on_task_canceled_or_killed,
)
self.eye_movement_task.start()
def recent_events(self, events):
gaze_data = events["gaze"]
capture = model.Immutable_Capture(self.g_pool.capture)
min_data_confidence = self.min_data_confidence
gaze_data = [
datum for datum in gaze_data
if datum["confidence"] > min_data_confidence
]
self._buffered_detector.extend_gaze_data(gaze_data=gaze_data,
capture=capture)
if "frame" not in events:
return
frame_timestamp = events["frame"].timestamp
self._recent_segments = self._buffered_detector.segments_at_timestamp(
frame_timestamp)
public_segments = [
segment.to_public_dict() for segment in self._recent_segments
]
events[utils.EYE_MOVEMENT_EVENT_KEY] = public_segments