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test_metrics.py
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test_metrics.py
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# -*- coding: utf-8 -*-
# @Time : 2020/11/21
# @Author : Lart Pang
# @GitHub : https://github.com/lartpang
import os
import sys
import unittest
from pprint import pprint
import cv2
sys.path.append("..")
import py_sod_metrics
FM = py_sod_metrics.Fmeasure()
WFM = py_sod_metrics.WeightedFmeasure()
SM = py_sod_metrics.Smeasure()
EM = py_sod_metrics.Emeasure()
MAE = py_sod_metrics.MAE()
MSIOU = py_sod_metrics.MSIoU()
sample_gray = dict(with_adaptive=True, with_dynamic=True)
sample_bin = dict(with_adaptive=False, with_dynamic=False, with_binary=True, sample_based=True)
overall_bin = dict(with_adaptive=False, with_dynamic=False, with_binary=True, sample_based=False)
FMv2 = py_sod_metrics.FmeasureV2(
metric_handlers={
# 灰度数据指标
"fm": py_sod_metrics.FmeasureHandler(**sample_gray, beta=0.3),
"f1": py_sod_metrics.FmeasureHandler(**sample_gray, beta=1),
"pre": py_sod_metrics.PrecisionHandler(**sample_gray),
"rec": py_sod_metrics.RecallHandler(**sample_gray),
"fpr": py_sod_metrics.FPRHandler(**sample_gray),
"iou": py_sod_metrics.IOUHandler(**sample_gray),
"dice": py_sod_metrics.DICEHandler(**sample_gray),
"spec": py_sod_metrics.SpecificityHandler(**sample_gray),
"ber": py_sod_metrics.BERHandler(**sample_gray),
"oa": py_sod_metrics.OverallAccuracyHandler(**sample_gray),
"kappa": py_sod_metrics.KappaHandler(**sample_gray),
# 二值化数据指标的特殊情况一：各个样本独立计算指标后取平均
"sample_bifm": py_sod_metrics.FmeasureHandler(**sample_bin, beta=0.3),
"sample_bif1": py_sod_metrics.FmeasureHandler(**sample_bin, beta=1),
"sample_bipre": py_sod_metrics.PrecisionHandler(**sample_bin),
"sample_birec": py_sod_metrics.RecallHandler(**sample_bin),
"sample_bifpr": py_sod_metrics.FPRHandler(**sample_bin),
"sample_biiou": py_sod_metrics.IOUHandler(**sample_bin),
"sample_bidice": py_sod_metrics.DICEHandler(**sample_bin),
"sample_bispec": py_sod_metrics.SpecificityHandler(**sample_bin),
"sample_biber": py_sod_metrics.BERHandler(**sample_bin),
"sample_bioa": py_sod_metrics.OverallAccuracyHandler(**sample_bin),
"sample_bikappa": py_sod_metrics.KappaHandler(**sample_bin),
# 二值化数据指标的特殊情况二：汇总所有样本的tp、fp、tn、fn后整体计算指标
"overall_bifm": py_sod_metrics.FmeasureHandler(**overall_bin, beta=0.3),
"overall_bif1": py_sod_metrics.FmeasureHandler(**overall_bin, beta=1),
"overall_bipre": py_sod_metrics.PrecisionHandler(**overall_bin),
"overall_birec": py_sod_metrics.RecallHandler(**overall_bin),
"overall_bifpr": py_sod_metrics.FPRHandler(**overall_bin),
"overall_biiou": py_sod_metrics.IOUHandler(**overall_bin),
"overall_bidice": py_sod_metrics.DICEHandler(**overall_bin),
"overall_bispec": py_sod_metrics.SpecificityHandler(**overall_bin),
"overall_biber": py_sod_metrics.BERHandler(**overall_bin),
"overall_bioa": py_sod_metrics.OverallAccuracyHandler(**overall_bin),
"overall_bikappa": py_sod_metrics.KappaHandler(**overall_bin),
}
)
data_root = "./test_data"
mask_root = os.path.join(data_root, "masks")
pred_root = os.path.join(data_root, "preds")
mask_name_list = sorted(os.listdir(mask_root))
for i, mask_name in enumerate(mask_name_list):
print(f"[{i}] Processing {mask_name}...")
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)
MSIOU.step(pred=pred, gt=mask)
FMv2.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"]
msiou = MSIOU.get_results()["msiou"]
fmv2 = FMv2.get_results()
curr_results = {
"MAE": mae,
"Smeasure": sm,
"wFmeasure": wfm,
"MSIOU": msiou,
# E-measure for sod
"adpEm": em["adp"],
"meanEm": em["curve"].mean(),
"maxEm": em["curve"].max(),
# F-measure for sod
"adpFm": fm["adp"],
"meanFm": fm["curve"].mean(),
"maxFm": fm["curve"].max(),
# general F-measure
"adpfm": fmv2["fm"]["adaptive"],
"meanfm": fmv2["fm"]["dynamic"].mean(),
"maxfm": fmv2["fm"]["dynamic"].max(),
"sample_bifm": fmv2["sample_bifm"]["binary"],
"overall_bifm": fmv2["overall_bifm"]["binary"],
# precision
"adppre": fmv2["pre"]["adaptive"],
"meanpre": fmv2["pre"]["dynamic"].mean(),
"maxpre": fmv2["pre"]["dynamic"].max(),
"sample_bipre": fmv2["sample_bipre"]["binary"],
"overall_bipre": fmv2["overall_bipre"]["binary"],
# recall
"adprec": fmv2["rec"]["adaptive"],
"meanrec": fmv2["rec"]["dynamic"].mean(),
"maxrec": fmv2["rec"]["dynamic"].max(),
"sample_birec": fmv2["sample_birec"]["binary"],
"overall_birec": fmv2["overall_birec"]["binary"],
# fpr
"adpfpr": fmv2["fpr"]["adaptive"],
"meanfpr": fmv2["fpr"]["dynamic"].mean(),
"maxfpr": fmv2["fpr"]["dynamic"].max(),
"sample_bifpr": fmv2["sample_bifpr"]["binary"],
"overall_bifpr": fmv2["overall_bifpr"]["binary"],
# dice
"adpdice": fmv2["dice"]["adaptive"],
"meandice": fmv2["dice"]["dynamic"].mean(),
"maxdice": fmv2["dice"]["dynamic"].max(),
"sample_bidice": fmv2["sample_bidice"]["binary"],
"overall_bidice": fmv2["overall_bidice"]["binary"],
# iou
"adpiou": fmv2["iou"]["adaptive"],
"meaniou": fmv2["iou"]["dynamic"].mean(),
"maxiou": fmv2["iou"]["dynamic"].max(),
"sample_biiou": fmv2["sample_biiou"]["binary"],
"overall_biiou": fmv2["overall_biiou"]["binary"],
# f1 score
"adpf1": fmv2["f1"]["adaptive"],
"meanf1": fmv2["f1"]["dynamic"].mean(),
"maxf1": fmv2["f1"]["dynamic"].max(),
"sample_bif1": fmv2["sample_bif1"]["binary"],
"overall_bif1": fmv2["overall_bif1"]["binary"],
# specificity
"adpspec": fmv2["spec"]["adaptive"],
"meanspec": fmv2["spec"]["dynamic"].mean(),
"maxspec": fmv2["spec"]["dynamic"].max(),
"sample_bispec": fmv2["sample_bispec"]["binary"],
"overall_bispec": fmv2["overall_bispec"]["binary"],
# ber
"adpber": fmv2["ber"]["adaptive"],
"meanber": fmv2["ber"]["dynamic"].mean(),
"maxber": fmv2["ber"]["dynamic"].max(),
"sample_biber": fmv2["sample_biber"]["binary"],
"overall_biber": fmv2["overall_biber"]["binary"],
# overall accuracy
"adpoa": fmv2["oa"]["adaptive"],
"meanoa": fmv2["oa"]["dynamic"].mean(),
"maxoa": fmv2["oa"]["dynamic"].max(),
"sample_bioa": fmv2["sample_bioa"]["binary"],
"overall_bioa": fmv2["overall_bioa"]["binary"],
# kappa
"adpkappa": fmv2["kappa"]["adaptive"],
"meankappa": fmv2["kappa"]["dynamic"].mean(),
"maxkappa": fmv2["kappa"]["dynamic"].max(),
"sample_bikappa": fmv2["sample_bikappa"]["binary"],
"overall_bikappa": fmv2["overall_bikappa"]["binary"],
}
default_results = {
"v1_2_3": {
"Smeasure": 0.9029763868504661,
"wFmeasure": 0.5579812753638986,
"MAE": 0.03705558476661653,
"adpEm": 0.9408760066970631,
"meanEm": 0.9566258293508715,
"maxEm": 0.966954482892271,
"adpFm": 0.5816750824038355,
"meanFm": 0.577051059518767,
"maxFm": 0.5886784581120638,
},
"v1_3_0": {
"Smeasure": 0.9029761578759272,
"wFmeasure": 0.5579812753638986,
"MAE": 0.03705558476661653,
"adpEm": 0.9408760066970617,
"meanEm": 0.9566258293508704,
"maxEm": 0.9669544828922699,
"adpFm": 0.5816750824038355,
"meanFm": 0.577051059518767,
"maxFm": 0.5886784581120638,
},
"v1_4_0": {
"MAE": 0.03705558476661653,
"Smeasure": 0.9029761578759272,
"adpEm": 0.9408760066970617,
"adpFm": 0.5816750824038355,
"adpber": 0.2354784689008184,
"adpdice": 0.5801020564379223,
"adpf1": 0.5801020564379223,
"adpfm": 0.5816750824038355,
"adpiou": 0.5141023436626048,
"adpkappa": 0.6568702977598276,
"adpoa": 0.9391947016812359,
"adppre": 0.583200007681871,
"adprec": 0.5777548546727481,
"adpspec": 0.9512882075256152,
"maxEm": 0.9669544828922699,
"maxFm": 0.5886784581120638,
"maxber": 0.6666666666666666,
"maxdice": 0.5830613926289557,
"maxf1": 0.5830613926289557,
"maxfm": 0.5886784581120638,
"maxiou": 0.5201569938888494,
"maxkappa": 0.6759493461328753,
"maxoa": 0.9654783867686053,
"maxpre": 0.6396783912301717,
"maxrec": 0.6666666666666666,
"maxspec": 0.9965927890353435,
"meanEm": 0.9566258293508704,
"meanFm": 0.577051059518767,
"meanber": 0.23290802950995626,
"meandice": 0.5689913551800527,
"meanf1": 0.568991355180053,
"meanfm": 0.577051059518767,
"meaniou": 0.49816648786971,
"meankappa": 0.6443053495487194,
"meanoa": 0.9596413706286032,
"meanpre": 0.5857695537152126,
"meanrec": 0.5599653001125341,
"meanspec": 0.9742186408675534,
"overall_biber": 0.08527759498137788,
"overall_bidice": 0.8510675335753018,
"overall_bif1": 0.8510675335753017,
"overall_bifm": 0.8525259082995088,
"overall_biiou": 0.740746352327995,
"overall_bikappa": 0.7400114676102276,
"overall_bioa": 0.965778,
"overall_bipre": 0.8537799277020065,
"overall_birec": 0.8483723190115916,
"overall_bispec": 0.9810724910256526,
"sample_biber": 0.23037858807333392,
"sample_bidice": 0.5738376903441331,
"sample_bif1": 0.5738376903441331,
"sample_bifm": 0.5829998670906196,
"sample_biiou": 0.5039622042094377,
"sample_bikappa": 0.6510635726572914,
"sample_bioa": 0.964811758770181,
"sample_bipre": 0.5916996553523113,
"sample_birec": 0.5592859147614985,
"sample_bispec": 0.9799569090918337,
"wFmeasure": 0.5579812753638986,
},
"v1_4_1": {
"MAE": 0.03705558476661653,
"MSIOU": 0.8228002109838289,
"Smeasure": 0.9029761578759272,
"adpEm": 0.9408760066970617,
"adpFm": 0.5816750824038355,
"adpber": 0.2354784689008184,
"adpdice": 0.5801020564379223,
"adpf1": 0.5801020564379223,
"adpfm": 0.5816750824038355,
"adpiou": 0.5141023436626048,
"adpkappa": 0.6568702977598276,
"adpoa": 0.9391947016812359,
"adppre": 0.583200007681871,
"adprec": 0.5777548546727481,
"adpfpr": 0.04871179247438492,
"adpspec": 0.9512882075256152,
"maxEm": 0.9669544828922699,
"maxFm": 0.5886784581120638,
"maxber": 0.6666666666666666,
"maxdice": 0.5830613926289557,
"maxf1": 0.5830613926289557,
"maxfm": 0.5886784581120638,
"maxiou": 0.5201569938888494,
"maxkappa": 0.6759493461328753,
"maxoa": 0.9654783867686053,
"maxpre": 0.6396783912301717,
"maxrec": 0.6666666666666666,
"maxfpr": 1.0,
"maxspec": 0.9965927890353435,
"meanEm": 0.9566258293508704,
"meanFm": 0.577051059518767,
"meanber": 0.23290802950995626,
"meandice": 0.5689913551800527,
"meanf1": 0.568991355180053,
"meanfm": 0.577051059518767,
"meaniou": 0.49816648786971,
"meankappa": 0.6443053495487194,
"meanoa": 0.9596413706286032,
"meanpre": 0.5857695537152126,
"meanrec": 0.5599653001125341,
"meanfpr": 0.02578135913244661,
"meanspec": 0.9742186408675534,
"overall_biber": 0.08527759498137788,
"overall_bidice": 0.8510675335753018,
"overall_bif1": 0.8510675335753017,
"overall_bifm": 0.8525259082995088,
"overall_biiou": 0.740746352327995,
"overall_bikappa": 0.7400114676102276,
"overall_bioa": 0.965778,
"overall_bipre": 0.8537799277020065,
"overall_birec": 0.8483723190115916,
"overall_bifpr": 0.018927508974347383,
"overall_bispec": 0.9810724910256526,
"sample_biber": 0.23037858807333392,
"sample_bidice": 0.5738376903441331,
"sample_bif1": 0.5738376903441331,
"sample_bifm": 0.5829998670906196,
"sample_biiou": 0.5039622042094377,
"sample_bikappa": 0.6510635726572914,
"sample_bioa": 0.964811758770181,
"sample_bipre": 0.5916996553523113,
"sample_birec": 0.5592859147614985,
"sample_bifpr": 0.02004309090816628,
"sample_bispec": 0.9799569090918337,
"wFmeasure": 0.5579812753638986,
},
}
class CheckMetricTestCase(unittest.TestCase):
@classmethod
def setUpClass(cls):
print("Current results:")
pprint(curr_results)
cls.default_results = default_results["v1_4_1"]
def test_sm(self):
self.assertEqual(curr_results["Smeasure"], self.default_results["Smeasure"])
def test_wfm(self):
self.assertEqual(curr_results["wFmeasure"], self.default_results["wFmeasure"])
def test_mae(self):
self.assertEqual(curr_results["MAE"], self.default_results["MAE"])
def test_msiou(self):
self.assertEqual(curr_results["MSIOU"], self.default_results["MSIOU"])
def test_fm(self):
self.assertEqual(curr_results["adpFm"], self.default_results["adpFm"])
self.assertEqual(curr_results["meanFm"], self.default_results["meanFm"])
self.assertEqual(curr_results["maxFm"], self.default_results["maxFm"])
self.assertEqual(curr_results["adpfm"], self.default_results["adpfm"])
self.assertEqual(curr_results["meanfm"], self.default_results["meanfm"])
self.assertEqual(curr_results["maxfm"], self.default_results["maxfm"])
# 对齐v1版本
self.assertEqual(curr_results["adpFm"], self.default_results["adpfm"])
self.assertEqual(curr_results["meanFm"], self.default_results["meanfm"])
self.assertEqual(curr_results["maxFm"], self.default_results["maxfm"])
self.assertEqual(curr_results["sample_bifm"], self.default_results["sample_bifm"])
self.assertEqual(curr_results["overall_bifm"], self.default_results["overall_bifm"])
def test_em(self):
self.assertEqual(curr_results["adpEm"], self.default_results["adpEm"])
self.assertEqual(curr_results["meanEm"], self.default_results["meanEm"])
self.assertEqual(curr_results["maxEm"], self.default_results["maxEm"])
def test_f1(self):
self.assertEqual(curr_results["adpf1"], self.default_results["adpf1"])
self.assertEqual(curr_results["meanf1"], self.default_results["meanf1"])
self.assertEqual(curr_results["maxf1"], self.default_results["maxf1"])
self.assertEqual(curr_results["sample_bif1"], self.default_results["sample_bif1"])
self.assertEqual(curr_results["overall_bif1"], self.default_results["overall_bif1"])
def test_pre(self):
self.assertEqual(curr_results["adppre"], self.default_results["adppre"])
self.assertEqual(curr_results["meanpre"], self.default_results["meanpre"])
self.assertEqual(curr_results["maxpre"], self.default_results["maxpre"])
self.assertEqual(curr_results["sample_bipre"], self.default_results["sample_bipre"])
self.assertEqual(curr_results["overall_bipre"], self.default_results["overall_bipre"])
def test_rec(self):
self.assertEqual(curr_results["adprec"], self.default_results["adprec"])
self.assertEqual(curr_results["meanrec"], self.default_results["meanrec"])
self.assertEqual(curr_results["maxrec"], self.default_results["maxrec"])
self.assertEqual(curr_results["sample_birec"], self.default_results["sample_birec"])
self.assertEqual(curr_results["overall_birec"], self.default_results["overall_birec"])
def test_fpr(self):
self.assertEqual(curr_results["adpfpr"], self.default_results["adpfpr"])
self.assertEqual(curr_results["meanfpr"], self.default_results["meanfpr"])
self.assertEqual(curr_results["maxfpr"], self.default_results["maxfpr"])
self.assertEqual(curr_results["sample_bifpr"], self.default_results["sample_bifpr"])
self.assertEqual(curr_results["overall_bifpr"], self.default_results["overall_bifpr"])
def test_iou(self):
self.assertEqual(curr_results["adpiou"], self.default_results["adpiou"])
self.assertEqual(curr_results["meaniou"], self.default_results["meaniou"])
self.assertEqual(curr_results["maxiou"], self.default_results["maxiou"])
self.assertEqual(curr_results["sample_biiou"], self.default_results["sample_biiou"])
self.assertEqual(curr_results["overall_biiou"], self.default_results["overall_biiou"])
def test_dice(self):
self.assertEqual(curr_results["adpdice"], self.default_results["adpdice"])
self.assertEqual(curr_results["meandice"], self.default_results["meandice"])
self.assertEqual(curr_results["maxdice"], self.default_results["maxdice"])
self.assertEqual(curr_results["sample_bidice"], self.default_results["sample_bidice"])
self.assertEqual(curr_results["overall_bidice"], self.default_results["overall_bidice"])
def test_spec(self):
self.assertEqual(curr_results["adpspec"], self.default_results["adpspec"])
self.assertEqual(curr_results["meanspec"], self.default_results["meanspec"])
self.assertEqual(curr_results["maxspec"], self.default_results["maxspec"])
self.assertEqual(curr_results["sample_bispec"], self.default_results["sample_bispec"])
self.assertEqual(curr_results["overall_bispec"], self.default_results["overall_bispec"])
def test_ber(self):
self.assertEqual(curr_results["adpber"], self.default_results["adpber"])
self.assertEqual(curr_results["meanber"], self.default_results["meanber"])
self.assertEqual(curr_results["maxber"], self.default_results["maxber"])
self.assertEqual(curr_results["sample_biber"], self.default_results["sample_biber"])
self.assertEqual(curr_results["overall_biber"], self.default_results["overall_biber"])
def test_oa(self):
self.assertEqual(curr_results["adpoa"], self.default_results["adpoa"])
self.assertEqual(curr_results["meanoa"], self.default_results["meanoa"])
self.assertEqual(curr_results["maxoa"], self.default_results["maxoa"])
self.assertEqual(curr_results["sample_bioa"], self.default_results["sample_bioa"])
self.assertEqual(curr_results["overall_bioa"], self.default_results["overall_bioa"])
def test_kappa(self):
self.assertEqual(curr_results["adpkappa"], self.default_results["adpkappa"])
self.assertEqual(curr_results["meankappa"], self.default_results["meankappa"])
self.assertEqual(curr_results["maxkappa"], self.default_results["maxkappa"])
self.assertEqual(curr_results["sample_bikappa"], self.default_results["sample_bikappa"])
self.assertEqual(curr_results["overall_bikappa"], self.default_results["overall_bikappa"])
if __name__ == "__main__":
unittest.main()