def test_assembly():
ass = inferenceutils.Assembly(3)
assert len(ass) == 0
j1 = inferenceutils.Joint((1, 1), label=0)
j2 = inferenceutils.Joint((1, 1), label=1)
assert ass.add_link(inferenceutils.Link(j1, j2), store_dict=True)
assert len(ass) == 2
assert ass.data[j2.label, 0] == 1
assert ass.data[j2.label, -1] == -1
assert ass.area == 0
assert ass.intersection_with(ass) == 1.0
assert np.all(np.isnan(ass._dict["data"]))
ass.remove_joint(j2)
assert len(ass) == 1
assert np.all(np.isnan(ass.data[j2.label]))
ass2 = inferenceutils.Assembly(2)
ass2.add_link(inferenceutils.Link(j1, j2))
with pytest.raises(ValueError):
_ = ass + ass2
ass2.remove_joint(j1)
assert ass2 not in ass
ass3 = ass + ass2
assert len(ass3) == 2
def find_outliers_in_raw_data(
config,
pickle_file,
video_file,
pcutoff=0.1,
percentiles=(5, 95),
with_annotations=True,
extraction_algo="kmeans",
):
"""
Extract outlier frames from either raw detections or assemblies of multiple animals.
Parameter
----------
config : str
Absolute path to the project config.yaml.
pickled_file : str
Path to a *_full.pickle or *_assemblies.pickle.
video_file : str
Path to the corresponding video file for frame extraction.
pcutoff : float, optional (default=0.1)
Detection confidence threshold below which frames are flagged as
containing outliers. Only considered if raw detections are passed in.
percentiles : tuple, optional (default=(5, 95))
Assemblies are considered outliers if their areas are beyond the 5th
and 95th percentiles. Must contain a lower and upper bound.
with_annotations : bool, optional (default=True)
If true, extract frames and the corresponding network predictions.
Otherwise, only the frames are extracted.
extraction_algo : string, optional (default="kmeans")
Outlier detection algorithm. Must be either ``uniform`` or ``kmeans``.
"""
if extraction_algo not in ("kmeans", "uniform"):
raise ValueError(
f"Unsupported extraction algorithm {extraction_algo}.")
video_name = Path(video_file).stem
pickle_name = Path(pickle_file).stem
if not pickle_name.startswith(video_name):
raise ValueError("Video and pickle files do not match.")
with open(pickle_file, "rb") as file:
data = pickle.load(file)
if pickle_file.endswith("_full.pickle"):
inds, data = find_outliers_in_raw_detections(data, threshold=pcutoff)
with_annotations = False
elif pickle_file.endswith("_assemblies.pickle"):
assemblies = dict()
for k, lst in data.items():
if k == "single":
continue
ass = []
for vals in lst:
a = inferenceutils.Assembly(len(vals))
a.data = vals
ass.append(a)
assemblies[k] = ass
inds = inferenceutils.find_outlier_assemblies(assemblies,
qs=percentiles)
else:
raise IOError(f"Raw data file {pickle_file} could not be parsed.")
cfg = auxiliaryfunctions.read_config(config)
ExtractFramesbasedonPreselection(
inds,
extraction_algo,
data,
video=video_file,
cfg=cfg,
config=config,
savelabeled=False,
with_annotations=with_annotations,
)