def eye_movement_detection_generator(
capture: model.Immutable_Capture,
gaze_data: utils.Gaze_Data,
factory_start_id: int = None,
) -> Offline_Detection_Task_Generator:
def serialized_dict(datum):
if type(datum) is dict:
return fm.Serialized_Dict(python_dict=datum)
elif type(datum) is bytes:
return fm.Serialized_Dict(msgpack_bytes=datum)
else:
raise ValueError("Unsupported gaze datum type: {}.".format(type(datum)))
yield EYE_MOVEMENT_DETECTION_STEP_PREPARING_LOCALIZED_STRING, ()
gaze_data = [serialized_dict(datum) for datum in gaze_data]
if not gaze_data:
utils.logger.warning("No data available to find fixations")
yield EYE_MOVEMENT_DETECTION_STEP_COMPLETE_LOCALIZED_STRING, ()
return
use_pupil = utils.can_use_3d_gaze_mapping(gaze_data)
segment_factory = model.Classified_Segment_Factory(start_id=factory_start_id)
gaze_time = np.array([gp["timestamp"] for gp in gaze_data])
yield EYE_MOVEMENT_DETECTION_STEP_PROCESSING_LOCALIZED_STRING, ()
eye_positions = utils.gaze_data_to_nslr_data(
capture, gaze_data, gaze_time, use_pupil=use_pupil
)
yield EYE_MOVEMENT_DETECTION_STEP_CLASSIFYING_LOCALIZED_STRING, ()
gaze_classification, segmentation, segment_classification = nslr_hmm.classify_gaze(
gaze_time, eye_positions
)
# `gaze_classification` holds the classification for each gaze datum.
yield EYE_MOVEMENT_DETECTION_STEP_DETECTING_LOCALIZED_STRING, ()
for i, nslr_segment in enumerate(segmentation.segments):
nslr_segment_class = segment_classification[i]
segment = segment_factory.create_segment(
gaze_data=gaze_data,
gaze_time=gaze_time,
use_pupil=use_pupil,
nslr_segment=nslr_segment,
nslr_segment_class=nslr_segment_class,
)
if not segment:
continue
serialized = segment.to_msgpack()
yield EYE_MOVEMENT_DETECTION_STEP_DETECTING_LOCALIZED_STRING, serialized
yield EYE_MOVEMENT_DETECTION_STEP_COMPLETE_LOCALIZED_STRING, ()
def __init__(self,
max_segment_count: int = 1,
max_sample_count: int = 1000):
self._capture = None
self._gaze_data_buffer = stdlib_utils.sliceable_deque(
[], maxlen=max_sample_count)
self._segment_buffer = stdlib_utils.sliceable_deque(
[], maxlen=max_segment_count)
self._segment_factory = model.Classified_Segment_Factory()
self._is_gaze_buffer_classified: bool = True
def _segment_generator(
capture: model.Immutable_Capture,
gaze_data: utils.Gaze_Data,
factory_start_id: int = None,
):
# TODO: Merge this version with the one in offline_detection_task
if len(gaze_data) < 2:
utils.logger.warning("Not enough data available to find fixations")
return
use_pupil = utils.can_use_3d_gaze_mapping(gaze_data)
segment_factory = model.Classified_Segment_Factory(
start_id=factory_start_id)
gaze_time = np.array([gp["timestamp"] for gp in gaze_data])
try:
eye_positions = utils.gaze_data_to_nslr_data(capture,
gaze_data,
gaze_time,
use_pupil=use_pupil)
except utils.NSLRValidationError as e:
utils.logger.error(f"{e}")
return
gaze_classification, segmentation, segment_classification = nslr_hmm.classify_gaze(
gaze_time, eye_positions)
# by-gaze clasification, modifies events["gaze"] by reference
for gaze, classification in zip(gaze_data, gaze_classification):
gaze[utils.
EYE_MOVEMENT_GAZE_KEY] = model.Segment_Class.from_nslr_class(
classification).value
# by-segment classification
for i, nslr_segment in enumerate(segmentation.segments):
nslr_segment_class = segment_classification[i]
segment = segment_factory.create_segment(
gaze_data=gaze_data,
gaze_time=gaze_time,
use_pupil=use_pupil,
nslr_segment=nslr_segment,
nslr_segment_class=nslr_segment_class,
world_timestamps=capture.timestamps,
)
if not segment:
continue
yield segment
def _segment_generator(
capture: model.Immutable_Capture,
gaze_data: utils.Gaze_Data,
factory_start_id: int = None,
):
# TODO: Merge this version with the one in offline_detection_task
if not gaze_data:
utils.logger.warning("No data available to find fixations")
return
use_pupil = utils.can_use_3d_gaze_mapping(gaze_data)
segment_factory = model.Classified_Segment_Factory(start_id=factory_start_id)
gaze_time = np.array([gp["timestamp"] for gp in gaze_data])
eye_positions = utils.gaze_data_to_nslr_data(
capture, gaze_data, use_pupil=use_pupil
)
gaze_classification, segmentation, segment_classification = nslr_hmm.classify_gaze(
gaze_time, eye_positions
)
for i, nslr_segment in enumerate(segmentation.segments):
nslr_segment_class = segment_classification[i]
segment = segment_factory.create_segment(
gaze_data=gaze_data,
gaze_time=gaze_time,
use_pupil=use_pupil,
nslr_segment=nslr_segment,
nslr_segment_class=nslr_segment_class,
)
if not segment:
continue
yield segment