def __init__(self, ground_truth_filename=None, prediction_filename=None,
ground_truth_fields=GROUND_TRUTH_FIELDS,
prediction_fields=PREDICTION_FIELDS,
tiou_thr=0.5, subset='validation', verbose=False):
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_thr = tiou_thr
self.verbose = verbose
self.gt_fields = ground_truth_fields
self.pred_fields = prediction_fields
self.ap = None
# Retrieve blocked videos from server.
self.blocked_videos = get_blocked_videos()
# 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_thr)
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.
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)
def __init__(self, ground_truth_filename=None, prediction_filename=None,
ground_truth_fields=GROUND_TRUTH_FIELDS,
prediction_fields=PREDICTION_FIELDS,
subset='validation', verbose=False, top_k=3,
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.verbose = verbose
self.gt_fields = ground_truth_fields
self.pred_fields = prediction_fields
self.top_k = top_k
self.ap = None
self.hit_at_k = 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 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)
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=True):
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),
limit_factor=None,
min_tiou_thr=0.1,
subset='testing',
verbose=False,
check_status=True,
load_extra_annotations=False,
characteristic_names_to_bins={'context-size': (range(-1, 7), ['0', '1', '2', '3', '4', '5', '6']),
'context-distance': (range(-1, 4), ['Inf', 'N', 'M', 'F']),
'agreement': (np.linspace(0, 1.0, 6), ['XW', 'W', 'M', 'H', 'XH']),
'coverage': (np.linspace(0, 1.0, 6), ['XS', 'S', 'M', 'L', 'XL']),
'length': (np.array([0, 30, 60, 120, 180, np.inf]), ['XS', 'S', 'M', 'L', 'XL']),
'num-instances': (np.array([-1, 1, 4, 8, np.inf]), ['XS', 'S', 'M', 'L'])},
normalize_ap=False,
minimum_normalized_precision_threshold_for_detection=0.00,
evaluate_with_multi_segments=None):
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
self.load_extra_annotations = load_extra_annotations
self.characteristic_names_to_bins = characteristic_names_to_bins
self.characteristic_names = characteristic_names_to_bins.keys()
self.normalize_ap = normalize_ap
self.minimum_normalized_precision_threshold_for_detection = minimum_normalized_precision_threshold_for_detection
self.evaluate_with_multi_segments = evaluate_with_multi_segments
# Retrieve blocked videos from server.
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.average_num_instance_per_class = len(self.ground_truth) / len(self.activity_index)
self.prediction = self._import_prediction(prediction_filename)
self.limit_factor = limit_factor
if self.limit_factor:
self.prediction = self._limit_prediction()
self.matched_gt_id_cols, self.fp_error_type_cols = [], []
for tiou in self.tiou_thresholds:
self.matched_gt_id_cols += ['matched-gt-id-' + str(tiou)]
self.fp_error_type_cols += ['fp-error-type-' + str(tiou)]
self.min_tiou_thr = min_tiou_thr
if self.verbose:
print('[INIT] Loaded annotations from {} subset.'.format(subset))
nr_gt = len(np.unique(self.ground_truth['gt-id']))
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))
