def predict(
self,
data_provider: Provider,
make_instances: bool = True,
make_labels: bool = False,
):
t0_gen = time.time()
if isinstance(data_provider, sleap.Labels):
data_provider = LabelsReader(data_provider)
elif isinstance(data_provider, sleap.Video):
data_provider = VideoReader(data_provider)
generator = self.predict_generator(data_provider)
if make_instances or make_labels:
lfs = self.make_labeled_frames_from_generator(generator, data_provider)
elapsed = time.time() - t0_gen
logger.info(
f"Predicted {len(lfs)} labeled frames in {elapsed:.3f} secs [{len(lfs)/elapsed:.1f} FPS]"
)
if make_labels:
return sleap.Labels(lfs)
else:
return lfs
else:
examples = list(generator)
elapsed = time.time() - t0_gen
logger.info(
f"Predicted {len(examples)} examples in {elapsed:.3f} secs [{len(examples)/elapsed:.1f} FPS]"
)
return examples
def make_video_readers_from_cli(args) -> List[VideoReader]:
if args.video_path:
# TODO: better support for video params
video_kwargs = dict(
dataset=vars(args).get("video.dataset"),
input_format=vars(args).get("video.input_format"),
)
video_reader = VideoReader.from_filepath(filename=args.video_path,
example_indices=args.frames,
**video_kwargs)
return [video_reader]
if args.labels:
labels = sleap.Labels.load_file(args.labels)
readers = []
if args.only_labeled_frames:
user_labeled_frames = labels.user_labeled_frames
else:
user_labeled_frames = []
for video in labels.videos:
if args.only_labeled_frames:
frame_indices = [
lf.frame_idx for lf in user_labeled_frames
if lf.video == video
]
readers.append(
VideoReader(video=video, example_indices=frame_indices))
elif args.only_suggested_frames:
readers.append(
VideoReader(
video=video,
example_indices=labels.get_video_suggestions(video)))
else:
readers.append(VideoReader(video=video))
return readers
raise ValueError(
"You must specify either video_path or labels dataset path.")
def predict(
self,
data_provider: Provider,
make_instances: bool = True,
make_labels: bool = False,
):
if isinstance(data_provider, sleap.Labels):
data_provider = LabelsReader(data_provider)
elif isinstance(data_provider, sleap.Video):
data_provider = VideoReader(data_provider)
generator = self.predict_generator(data_provider)
if make_instances or make_labels:
lfs = self.make_labeled_frames_from_generator(generator, data_provider)
if make_labels:
return sleap.Labels(lfs)
else:
return lfs
return list(generator)