def from_labels(
cls,
training: Union[Text, sleap.Labels],
validation: Union[Text, sleap.Labels, float],
test: Optional[Union[Text, sleap.Labels]] = None,
video_search_paths: Optional[List[Text]] = None,
) -> "DataReaders":
"""Create data readers from sleap.Labels datasets as data providers."""
if isinstance(training, str):
print("video search paths: ", video_search_paths)
training = sleap.Labels.load_file(training,
video_search=video_search_paths)
print(training.videos)
if isinstance(validation, str):
validation = sleap.Labels.load_file(
validation, video_search=video_search_paths)
elif isinstance(validation, float):
training, validation = split_labels(training, [-1, validation])
if isinstance(test, str):
test = sleap.Labels.load_file(test,
video_search=video_search_paths)
test_reader = None
if test is not None:
test_reader = LabelsReader.from_user_instances(test)
return cls(
training_labels_reader=LabelsReader.from_user_instances(training),
validation_labels_reader=LabelsReader.from_user_instances(
validation),
test_labels_reader=test_reader,
)
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 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)