def test_matching_adaptor():
from sleap.io.format import read
read(
"tests/data/hdf5_format_v1/centered_pair_predictions.h5",
for_object="labels",
as_format="*",
)
read(
"tests/data/json_format_v1/centered_pair.json",
for_object="labels",
as_format="*",
)
def test_madlc():
labels = read(
"tests/data/dlc/madlc_testdata.csv",
for_object="labels",
as_format="deeplabcut",
)
assert labels.skeleton.node_names == ["A", "B", "C"]
assert len(labels) == 3
assert len(labels[0]) == 2
assert len(labels[1]) == 2
assert len(labels[2]) == 1
assert_array_equal(labels[0][0].numpy(), [[0, 1], [2, 3], [4, 5]])
assert_array_equal(labels[0][1].numpy(), [[6, 7], [8, 9], [10, 11]])
assert_array_equal(labels[1][0].numpy(),
[[12, 13], [np.nan, np.nan], [15, 16]])
assert_array_equal(labels[1][1].numpy(),
[[17, 18], [np.nan, np.nan], [20, 21]])
assert_array_equal(labels[2][0].numpy(), [[22, 23], [24, 25], [26, 27]])
def test_analysis_hdf5(tmpdir, centered_pair_predictions):
from sleap.info.write_tracking_h5 import main as write_analysis
filename = os.path.join(tmpdir, "analysis.h5")
video = centered_pair_predictions.videos[0]
write_analysis(centered_pair_predictions,
output_path=filename,
all_frames=True)
labels = read(
filename,
for_object="labels",
as_format="analysis",
video=video,
)
assert len(labels) == len(centered_pair_predictions)
assert len(labels.tracks) == len(centered_pair_predictions.tracks)
assert len(labels.all_instances) == len(
centered_pair_predictions.all_instances)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("input_path", help="Path to input file.")
parser.add_argument(
"-o", "--output", default="", help="Path to output file (optional)."
)
parser.add_argument(
"--format",
default="slp",
help="Output format. Default ('slp') is SLEAP dataset; "
"'analysis' results in analysis.h5 file; "
"'h5' or 'json' results in SLEAP dataset "
"with specified file format.",
)
parser.add_argument(
"--video", default="", help="Path to video (if needed for conversion)."
)
args = parser.parse_args()
video_callback = Labels.make_video_callback([os.path.dirname(args.input_path)])
try:
labels = Labels.load_file(args.input_path, video_search=video_callback)
except TypeError:
print("Input file isn't SLEAP dataset so attempting other importers...")
from sleap.io.format import read
video_path = args.video if args.video else None
labels = read(
args.input_path,
for_object="labels",
as_format="*",
video_search=video_callback,
video=video_path,
)
if args.format == "analysis":
from sleap.info.write_tracking_h5 import main as write_analysis
if args.output:
output_path = args.output
else:
output_path = args.input_path
output_path = re.sub("(\.json(\.zip)?|\.h5|\.slp)$", "", output_path)
output_path = output_path + ".analysis.h5"
write_analysis(labels, output_path=output_path, all_frames=True)
elif args.output:
print(f"Output SLEAP dataset: {args.output}")
Labels.save_file(labels, args.output)
elif args.format in ("slp", "h5", "json"):
output_path = f"{args.input_path}.{args.format}"
print(f"Output SLEAP dataset: {output_path}")
Labels.save_file(labels, output_path)
else:
print("You didn't specify how to convert the file.")
print(args)