def object_stats(self):
object_stats = ObjectStats()
object_stats.update(self._left_time)
object_stats.update(self._num_left_looks)
object_stats.update(self._right_time)
object_stats.update(self._num_right_looks)
init_features = [
'num_left_looks', 'left_time', 'mean_left', 'left_stddev',
'num_right_looks', 'right_time', 'mean_right', 'right_stddev'
]
for obj in objects.OBJECTS:
for feature in init_features:
object_stats.add(obj, feature, 0)
for obj, stats in object_stats.items():
num_left_looks = stats['num_left_looks']
left_time = stats['left_time']
mean_left = left_time / num_left_looks if num_left_looks != 0 else 0
stats['mean_left'] = mean_left
squared_left = self._squared_left.get_stats(obj)['squared_left']
squared_mean = mean_left * mean_left
stddev = (
squared_left / num_left_looks - squared_mean
if num_left_looks != 0 else 0
)
stats['left_stddev'] = stddev
num_right_looks = stats['num_right_looks']
right_time = stats['right_time']
mean_right = right_time / num_right_looks if num_right_looks != 0 else 0
stats['mean_right'] = mean_right
squared_right = self._squared_right.get_stats(obj)['squared_right']
squared_mean = mean_right * mean_right
stddev = (
squared_right / num_right_looks - squared_mean
if num_right_looks != 0 else 0
)
stats['right_stddev'] = stddev
return object_stats
def process_experiment((path, object_timeline)):
"""Extract features of the bag file."""
print('Processing', path)
bag = open_bag(path)
if bag is None:
return None
object_stats = ObjectStats()
time_taken_processor = processors.TimeTaken(object_timeline)
camera_movement_processor = processors.CameraMovementTime(object_timeline)
marker_movement_processor = processors.MarkerMovementTime(object_timeline)
grasp_count_processor = processors.GraspCount(object_timeline)
for topic, message, time in bag.read_messages(topics=EXPERIMENT_TOPICS):
model = message_factory.model(message)
if model is None:
continue
time_taken_processor.update(topic, model, time)
camera_movement_processor.update(topic, model, time)
marker_movement_processor.update(topic, model, time)
grasp_count_processor.update(topic, model, time)
bag.close()
time_taken_processor.update_last()
object_stats.update(time_taken_processor.object_stats())
object_stats.update(camera_movement_processor.object_stats())
object_stats.update(marker_movement_processor.object_stats())
object_stats.update(grasp_count_processor.object_stats())
for obj, stats in object_stats.items():
stats['other_time'] = (
stats['time_taken'] - stats['camera_movement_time'] -
stats['marker_movement_time']
)
timeline = build_timeline(
marker_movement_processor.timeline(),
camera_movement_processor.timeline())
return object_stats, timeline
