Python测指标模板

import cv2
import glob
import os
import numpy as np
from medpy import metric

def calculate_metric_percase(pred, gt):
    # dice = metric.binary.dc(pred, gt)
    # jc = metric.binary.jc(pred, gt)
    hd = metric.binary.hd95(pred, gt)
    asd = metric.binary.asd(pred, gt)
    return  hd, asd


dice_mean = 0
hd_mean = 0
asd_mean = 0
hd = list()
asd =list()

predlist=glob.glob(os.path.join('./Dataset/data2/predict', "*"))#预测文件夹路径
gtlist=glob.glob(os.path.join('./Dataset/data2/val/M', "*"))#真值文件夹路径


for i in range(len(predlist)):
    print(i, os.path.basename(predlist[i]))

    img_ground = cv2.imread(predlist[i])
    img_predict = cv2.imread(gtlist[i])
    x = 0
    y = 0

    for w in range(256):
        for h in range(256):
            x += img_ground.item(w, h, 1)/255
            y += img_predict.item(w, h, 1)/255
    if x + y == 0 or x == 0 or y == 0:
        current_hd=0
        current_asd=0
    else:
        current_hd, current_asd = calculate_metric_percase(img_predict, img_ground)
    print('hd', current_hd, 'asd', current_asd)

    hd_mean += current_hd
    hd.append((current_hd))
    asd_mean += current_asd
    asd.append((current_asd))


hd_mean /=len(predlist)
asd_mean /=len(predlist)
print('Dice',dice_mean)
print('hd',hd_mean,'asd',asd_mean)

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