一、操作

需要会调试代码的人自己改，小白直接运行会出错

这是我从自己的大文件里摘取的一部分代码，可以运行，只是要改的文件地址path比较多，遇到双引号“”的地址注意一下，不然地址不对容易出错

 把 calculate.py和 utiles_metrics.py放在同一文件夹下，然后运行 calculate.py。

二、理解

test_mIou,test_mPA,test_miou,test_mpa=compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes,weight_name)  # 执行计算mIoU的函数

gt_dir 真实标签文件夹

pred_dir 预测结果文件夹

主要是这两个变量设置，后面的可以选择性修改

image_ids 文件名称 dirList(pred_dir,path_list) saveList(path_list) 这两个函数得到

num_classes 类别数

name_classes 类别名称

weight_name 权重名称

hist为混淆矩阵，mIoU为交并比

三、代码 

 calculate.py

# -*- coding: utf-8 -*-
import torch
import osfrom time import time
# from PIL import Image
from utils_metrics import compute_mIoU
def saveList(pathName):for file_name in pathName:#f=open("C:/Users/Administrator/Desktop/DeepGlobe-Road-Extraction-link34-py3/dataset/real/gt.txt", "x")with open("./dataset/gt.txt", "a") as f:f.write(file_name.split(".")[0] + "\n")f.closedef dirList(gt_dir,path_list):for i in range(0, len(path_list)):path = os.path.join(gt_dir, path_list[i])if os.path.isdir(path):saveList(os.listdir(path))data_path  = './dataset/'f=open("./dataset/gt.txt", 'w')
gt_dir      = os.path.join(data_path, "real/")
pred_dir    = "./submits/log01_Dink101_five_100/test_iou/iou_60u/"
path_list = os.listdir(pred_dir)
path_list.sort()
dirList(pred_dir,path_list)
saveList(path_list)
num_classes=2
name_classes    = ["nontarget","target"]
weight_name='log01_Dink101_five_100'
image_ids   = open(os.path.join(data_path, "gt.txt"),'r').read().splitlines() test_mIou,test_mPA,test_miou,test_mpa=compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes,weight_name)  # 执行计算mIoU的函数
print('  test_mIoU:  '+str(test_miou))

 utiles_metrics.py

from os.path import joinimport numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
import os
import cv2# from matplotlib import pyplot as plt
import shutil
import numpy as np
# from matplotlib.pyplot import MultipleLocatordef f_score(inputs, target, beta=1, smooth = 1e-5, threhold = 0.5):n, c, h, w = inputs.size()nt, ht, wt, ct = target.size()if h != ht and w != wt:inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)temp_inputs = torch.softmax(inputs.transpose(1, 2).transpose(2, 3).contiguous().view(n, -1, c),-1)temp_target = target.view(n, -1, ct)#--------------------------------------------##   计算dice系数#--------------------------------------------#temp_inputs = torch.gt(temp_inputs, threhold).float()tp = torch.sum(temp_target[...,:-1] * temp_inputs, axis=[0,1])fp = torch.sum(temp_inputs                       , axis=[0,1]) - tpfn = torch.sum(temp_target[...,:-1]              , axis=[0,1]) - tpscore = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)score = torch.mean(score)return score# 设标签宽W，长H
def fast_hist(a, b, n):#--------------------------------------------------------------------------------##   a是转化成一维数组的标签，形状(H×W,)；b是转化成一维数组的预测结果，形状(H×W,)#--------------------------------------------------------------------------------#k = (a >= 0) & (a < n)#--------------------------------------------------------------------------------##   np.bincount计算了从0到n**2-1这n**2个数中每个数出现的次数，返回值形状(n, n)#   返回中，写对角线上的为分类正确的像素点#--------------------------------------------------------------------------------#return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)  def per_class_iu(hist):return np.diag(hist) / np.maximum((hist.sum(1) + hist.sum(0) - np.diag(hist)), 1) def per_class_PA(hist):return np.diag(hist) / np.maximum(hist.sum(1), 1) def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes,weight_name):  # print('Num classes', num_classes)  #-----------------------------------------##   创建一个全是0的矩阵，是一个混淆矩阵#-----------------------------------------#hist = np.zeros((num_classes, num_classes))#------------------------------------------------##   获得验证集标签路径列表，方便直接读取#   获得验证集图像分割结果路径列表，方便直接读取#------------------------------------------------#gt_imgs     = [join(gt_dir, x + ".png") for x in png_name_list]  pred_imgs   = [join(pred_dir, x + ".png") for x in png_name_list]  # building_iou=[]# background_iou=[]m_iou=[]# building_pa=[]# background_pa=[]m_pa=[]#------------------------------------------------##   读取每一个（图片-标签）对#------------------------------------------------#for ind in range(len(gt_imgs)): #------------------------------------------------##   读取一张图像分割结果，转化成numpy数组#------------------------------------------------#pred = np.array(Image.open(pred_imgs[ind]))#------------------------------------------------##   读取一张对应的标签，转化成numpy数组#------------------------------------------------#label = np.array(Image.open(gt_imgs[ind]))  # 如果图像分割结果与标签的大小不一样，这张图片就不计算if len(label.flatten()) != len(pred.flatten()):  print('Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(len(label.flatten()), len(pred.flatten()), gt_imgs[ind],pred_imgs[ind]))continue#------------------------------------------------##   对一张图片计算21×21的hist矩阵，并累加#------------------------------------------------#a=label.flatten()a//=254b=pred.flatten()b//=254hist += fast_hist(a, b,num_classes)  # # 每计算10张就输出一下目前已计算的图片中所有类别平均的mIoU值# mIoUs   = per_class_iu(hist)# mPA     = per_class_PA(hist)# m_iou.append(100 * np.nanmean(mIoUs[1]))# m_pa.append(100 * np.nanmean(mPA[1]))# # if ind > 0 and ind % 10 == 0:  # #     print('{:d} / {:d}: mIou-{:0.2f}; mPA-{:0.2f}'.format(ind, len(gt_imgs),# #                                             100 * np.nanmean(mIoUs[1]),# #                                             100 * np.nanmean(mPA[1])))mIoUs   = per_class_iu(hist)mPA     = per_class_PA(hist)print(mIoUs)# plt.figure()# x=np.arange(len(m_iou))# plt.plot(x,m_iou)# plt.plot(x,m_pa)# plt.grid(True)# y_major_locator=MultipleLocator(10)#把y轴的刻度间隔设置为10，并存在变量里# ax = plt.gca()# ax.yaxis.set_major_locator(y_major_locator)# ax.set_ylim(0,100)# plt.xlabel('Order')# plt.ylabel('mIOU & mPA')# plt.legend(['mIOU','mPA'],loc="upper right")# targ=os.path.join(pred_dir,os.path.pardir)# plt.savefig(os.path.join(targ, weight_name[:-3]+"_sin_miou.png"))return m_iou,m_pa,str(round(mIoUs[1] * 100, 2)),str(round(mPA[1] * 100, 2))

调试