def subcompute(self, experiment: Experiment, tracker: Tracker,
sequence: Sequence, dependencies: List[Grid]) -> Tuple[Any]:
thresholds = dependencies[0, 0][0][0] # dependencies[0][0, 0]
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
precision = len(thresholds) * [float(0)]
recall = len(thresholds) * [float(0)]
for trajectory in trajectories:
confidence = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
pr, re = compute_tpr_curves(trajectory.regions(), confidence,
sequence, thresholds,
self.ignore_unknown, self.bounded)
for i in range(len(thresholds)):
precision[i] += pr[i]
recall[i] += re[i]
# return [(re / len(trajectories), pr / len(trajectories)) for pr, re in zip(precision, recall)], thresholds
return [(pr / len(trajectories), re / len(trajectories))
for pr, re in zip(precision, recall)], thresholds
def compute_partial(self, tracker: Tracker, experiment: Experiment,
sequence: Sequence):
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
failures = 0
for trajectory in trajectories:
failures = failures + count_failures(trajectory.regions())
return failures / len(trajectories), len(trajectories[0])
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequence: Sequence, dependencies: List[Grid]) -> Tuple[Any]:
assert isinstance(experiment, SupervisedExperiment)
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
failures = 0
for trajectory in trajectories:
failures = failures + count_failures(trajectory.regions())[0]
return failures / len(trajectories), len(trajectories[0])
def compute_partial(self, tracker: Tracker, experiment: Experiment,
sequence: Sequence):
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
accuracy = 0
failures = 0
for trajectory in trajectories:
failures += count_failures(trajectory.regions())[0]
accuracy += compute_accuracy(trajectory.regions(), sequence,
self._burnin, self._ignore_unknown,
self._bounded)[0]
return accuracy / len(trajectories), failures / len(trajectories), len(
trajectories[0])
def compute_partial(self, tracker: Tracker, experiment: Experiment,
sequence: Sequence):
if isinstance(experiment, MultiRunExperiment):
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
cummulative = 0
for trajectory in trajectories:
accuracy, _ = compute_accuracy(trajectory.regions(), sequence,
self._burnin,
self._ignore_unknown,
self._bounded)
cummulative = cummulative + accuracy
return cummulative / len(trajectories), len(sequence)
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequence: Sequence, dependencies: List[Grid]) -> Tuple[Any]:
assert isinstance(experiment, MultiRunExperiment)
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
cummulative = 0
for trajectory in trajectories:
accuracy, _ = compute_accuracy(trajectory.regions(), sequence,
self.burnin, self.ignore_unknown,
self.bounded)
cummulative = cummulative + accuracy
return cummulative / len(trajectories),
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequence: Sequence, dependencies: List[Grid]) -> Tuple[Any]:
scores_all = []
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException(
"Missing results for sequence {}".format(sequence.name))
for trajectory in trajectories:
confidence = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
scores_all.extend(confidence)
return scores_all,
def compute_measure(self, tracker: Tracker, experiment: Experiment):
from vot.region.utils import calculate_overlaps
overlaps_all = []
weights_all = []
success_all = []
for sequence in experiment.workspace.dataset:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
for trajectory in trajectories:
overlaps = calculate_overlaps(
trajectory.regions(), sequence.groundtruth(),
(sequence.size) if self._bounded else None)
fail_idxs, init_idxs = locate_failures_inits(
trajectory.regions())
if len(fail_idxs) > 0:
for i in range(len(fail_idxs)):
overlaps_all.append(
overlaps[init_idxs[i]:fail_idxs[i]])
success_all.append(False)
weights_all.append(1)
# handle last initialization
if len(init_idxs) > len(fail_idxs):
# tracker was initilized, but it has not failed until the end of the sequence
overlaps_all.append(overlaps[init_idxs[-1]:])
success_all.append(True)
weights_all.append(1)
else:
overlaps_all.append(overlaps)
success_all.append(True)
weights_all.append(1)
return compute_eao(overlaps_all, weights_all, success_all,
self._interval_low, self._interval_high)[0]
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequences: List[Sequence],
dependencies: List[Grid]) -> Tuple[Any]:
overlaps_all = []
weights_all = []
success_all = []
for sequence in sequences:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
for trajectory in trajectories:
overlaps = calculate_overlaps(
trajectory.regions(), sequence.groundtruth(),
(sequence.size) if self.bounded else None)
fail_idxs, init_idxs = locate_failures_inits(
trajectory.regions())
if len(fail_idxs) > 0:
for i in range(len(fail_idxs)):
overlaps_all.append(
overlaps[init_idxs[i]:fail_idxs[i]])
success_all.append(False)
weights_all.append(1)
# handle last initialization
if len(init_idxs) > len(fail_idxs):
# tracker was initilized, but it has not failed until the end of the sequence
overlaps_all.append(overlaps[init_idxs[-1]:])
success_all.append(True)
weights_all.append(1)
else:
overlaps_all.append(overlaps)
success_all.append(True)
weights_all.append(1)
return compute_eao_curve(overlaps_all, weights_all, success_all),
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequence: Sequence, dependencies: List[Grid]) -> Tuple[Any]:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
accuracy = 0
failures = 0
for trajectory in trajectories:
failures += count_failures(trajectory.regions())[0]
accuracy += compute_accuracy(trajectory.regions(), sequence,
self.burnin, self.ignore_unknown,
self.bounded)[0]
ar = (math.exp(-(float(failures) / len(trajectories)) *
self.sensitivity), accuracy / len(trajectories))
return accuracy / len(trajectories), failures / len(
trajectories), ar, len(trajectories[0])
def compute_measure(self, tracker: Tracker, experiment: Experiment):
# calculate thresholds
total_scores = 0
for sequence in experiment.workspace.dataset:
trajectories = experiment.gather(tracker, sequence)
for trajectory in trajectories:
total_scores += len(trajectory)
# allocate memory for all scores
scores_all = total_scores * [float(0)]
idx = 0
for sequence in experiment.workspace.dataset:
trajectories = experiment.gather(tracker, sequence)
for trajectory in trajectories:
conf_ = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
scores_all[idx:idx + len(conf_)] = conf_
idx += len(conf_)
thresholds = determine_thresholds(scores_all, self._resolution)
# calculate per-sequence Precision and Recall curves
pr_curves = []
re_curves = []
for sequence in experiment.workspace.dataset:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
pr = len(thresholds) * [float(0)]
re = len(thresholds) * [float(0)]
for trajectory in trajectories:
conf_ = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
pr_, re_ = compute_tpr_curves(trajectory.regions(), conf_,
sequence, thresholds,
self._ignore_unknown,
self._bounded)
pr = [p1 + p2 for p1, p2 in zip(pr, pr_)]
re = [r1 + r2 for r1, r2 in zip(re, re_)]
pr = [p1 / len(trajectories) for p1 in pr]
re = [r1 / len(trajectories) for r1 in re]
pr_curves.append(pr)
re_curves.append(re)
# calculate a single Precision, Recall and F-score curves for a given tracker
# average Pr-Re curves over the sequences
pr_curve = len(thresholds) * [float(0)]
re_curve = len(thresholds) * [float(0)]
for i in range(len(thresholds)):
for j in range(len(pr_curves)):
pr_curve[i] += pr_curves[j][i]
re_curve[i] += re_curves[j][i]
pr_curve = [pr_ / len(pr_curves) for pr_ in pr_curve]
re_curve = [re_ / len(re_curves) for re_ in re_curve]
f_curve = [(2 * pr_ * re_) / (pr_ + re_)
for pr_, re_ in zip(pr_curve, re_curve)]
# get optimal F-score and Pr and Re at this threshold
f_score = max(f_curve)
best_i = f_curve.index(f_score)
pr_score = pr_curve[best_i]
re_score = re_curve[best_i]
return pr_score, re_score, f_score
def subcompute(self, experiment: Experiment, tracker: Tracker,
sequences: List[Sequence]):
# calculate thresholds
total_scores = 0
for sequence in sequences:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException(
"Missing results for sequence {}".format(sequence.name))
for trajectory in trajectories:
total_scores += len(trajectory)
# allocate memory for all scores
scores_all = total_scores * [float(0)]
idx = 0
for sequence in sequences:
trajectories = experiment.gather(tracker, sequence)
for trajectory in trajectories:
conf_ = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
scores_all[idx:idx + len(conf_)] = conf_
idx += len(conf_)
thresholds = determine_thresholds(scores_all, self.resolution)
# calculate per-sequence Precision and Recall curves
pr_curves = []
re_curves = []
for sequence in sequences:
trajectories = experiment.gather(tracker, sequence)
if len(trajectories) == 0:
raise MissingResultsException()
pr = len(thresholds) * [float(0)]
re = len(thresholds) * [float(0)]
for trajectory in trajectories:
conf_ = [
trajectory.properties(i).get('confidence', 0)
for i in range(len(trajectory))
]
pr_, re_ = compute_tpr_curves(trajectory.regions(), conf_,
sequence, thresholds,
self.ignore_unknown,
self.bounded)
pr = [p1 + p2 for p1, p2 in zip(pr, pr_)]
re = [r1 + r2 for r1, r2 in zip(re, re_)]
pr = [p1 / len(trajectories) for p1 in pr]
re = [r1 / len(trajectories) for r1 in re]
pr_curves.append(pr)
re_curves.append(re)
# calculate a single Precision, Recall and F-score curves for a given tracker
# average Pr-Re curves over the sequences
pr_curve = len(thresholds) * [float(0)]
re_curve = len(thresholds) * [float(0)]
for i, _ in enumerate(thresholds):
for j, _ in enumerate(pr_curves):
pr_curve[i] += pr_curves[j][i]
re_curve[i] += re_curves[j][i]
curve = [(re / len(pr_curves), pr / len(pr_curves))
for pr, re in zip(pr_curve, re_curve)]
return curve, thresholds
