def main(ground_truth_filename, proposal_filename, max_avg_nr_proposals=100,
tiou_thresholds=np.linspace(0.5, 0.95, 10),
subset='validation', verbose=True, check_status=True):
anet_proposal = ANETproposal(ground_truth_filename, proposal_filename,
tiou_thresholds=tiou_thresholds,
max_avg_nr_proposals=max_avg_nr_proposals,
subset=subset, verbose=True, check_status=True)
anet_proposal.evaluate()
def run_evaluation(gt_file, res_file, max_avg_nr_proposals = 100, \
tiou_thresholds = np.linspace(0.5, 0.95, 10), subset='validation'):
anet_proposal = ANETproposal(gt_file, res_file,
tiou_thresholds=tiou_thresholds,
max_avg_nr_proposals=max_avg_nr_proposals,
subset=subset, verbose=True, check_status=False)
anet_proposal.evaluate()
recall = anet_proposal.recall
average_recall = anet_proposal.avg_recall
average_nr_proposals = anet_proposal.proposals_per_video
return (average_nr_proposals, average_recall, recall)
def evaluation_proposal(args, eval_file):
ground_truth_filename = './Evaluation/data/thumos14.json'
anet_proposal = ANETproposal(ground_truth_filename,
eval_file,
tiou_thresholds=np.linspace(0.5, 1.0, 11),
max_avg_nr_proposals=1000,
subset='test',
verbose=True,
check_status=False)
anet_proposal.evaluate()
recall = anet_proposal.recall
average_recall = anet_proposal.avg_recall
average_nr_proposals = anet_proposal.proposals_per_video
names = ['AR@50', 'AR@100', 'AR@200', 'AR@500', 'AR@1000']
values = [np.mean(recall[:, i]) for i in [49, 99, 199, 499, 999]]
return names, values
def evaluate_return_area(ground_truth_filename,proposal_filename,max_avg_nr_proposals=100,
tiou_thresholds=np.linspace(0.5,0.95,10),
subset='validation',verbose = True, check_status = True):
anet_proposal = ANETproposal(ground_truth_filename, proposal_filename,
tiou_thresholds=tiou_thresholds,
max_avg_nr_proposals=max_avg_nr_proposals,
subset=subset, verbose=True, check_status=True)
anet_proposal.evaluate()
recall = anet_proposal.recall
average_recall = anet_proposal.avg_recall
average_nr_proposals = anet_proposal.proposals_per_video
area_under_curve = np.trapz(average_recall, average_nr_proposals)
AR_AN = 100.*float(area_under_curve)/average_nr_proposals[-1]
Recall_all = 100.*float(average_recall[-1])
return (AR_AN,Recall_all)
def run_evaluation(ground_truth_filename,
proposal_filename,
max_avg_nr_proposals=100,
tiou_thresholds=np.linspace(0.5, 0.95, 10),
subset='validation'):
anet_proposal = ANETproposal(ground_truth_filename,
proposal_filename,
tiou_thresholds=tiou_thresholds,
max_avg_nr_proposals=max_avg_nr_proposals,
subset=subset,
verbose=True,
check_status=True)
auc_score = anet_proposal.evaluate()
recall = anet_proposal.recall
average_recall = anet_proposal.avg_recall
average_nr_proposals = anet_proposal.proposals_per_video
return (average_nr_proposals, average_recall, recall, auc_score)
def evaluation_proposal(opt):
app = ANETproposal(
ground_truth_filename="./Evaluation/data/activity_net_1_3_new.json",
proposal_filename=os.path.join(opt['output'], opt["result_file"]),
tiou_thresholds=np.linspace(0.5, 0.95, 10),
max_avg_nr_proposals=100,
subset='validation',
verbose=True,
check_status=False)
app.evaluate()
parent_path, run_id = os.path.split(os.path.normpath(opt['output']))
results = (f'[{run_id}|Proposals]'
f' AUC {app.auc*100:.3f}'
f' AR@1 {np.mean(app.recall[:,0])*100:.3f}'
f' AR@5 {np.mean(app.recall[:,4])*100:.3f}'
f' AR@10 {np.mean(app.recall[:,9])*100:.3f}'
f' AR@100 {np.mean(app.recall[:,-1])*100:.3f}')
print(results)
with open(os.path.join(parent_path, 'results.txt'), 'a') as fobj:
fobj.write(f'{results}\n')