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import numpy as np
import pandas as pd
import os
from libs.utils.Evaluation.utils import get_blocked_videos
def interpolated_prec_rec(prec, rec):
"""Interpolated AP - VOCdevkit from VOC 2011.
"""
mprec = np.hstack([[0], prec, [0]])
mrec = np.hstack([[0], rec, [1]])
for i in range(len(mprec) - 1)[::-1]:
mprec[i] = max(mprec[i], mprec[i + 1])
idx = np.where(mrec[1::] != mrec[0:-1])[0] + 1
ap = np.sum((mrec[idx] - mrec[idx - 1]) * mprec[idx])
return ap
def segment_iou(target_segment, candidate_segments):
"""Compute the temporal intersection over union between a
target segment and all the test segments.
Parameters
----------
target_segment : 1d array
Temporal target segment containing [starting, ending] times.
candidate_segments : 2d array
Temporal candidate segments containing N x [starting, ending] times.
Outputs
-------
tiou : 1d array
Temporal intersection over union score of the N's candidate segments.
"""
tt1 = np.maximum(target_segment[0], candidate_segments[:, 0])
tt2 = np.minimum(target_segment[1], candidate_segments[:, 1])
# Intersection including Non-negative overlap score.
segments_intersection = (tt2 - tt1).clip(0)
# Segment union.
segments_union = (candidate_segments[:, 1] - candidate_segments[:, 0]) \
+ (target_segment[1] - target_segment[0]) - segments_intersection
# Compute overlap as the ratio of the intersection
# over union of two segments.
tIoU = segments_intersection.astype(float) / segments_union
return tIoU
def wrapper_segment_iou(target_segments, candidate_segments):
"""Compute intersection over union btw segments
Parameters
----------
target_segments : ndarray
2-dim array in format [m x 2:=[init, end]]
candidate_segments : ndarray,
2-dim array in format [n x 2:=[init, end]]
Outputs
-------
tiou : ndarray
2-dim array [n x m] with IOU ratio.
Note: It assumes that candidate-segments are more scarce that target-segments
"""
if candidate_segments.ndim != 2 or target_segments.ndim != 2:
raise ValueError('Dimension of arguments is incorrect')
n, m = candidate_segments.shape[0], target_segments.shape[0]
tiou = np.empty((n, m))
for i in range(m):
tiou[:, i] = segment_iou(target_segments[i, :], candidate_segments)
return tiou
class ANETproposal(object):
GROUND_TRUTH_FIELDS = ['database', 'taxonomy', 'version']
PROPOSAL_FIELDS = ['results', 'version', 'external_data']
def __init__(self, ground_truth_filename=None, proposal_filename=None, dataset_name='',
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
self.dataset_name = dataset_name
# Retrieve blocked videos from server.
if self.check_status:
pass
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 _import_ground_truth(self, ground_truth_filename):
"""Reads ground truth file, checks if it is well formatted, and returns
the ground truth instances and the activity classes.
Parameters
----------
ground_truth_filename : str
Full path to the ground truth json file.
Outputs
-------
ground_truth : df
Data frame containing the ground truth instances.
activity_index : dict
Dictionary containing class index.
"""
with open(ground_truth_filename, 'r') as fobj:
data = json.load(fobj)
# Checking format
# if not all([field in data.keys() for field in self.gt_fields]):
# raise IOError('Please input a valid ground truth file.')
# Read ground truth data.
activity_index= {'Fake': 0}
video_lst, t_start_lst, t_end_lst, label_lst = [], [], [], []
for v in data:
if isinstance(v, str):
v = data[v]
videoid = os.path.basename(v['file']).replace('.mp4','') if v['file'].endswith('.mp4') else os.path.basename(v['file']).replace('.wav','')
# print(v)
if self.subset != v['split']:
continue
if videoid in self.blocked_videos:
continue
if v['n_fakes']==0:
continue
for ann in v['fake_periods']:
video_lst.append(videoid)
t_start_lst.append(float(ann[0]))
t_end_lst.append(float(ann[1]))
label_lst.append(0)
ground_truth = pd.DataFrame({'video-id': video_lst,
't-start': t_start_lst,
't-end': t_end_lst,
'label': label_lst})
if self.verbose:
print(activity_index)
return ground_truth, activity_index
def _import_proposal(self, proposal_filename):
"""Reads proposal file, checks if it is well formatted, and returns
the proposal instances.
Parameters
----------
proposal_filename : str
Full path to the proposal json file.
Outputs
-------
proposal : df
Data frame containing the proposal instances.
"""
with open(proposal_filename, 'r') as fobj:
data = json.load(fobj)
# Checking format...
if not all([field in data.keys() for field in self.pred_fields]):
raise IOError('Please input a valid proposal file.')
# Read predictions.
video_lst, t_start_lst, t_end_lst = [], [], []
score_lst = []
for videoid, v in data['results'].items():
if videoid in self.blocked_videos:
continue
for result in v:
video_lst.append(videoid)
t_start_lst.append(result['segment'][0])
t_end_lst.append(result['segment'][1])
score_lst.append(result['score'])
proposal = pd.DataFrame({'video-id': video_lst,
't-start': t_start_lst,
't-end': t_end_lst,
'score': score_lst})
return proposal
def evaluate(self):
"""Evaluates a proposal file. To measure the performance of a
method for the proposal task, we computes the area under the
average recall vs average number of proposals per video curve.
"""
recall, avg_recall, proposals_per_video = average_recall_vs_avg_nr_proposals(
self.ground_truth, self.proposal,
max_avg_nr_proposals=self.max_avg_nr_proposals,
tiou_thresholds=self.tiou_thresholds)
area_under_curve = np.trapz(avg_recall, proposals_per_video)
if self.verbose:
print(f'[RESULTS] Performance on {self.dataset_name} proposal task.')
print(
'\tArea Under the AR vs AN curve: {}%'.format(100. * float(area_under_curve) / proposals_per_video[-1]))
self.recall = recall
self.avg_recall = avg_recall
self.proposals_per_video = proposals_per_video
def average_recall_vs_avg_nr_proposals(ground_truth, proposals,
max_avg_nr_proposals=None,
tiou_thresholds=np.linspace(0.5, 0.95, 11)):
""" Computes the average recall given an average number
of proposals per video.
Parameters
----------
ground_truth : df
Data frame containing the ground truth instances.
Required fields: ['video-id', 't-start', 't-end']
proposal : df
Data frame containing the proposal instances.
Required fields: ['video-id, 't-start', 't-end', 'score']
tiou_thresholds : 1darray, optional
array with tiou thresholds.
Outputs
-------
recall : 2darray
recall[i,j] is recall at ith tiou threshold at the jth average number of average number of proposals per video.
average_recall : 1darray
recall averaged over a list of tiou threshold. This is equivalent to recall.mean(axis=0).
proposals_per_video : 1darray
average number of proposals per video.
"""
# Get list of videos.
video_lst = ground_truth['video-id'].unique()
if not max_avg_nr_proposals:
max_avg_nr_proposals = float(proposals.shape[0]) / video_lst.shape[0]
ratio = max_avg_nr_proposals * float(video_lst.shape[0]) / proposals.shape[0]
# Adaptation to query faster
ground_truth_gbvn = ground_truth.groupby('video-id')
proposals_gbvn = proposals.groupby('video-id')
# For each video, computes tiou scores among the retrieved proposals.
score_lst = []
total_nr_proposals = 0
for videoid in video_lst:
# Get proposals for this video.
proposals_videoid = proposals_gbvn.get_group(videoid)
this_video_proposals = proposals_videoid.loc[:, ['t-start', 't-end']].values
# Sort proposals by score.
sort_idx = proposals_videoid['score'].argsort()[::-1]
this_video_proposals = this_video_proposals[sort_idx, :]
# Get ground-truth instances associated to this video.
ground_truth_videoid = ground_truth_gbvn.get_group(videoid)
this_video_ground_truth = ground_truth_videoid.loc[:, ['t-start', 't-end']].values
if this_video_proposals.shape[0] == 0:
n = this_video_ground_truth.shape[0]
score_lst.append(np.zeros((n, 1)))
continue
if this_video_proposals.ndim != 2:
this_video_proposals = np.expand_dims(this_video_proposals, axis=0)
if this_video_ground_truth.ndim != 2:
this_video_ground_truth = np.expand_dims(this_video_ground_truth, axis=0)
nr_proposals = np.minimum(int(this_video_proposals.shape[0] * ratio), this_video_proposals.shape[0])
total_nr_proposals += nr_proposals
this_video_proposals = this_video_proposals[:nr_proposals, :]
tiou = wrapper_segment_iou(this_video_proposals, this_video_ground_truth)
score_lst.append(tiou)
# Given that the length of the videos is really varied, we
# compute the number of proposals in terms of a ratio of the total
# proposals retrieved, i.e. average recall at a percentage of proposals
# retrieved per video.
# Computes average recall.
pcn_lst = np.arange(1, max_avg_nr_proposals + 1) / float(max_avg_nr_proposals) * (
max_avg_nr_proposals * float(video_lst.shape[0]) / total_nr_proposals)
matches = np.empty((video_lst.shape[0], pcn_lst.shape[0]))
positives = np.empty(video_lst.shape[0])
recall = np.empty((tiou_thresholds.shape[0], pcn_lst.shape[0]))
# Iterates over each tiou threshold.
for ridx, tiou in enumerate(tiou_thresholds):
# Inspect positives retrieved per video at different
# number of proposals (percentage of the total retrieved).
for i, score in enumerate(score_lst):
# Total positives per video.
positives[i] = score.shape[0]
# Find proposals that satisfies minimum tiou threshold.
true_positives_tiou = score >= tiou
# Get number of proposals as a percentage of total retrieved.
pcn_proposals = np.minimum((score.shape[1] * pcn_lst).astype(np.int64), score.shape[1])
for j, nr_proposals in enumerate(pcn_proposals):
# Compute the number of matches for each percentage of the proposals
matches[i, j] = np.count_nonzero((true_positives_tiou[:, :nr_proposals]).sum(axis=1))
# Computes recall given the set of matches per video.
recall[ridx, :] = matches.sum(axis=0) / positives.sum()
# Recall is averaged.
avg_recall = recall.mean(axis=0)
# Get the average number of proposals per video.
proposals_per_video = pcn_lst * (float(total_nr_proposals) / video_lst.shape[0])
return recall, avg_recall, proposals_per_video |