|
|
| import sys
|
| sys.path.append('./Evaluation')
|
| from eval_detection_gentime import ANETdetection
|
| import matplotlib.pyplot as plt
|
| import numpy as np
|
|
|
| def run_evaluation_detection(opt, ground_truth_filename, prediction_filename,
|
| tiou_thresholds=np.linspace(0.5, 0.95, 10),
|
| subset='validation', verbose=True):
|
|
|
| anet_detection = ANETdetection(opt, ground_truth_filename, prediction_filename,
|
| subset=subset, tiou_thresholds=tiou_thresholds,
|
| verbose=verbose, check_status=False)
|
| anet_detection.evaluate()
|
|
|
| ap = anet_detection.ap
|
| mAP = anet_detection.mAP
|
| tdiff = anet_detection.tdiff
|
|
|
| return (mAP, ap, tdiff)
|
|
|
| def evaluation_detection(opt, verbose=True):
|
|
|
| mAP, AP, tdiff = run_evaluation_detection(
|
| opt,
|
| opt["video_anno"].format(opt["split"]),
|
| opt["result_file"].format(opt['exp']),
|
| tiou_thresholds=np.linspace(0.1, 0.50, 5),
|
| subset=opt['inference_subset'], verbose=verbose)
|
|
|
| if verbose:
|
| print('mAP')
|
| print(mAP)
|
| print('AEDT')
|
| print(tdiff)
|
|
|
| return mAP
|
|
|
|
|
