def identify_track(self, clip: Clip, track: Track):
"""
Runs through track identifying segments, and then returns it's prediction of what kind of animal this is.
One prediction will be made for every frame.
:param track: the track to identify.
:return: TrackPrediction object
"""
# uniform prior stats start with uniform distribution. This is the safest bet, but means that
# it takes a while to make predictions. When off the first prediction is used instead causing
# faster, but potentially more unstable predictions.
UNIFORM_PRIOR = False
num_labels = len(self.classifier.labels)
prediction_smooth = 0.1
smooth_prediction = None
smooth_novelty = None
prediction = 0.0
novelty = 0.0
try:
fp_index = self.classifier.labels.index("false-positive")
except ValueError:
fp_index = None
# go through making classifications at each frame
# note: we should probably be doing this every 9 frames or so.
state = None
track_prediction = self.predictions.get_or_create_prediction(track)
for i, region in enumerate(track.bounds_history):
frame = clip.frame_buffer.get_frame(region.frame_number)
track_data = track.crop_by_region(frame, region)
# note: would be much better for the tracker to store the thermal references as it goes.
# frame = clip.frame_buffer.get_frame(frame_number)
thermal_reference = np.median(frame.thermal)
# track_data = track.crop_by_region_at_trackframe(frame, i)
if i % self.FRAME_SKIP == 0:
# we use a tighter cropping here so we disable the default 2 pixel inset
frames = Preprocessor.apply([track_data], [thermal_reference],
default_inset=0)
if frames is None:
logging.info(
"Frame {} of track could not be classified.".format(
region.frame_number))
return
frame = frames[0]
(
prediction,
novelty,
state,
) = self.classifier.classify_frame_with_novelty(frame, state)
# make false-positive prediction less strong so if track has dead footage it won't dominate a strong
# score
if fp_index is not None:
prediction[fp_index] *= 0.8
# a little weight decay helps the model not lock into an initial impression.
# 0.98 represents a half life of around 3 seconds.
state *= 0.98
# precondition on weight, segments with small mass are weighted less as we can assume the error is
# higher here.
mass = region.mass
# we use the square-root here as the mass is in units squared.
# this effectively means we are giving weight based on the diameter
# of the object rather than the mass.
mass_weight = np.clip(mass / 20, 0.02, 1.0)**0.5
# cropped frames don't do so well so restrict their score
cropped_weight = 0.7 if region.was_cropped else 1.0
prediction *= mass_weight * cropped_weight
if smooth_prediction is None:
if UNIFORM_PRIOR:
smooth_prediction = np.ones([num_labels
]) * (1 / num_labels)
else:
smooth_prediction = prediction
smooth_novelty = 0.5
else:
smooth_prediction = (
1 - prediction_smooth
) * smooth_prediction + prediction_smooth * prediction
smooth_novelty = (
1 - prediction_smooth
) * smooth_novelty + prediction_smooth * novelty
track_prediction.classified_frame(region.frame_number,
smooth_prediction,
smooth_novelty)
return track_prediction
def identify_track(self, clip: Clip, track: Track):
"""
Runs through track identifying segments, and then returns it's prediction of what kind of animal this is.
One prediction will be made for every frame.
:param track: the track to identify.
:return: TrackPrediction object
"""
# go through making classifications at each frame
# note: we should probably be doing this every 9 frames or so.
state = None
if self.kerasmodel:
track_prediction = self.classifier.classify_track(clip, track)
self.predictions.prediction_per_track[
track.get_id()] = track_prediction
else:
track_prediction = self.predictions.get_or_create_prediction(track)
for i, region in enumerate(track.bounds_history):
frame = clip.frame_buffer.get_frame(region.frame_number)
track_data = track.crop_by_region(frame, region)
# note: would be much better for the tracker to store the thermal references as it goes.
# frame = clip.frame_buffer.get_frame(frame_number)
thermal_reference = np.median(frame.thermal)
# track_data = track.crop_by_region_at_trackframe(frame, i)
if i % self.FRAME_SKIP == 0:
# we use a tighter cropping here so we disable the default 2 pixel inset
frames = preprocess_segment([track_data],
[thermal_reference],
default_inset=0)
if frames is None:
logging.info(
"Frame {} of track could not be classified.".
format(region.frame_number))
continue
frame = frames[0]
(
prediction,
novelty,
state,
) = self.classifier.classify_frame_with_novelty(
frame, state)
# make false-positive prediction less strong so if track has dead footage it won't dominate a strong
# score
# a little weight decay helps the model not lock into an initial impression.
# 0.98 represents a half life of around 3 seconds.
state *= 0.98
# precondition on weight, segments with small mass are weighted less as we can assume the error is
# higher here.
mass = region.mass
# we use the square-root here as the mass is in units squared.
# this effectively means we are giving weight based on the diameter
# of the object rather than the mass.
mass_weight = np.clip(mass / 20, 0.02, 1.0)**0.5
# cropped frames don't do so well so restrict their score
cropped_weight = 0.7 if region.was_cropped else 1.0
track_prediction.classified_frame(
region.frame_number,
prediction,
mass_scale=mass_weight * cropped_weight,
novelty=novelty,
)
return track_prediction
