def average_recall_vs_avg_nr_proposals(ground_truth, proposals,
max_avg_nr_proposals=None,
tiou_thresholds=np.linspace(0.5, 0.95, 10)):
""" 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 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
# Get proposals for this video.
try:
proposals_videoid = proposals_gbvn.get_group(videoid)
except:
n = this_video_ground_truth.shape[0]
score_lst.append(np.zeros((n, 1)))
continue
this_video_proposals = proposals_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
# Sort proposals by score.
sort_idx = proposals_videoid['score'].argsort()[::-1]
this_video_proposals = this_video_proposals[sort_idx, :]
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, :]
# Compute tiou scores.
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, 101) / 100.0 *(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.int), 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
def average_recall_vs_avg_nr_proposals(ground_truth, proposals,
max_avg_nr_proposals=None,
tiou_thresholds=np.linspace(0.5, 0.95, 10)):
""" 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 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
# Get proposals for this video.
try:
proposals_videoid = proposals_gbvn.get_group(videoid)
except:
n = this_video_ground_truth.shape[0]
score_lst.append(np.zeros((n, 1)))
continue
this_video_proposals = proposals_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
# Sort proposals by score.
sort_idx = proposals_videoid['score'].argsort()[::-1]
this_video_proposals = this_video_proposals[sort_idx, :]
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, :]
# Compute tiou scores.
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, 101) / 100.0 *(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.int), 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
