def __init__(self,
nusc: NuScenes,
config: TrackingConfig,
result_path: str,
eval_set: str,
output_dir: str = None,
verbose: bool = True):
"""
Initialize a TrackingEval object.
:param nusc: A NuScenes object.
:param config: A TrackingConfig object.
:param result_path: Path of the nuScenes JSON result file.
:param eval_set: The dataset split to evaluate on, e.g. train, val or test.
:param output_dir: Folder to save plots and results to.
:param verbose: Whether to print to stdout.
"""
self.cfg = config
self.result_path = result_path
self.eval_set = eval_set
self.output_dir = output_dir
self.verbose = verbose
# Check result file exists.
assert os.path.exists(
result_path), 'Error: The result file does not exist!'
# Make dirs.
self.plot_dir = os.path.join(self.output_dir, 'plots')
if not os.path.isdir(self.output_dir):
os.makedirs(self.output_dir)
if not os.path.isdir(self.plot_dir):
os.makedirs(self.plot_dir)
# Load data.
if verbose:
print('Initializing nuScenes tracking evaluation')
pred_boxes, self.meta = load_prediction(self.result_path,
self.cfg.max_boxes_per_sample,
TrackingBox,
verbose=verbose)
gt_boxes = load_gt(nusc, self.eval_set, TrackingBox, verbose=verbose)
assert set(pred_boxes.sample_tokens) == set(gt_boxes.sample_tokens), \
"Samples in split don't match samples in predicted tracks."
# Add center distances.
pred_boxes = add_center_dist(nusc, pred_boxes)
gt_boxes = add_center_dist(nusc, gt_boxes)
# Filter boxes (distance, points per box, etc.).
if verbose:
print('Filtering tracks')
pred_boxes = filter_eval_boxes(nusc,
pred_boxes,
self.cfg.class_range,
verbose=verbose)
if verbose:
print('Filtering ground truth tracks')
gt_boxes = filter_eval_boxes(nusc,
gt_boxes,
self.cfg.class_range,
verbose=verbose)
self.sample_tokens = gt_boxes.sample_tokens
# Convert boxes to tracks format.
self.tracks_gt = create_tracks(gt_boxes, nusc, self.eval_set, gt=True)
self.tracks_pred = create_tracks(pred_boxes,
nusc,
self.eval_set,
gt=False)
nusc = NuScenes(version=version, dataroot=data_root, verbose=True)
pred_boxes, _ = load_prediction(detection_file, 10000, DetectionBox)
gt_boxes = load_gt(nusc, eval_set_, TrackingBox)
assert set(pred_boxes.sample_tokens) == set(gt_boxes.sample_tokens), \
"Samples in split don't match samples in predicted tracks."
# Add center distances.
pred_boxes = add_center_dist(nusc, pred_boxes)
gt_boxes = add_center_dist(nusc, gt_boxes)
print('len(pred_boxes.sample_tokens): ', len(pred_boxes.sample_tokens))
print('len(gt_boxes.sample_tokens): ', len(gt_boxes.sample_tokens))
tracks_gt = create_tracks(gt_boxes, nusc, eval_set_, gt=True)
mean, std, var = get_mean(tracks_gt)
print('GT: Global coordinate system')
print(
'h, w, l, x, y, z, a, x_dot, y_dot, z_dot, a_dot, x_dot_dot, y_dot_dot, z_dot_dot, a_dot_dot'
)
print('mean: ', mean)
print('std: ', std)
print('var: ', var)
# for observation noise covariance
mean, std, var, mean_vel, std_vel, var_vel = matching_and_get_diff_stats(
pred_boxes, gt_boxes, tracks_gt, matching_dist)
print('Diff: Global coordinate system')
print('h, w, l, x, y, z, a')
