def calc_accuracy(gt_trajectory,
pred_trajectory,
burnin=10,
ignore_unknown=True,
video_wh=None):
"""Calculate accuracy over the sequence.
Args:
gt_trajectory (list[list]): list of bboxes
pred_trajectory (list[ndarray]): The outer list contains the
tracking results of each frame in one video. The ndarray has two
cases:
- bbox: denotes the normal tracking box in [x1, y1, w, h]
format.
- special tracking state: [0] denotes the unknown state,
namely the skipping frame after failure, [1] denotes the
initialized state, and [2] denotes the failed state.
burnin: number of frames that have to be ignored after the
re-initialization when calculating accuracy. Default is 10.
ignore_unknown (bool): whether ignore the skipping frames after
failures when calculating accuracy. Default is True.
video_wh: bounding region (width, height)
Return:
Float: accuracy over the sequence.
"""
pred_traj_region = trajectory2region(pred_trajectory)
gt_traj_region = trajectory2region(gt_trajectory)
overlaps = np.array(
calculate_region_overlaps(pred_traj_region, gt_traj_region, video_wh))
mask = np.ones(len(overlaps), dtype=bool)
for i, region in enumerate(pred_traj_region):
if is_special(region, Special.UNKNOWN) and ignore_unknown:
mask[i] = False
elif is_special(region, Special.INITIALIZATION):
for j in range(i, min(len(pred_traj_region), i + burnin)):
mask[j] = False
elif is_special(region, Special.FAILURE):
mask[i] = False
return np.mean(overlaps[mask]) if any(mask) else 0.
def compute_accuracy(trajectory: List[Region],
sequence: Sequence,
burnin: int = 10,
ignore_unknown: bool = True,
bounded: bool = True) -> float:
overlaps = np.array(
calculate_overlaps(trajectory, sequence.groundtruth(),
(sequence.size) if bounded else None))
mask = np.ones(len(overlaps), dtype=bool)
for i, region in enumerate(trajectory):
if is_special(region, Special.UNKNOWN) and ignore_unknown:
mask[i] = False
elif is_special(region, Special.INITIALIZATION):
for j in range(i, min(len(trajectory), i + burnin)):
mask[j] = False
elif is_special(region, Special.FAILURE):
mask[i] = False
return np.mean(overlaps[mask]), np.sum(mask)
def locate_failures_inits(trajectory: List[Region]) -> Tuple[int, int]:
return [i for i, region in enumerate(trajectory) if is_special(region, Special.FAILURE)], \
[i for i, region in enumerate(trajectory) if is_special(region, Special.INITIALIZATION)]
def count_failures(trajectory: List[Region]) -> Tuple[int, int]:
return len([
region for region in trajectory if is_special(region, Special.FAILURE)
]), len(trajectory)
