def __init__(
self, dataset_dir: str, experiment_prefix: str, use_existing_files: bool = True, log_id: str = None
) -> None:
"""We will cache the accumulated trajectories per city, per log, and per frame
for the tracking benchmark.
"""
self.plot_lane_tangent_arrows = True
self.plot_lidar_bev = True
self.plot_lidar_in_img = False
self.experiment_prefix = experiment_prefix
self.dataset_dir = dataset_dir
self.labels_dir = dataset_dir
self.sdb = SynchronizationDB(self.dataset_dir)
if log_id is None:
tmp_dir = tempfile.gettempdir()
per_city_traj_dict_fpath = f"{tmp_dir}/per_city_traj_dict_{experiment_prefix}.pkl"
log_egopose_dict_fpath = f"{tmp_dir}/log_egopose_dict_{experiment_prefix}.pkl"
log_timestamp_dict_fpath = f"{tmp_dir}/log_timestamp_dict_{experiment_prefix}.pkl"
if not use_existing_files:
# write the accumulate data dictionaries to disk
PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix)
self.per_city_traj_dict = load_pkl_dictionary(per_city_traj_dict_fpath)
self.log_egopose_dict = load_pkl_dictionary(log_egopose_dict_fpath)
self.log_timestamp_dict = load_pkl_dictionary(log_timestamp_dict_fpath)
else:
pfa = PerFrameLabelAccumulator(dataset_dir, dataset_dir, experiment_prefix, save=False)
pfa.accumulate_per_log_data(log_id=log_id)
self.per_city_traj_dict = pfa.per_city_traj_dict
self.log_egopose_dict = pfa.log_egopose_dict
self.log_timestamp_dict = pfa.log_timestamp_dict
def test_load_pickle_from_disk() -> None:
"""Load a pickle file from disk.
The file should contain the following Python dictionary:
{'a': 1, 'b':'2', 'c':[9,8,7,6,5,'d','c','b','a'], 'd': np.array([True,False,True]) }
We demonstrate that we can load Numpy arrays and lists from Pickle files.
"""
gt_dict = {
"a": 1,
"b": "2",
"c": [9, 8, 7, 6, 5, "d", "c", "b", "a"],
"d": np.array([True, False, True])
}
pkl_fpath = _TEST_DIR / "test_data/pkl_test_file.pkl"
loaded_pkl_dict = load_pkl_dictionary(pkl_fpath)
dictionaries_are_equal(gt_dict, loaded_pkl_dict)