evaluation.py

import os
import cv2
from tqdm import tqdm

import sod_metrics as M

FM = M.Fmeasure()
WFM = M.WeightedFmeasure()
SM = M.Smeasure()
EM = M.Emeasure()
MAE = M.MAE()

mask_root = '../RGBT_dataset/test/VT1000/GT'
pred_root = './output/vt1000-MAE-tha_gwm_pgfm_20p/'
mask_name_list = sorted(os.listdir(mask_root))
for mask_name in tqdm(mask_name_list, total=len(mask_name_list)):
    mask_path = os.path.join(mask_root, mask_name)
    pred_path = os.path.join(pred_root, mask_name)
    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
    pred = cv2.imread(pred_path, cv2.IMREAD_GRAYSCALE)
    FM.step(pred=pred, gt=mask)
    WFM.step(pred=pred, gt=mask)
    SM.step(pred=pred, gt=mask)
    EM.step(pred=pred, gt=mask)
    MAE.step(pred=pred, gt=mask)

fm = FM.get_results()['fm']
wfm = WFM.get_results()['wfm']
sm = SM.get_results()['sm']
em = EM.get_results()['em']
mae = MAE.get_results()['mae']

print(
    'Smeasure:', sm.round(3), '; ',
    'wFmeasure:', wfm.round(3), '; ',
    'MAE:', mae.round(3), '; ',
    'adpEm:', em['adp'].round(3), '; ',
    'meanEm:', '-' if em['curve'] is None else em['curve'].mean().round(3), '; ',
    'maxEm:', '-' if em['curve'] is None else em['curve'].max().round(3), '; ',
    'adpFm:', fm['adp'].round(3), '; ',
    'meanFm:', fm['curve'].mean().round(3), '; ',
    'maxFm:', fm['curve'].max().round(3),
    sep=''
)

with open("../result.txt", "a+") as f:
    print('Smeasure:', sm.round(3), '; ',
          'meanEm:', '-' if em['curve'] is None else em['curve'].mean().round(3), '; ',
          'wFmeasure:', wfm.round(3), '; ',
          'MAE:', mae.round(3), '; ',
          file=f
          )