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# -*- coding: utf-8 -*-
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
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