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 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')
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)