def __init__(self,
ground_truth_filename=None,
proposal_filename=None,
ground_truth_fields=GROUND_TRUTH_FIELDS,
proposal_fields=PROPOSAL_FIELDS,
tiou_thresholds=np.linspace(0.5, 0.95, 10),
max_avg_nr_proposals=None,
subset='validation',
verbose=False,
check_status=False):
if not ground_truth_filename:
raise IOError('Please input a valid ground truth file.')
if not proposal_filename:
raise IOError('Please input a valid proposal file.')
self.subset = subset
self.tiou_thresholds = tiou_thresholds
self.max_avg_nr_proposals = max_avg_nr_proposals
self.verbose = verbose
self.gt_fields = ground_truth_fields
self.pred_fields = proposal_fields
self.recall = None
self.avg_recall = None
self.proposals_per_video = None
self.check_status = check_status
# Retrieve blocked videos from server.
if self.check_status:
self.blocked_videos = get_blocked_videos()
else:
self.blocked_videos = list()
# Import ground truth and proposals.
self.ground_truth, self.activity_index = self._import_ground_truth(
ground_truth_filename)
self.proposal = self._import_proposal(proposal_filename)
if self.verbose:
print('[INIT] Loaded annotations from {} subset.'.format(subset))
nr_gt = len(self.ground_truth)
print('\tNumber of ground truth instances: {}'.format(nr_gt))
nr_pred = len(self.proposal)
print('\tNumber of proposals: {}'.format(nr_pred))
print('\tFixed threshold for tiou score: {}'.format(
self.tiou_thresholds))
def __init__(self,
ground_truth_filename=None,
prediction_filename=None,
ground_truth_fields=GROUND_TRUTH_FIELDS,
prediction_fields=PREDICTION_FIELDS,
tiou_thresholds=np.linspace(0.5, 0.95, 10),
subset='validation',
verbose=False,
check_status=True):
if not ground_truth_filename:
raise IOError('Please input a valid ground truth file.')
if not prediction_filename:
raise IOError('Please input a valid prediction file.')
self.subset = subset
self.tiou_thresholds = tiou_thresholds
self.verbose = verbose
self.gt_fields = ground_truth_fields
self.pred_fields = prediction_fields
self.ap = None
self.check_status = check_status
# Retrieve blocked videos from server.
# TODO: we can filter the blocked_videos for validation and test dataset,
# improve efficiency
if self.check_status:
self.blocked_videos = get_blocked_videos()
else:
self.blocked_videos = list()
# Import ground truth and predictions.
self.ground_truth, self.activity_index = self._import_ground_truth(
ground_truth_filename)
self.prediction = self._import_prediction(prediction_filename)
if self.verbose:
print('[INIT] Loaded annotations from {} subset.'.format(subset))
nr_gt = len(self.ground_truth)
print('\tNumber of ground truth instances: {}'.format(nr_gt))
nr_pred = len(self.prediction)
print('\tNumber of predictions: {}'.format(nr_pred))
print('\tFixed threshold for tiou score: {}'.format(
self.tiou_thresholds))