val.py

# -*- coding: utf-8 -*-
# @Time    : 2020/11/21
# @Author  : Lart Pang
# @GitHub  : https://github.com/lartpang

import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"

import cv2
from tqdm import tqdm

# pip install pysodmetrics
from py_sod_metrics import MAE, Emeasure, Fmeasure, Smeasure, WeightedFmeasure

method='mfnet5/56'
# for _data_name in ['COD10K']:
for _data_name in ['CAMO','CHAMELEON','COD10K']:
# for _data_name in ['CAMO','CHAMELEON']:
    mask_root = './data/TestDataset/{}/GT'.format(_data_name)
    pred_root = './results/{}/{}/GT'.format(method, _data_name)
    mask_name_list = sorted(os.listdir(mask_root))
    FM = Fmeasure()
    WFM = WeightedFmeasure()
    SM = Smeasure()
    EM = Emeasure()
    M = MAE()
    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)
        M.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 = M.get_results()["mae"]

    results = {
        "Smeasure": sm,
        "wFmeasure": wfm,
        "MAE": mae,
        "meanEm": em["curve"].mean(),
        # "adpEm": em["adp"],
        # "maxEm": em["curve"].max(),
        # "adpFm": fm["adp"],
        # "meanFm": fm["curve"].mean(),
        # "maxFm": fm["curve"].max(),
    }

    print(results)
    file=open("evalresults.log", "a")
    file.write(method+' '+_data_name+' '+str(results)+'\n')