一、操作
import cv2
img_gray = cv2.imread("nezha.jpg",cv2.IMREAD_GRAYSCALE)
for i in range(22):
dst = cv2.applyColorMap(img_gray,i)
cv2.imshow('map',dst)
cv2.waitKey(500)
cv2.imwrite("map-"+str(i)+".jpg",dst)需要會調(diào)試代碼的人自己改，小白直接運(yùn)行會出錯(cuò)
這是我從自己的大文件里摘取的一部分代碼，可以運(yùn)行，只是要改的文件地址path比較多，遇到雙引號“”的地址注意一下，不然地址不對容易出錯(cuò)
?把?calculate.py和?utiles_metrics.py放在同一文件夾下，然后運(yùn)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) # 執(zhí)行計(jì)算mIoU的函數(shù)gt_dir 真實(shí)標(biāo)簽文件夾
pred_dir?預(yù)測結(jié)果文件夾
主要是這兩個(gè)變量設(shè)置，后面的可以選擇性修改
image_ids 文件名稱?dirList(pred_dir,path_list) saveList(path_list) 這兩個(gè)函數(shù)得到
num_classes 類別數(shù)
name_classes 類別名稱
weight_name 權(quán)重名稱
hist為混淆矩陣，mIoU為交并比
三、代碼?
?calculate.py
# -*- coding: utf-8 -*-
import torch
import os
from 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.close
def 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) # 執(zhí)行計(jì)算mIoU的函數(shù)
print(' test_mIoU: '+str(test_miou))
?utiles_metrics.py
from os.path import join
import 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 MultipleLocator
def 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)
#--------------------------------------------#
# 計(jì)算dice系數(shù)
#--------------------------------------------#
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]) - tp
fn = torch.sum(temp_target[...,:-1] , axis=[0,1]) - tp
score = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
score = torch.mean(score)
return score
# 設(shè)標(biāo)簽寬W，長H
def fast_hist(a, b, n):
#--------------------------------------------------------------------------------#
# a是轉(zhuǎn)化成一維數(shù)組的標(biāo)簽，形狀(H×W,)；b是轉(zhuǎn)化成一維數(shù)組的預(yù)測結(jié)果，形狀(H×W,)
#--------------------------------------------------------------------------------#
k = (a >= 0) & (a < n)
#--------------------------------------------------------------------------------#
# np.bincount計(jì)算了從0到n**2-1這n**2個(gè)數(shù)中每個(gè)數(shù)出現(xiàn)的次數(shù)，返回值形狀(n, n)
# 返回中，寫對角線上的為分類正確的像素點(diǎn)
#--------------------------------------------------------------------------------#
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)
#-----------------------------------------#
# 創(chuàng)建一個(gè)全是0的矩陣，是一個(gè)混淆矩陣
#-----------------------------------------#
hist = np.zeros((num_classes, num_classes))
#------------------------------------------------#
# 獲得驗(yàn)證集標(biāo)簽路徑列表，方便直接讀取
# 獲得驗(yàn)證集圖像分割結(jié)果路徑列表，方便直接讀取
#------------------------------------------------#
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=[]
#------------------------------------------------#
# 讀取每一個(gè)（圖片-標(biāo)簽）對
#------------------------------------------------#
for ind in range(len(gt_imgs)):
#------------------------------------------------#
# 讀取一張圖像分割結(jié)果，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
pred = np.array(Image.open(pred_imgs[ind]))
#------------------------------------------------#
# 讀取一張對應(yīng)的標(biāo)簽，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
label = np.array(Image.open(gt_imgs[ind]))
# 如果圖像分割結(jié)果與標(biāo)簽的大小不一樣，這張圖片就不計(jì)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
#------------------------------------------------#
# 對一張圖片計(jì)算21×21的hist矩陣，并累加
#------------------------------------------------#
a=label.flatten()
a//=254
b=pred.flatten()
b//=254
hist += fast_hist(a, b,num_classes)
# # 每計(jì)算10張就輸出一下目前已計(jì)算的圖片中所有類別平均的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軸的刻度間隔設(shè)置為10，并存在變量里
# 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))
調(diào)試
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import os
import shutil
data_path='./submits/log01_Dink101_five_100/test_iou/'
data=open(os.path.join(data_path, "log01_Dink101_five_100_excel.txt"),'r').read().splitlines()
valid_path='./dataset/valid/'
real_path='./dataset/real/'
iou_100=os.path.join(data_path,'iou_60u/')
iou_80=os.path.join(data_path,'iou_60d/')
if not os.path.exists(iou_100):
os.mkdir(iou_100)
os.mkdir(iou_80)
for n in data:
name=n.split()[1]
iou=float(n.split()[2])
if iou>=65:
img_path=os.path.join(data_path,'87.650/'+name+'.png')
shutil.copy(img_path,iou_100)
print(name,iou)
continue
else :
img_path=os.path.join(data_path,'87.650/'+name+'.png')
shutil.copy(img_path,iou_80)
print(name,iou)
continue
print('Finish')
# -*- coding: utf-8 -*-
import torch
import os
from time import time
# from PIL import Image
from utils.utils_metrics import compute_mIoU
from utils.utils_metrics import compute_IoU
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.close
def 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()
# compute_IoU(gt_dir, pred_dir, image_ids, num_classes, weight_name)
test_mIou,test_mPA,test_miou,test_mpa=compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes,weight_name) # 執(zhí)行計(jì)算mIoU的函數(shù)
print(' test_mIoU: '+str(test_miou))from os.path import join
import 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 MultipleLocator
def 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)
#--------------------------------------------#
# 計(jì)算dice系數(shù)
#--------------------------------------------#
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]) - tp
fn = torch.sum(temp_target[...,:-1] , axis=[0,1]) - tp
score = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
score = torch.mean(score)
return score
# 設(shè)標(biāo)簽寬W，長H
def fast_hist(a, b, n):
#--------------------------------------------------------------------------------#
# a是轉(zhuǎn)化成一維數(shù)組的標(biāo)簽，形狀(H×W,)；b是轉(zhuǎn)化成一維數(shù)組的預(yù)測結(jié)果，形狀(H×W,)
#--------------------------------------------------------------------------------#
k = (a >= 0) & (a < n)
#--------------------------------------------------------------------------------#
# np.bincount計(jì)算了從0到n**2-1這n**2個(gè)數(shù)中每個(gè)數(shù)出現(xiàn)的次數(shù)，返回值形狀(n, n)
# 返回中，寫對角線上的為分類正確的像素點(diǎn)
#--------------------------------------------------------------------------------#
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_IoU(gt_dir, pred_dir, png_name_list, num_classes,weight_name):
# print('Num classes')
#-----------------------------------------#
# 創(chuàng)建一個(gè)全是0的矩陣，是一個(gè)混淆矩陣
#-----------------------------------------#
# hist = np.zeros((num_classes, num_classes))
#------------------------------------------------#
# 獲得驗(yàn)證集標(biāo)簽路徑列表，方便直接讀取
# 獲得驗(yàn)證集圖像分割結(jié)果路徑列表，方便直接讀取
#------------------------------------------------#
gt_imgs = [join(gt_dir, x + "_mask.png") for x in png_name_list]
pred_imgs = [join(pred_dir, x + ".png") for x in png_name_list]
m_iou=[]
m_pa=[]
hist_save=[]
#------------------------------------------------#
# 讀取每一個(gè)（圖片-標(biāo)簽）對
#------------------------------------------------#
for ind in range(len(gt_imgs)):
#------------------------------------------------#
# 讀取一張圖像分割結(jié)果，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
pred = np.array(Image.open(pred_imgs[ind]).convert('L'))
# pred = pred/255
#------------------------------------------------#
# 讀取一張對應(yīng)的標(biāo)簽，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
label = np.array(Image.open(gt_imgs[ind]).convert('L'))
# label = label/255
# 如果圖像分割結(jié)果與標(biāo)簽的大小不一樣，這張圖片就不計(jì)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
#------------------------------------------------#
# 對一張圖片計(jì)算21×21的hist矩陣，并累加
#------------------------------------------------#
a=label.flatten()
a//=254
# for i in range(len(a)):
# a[i]=a[i]/255
b=pred.flatten()
b//=254
# for i in range(len(b)):
# b[i]=b[i]/255
hist = fast_hist(a, b,num_classes)
# 每計(jì)算10張就輸出一下目前已計(jì)算的圖片中所有類別平均的mIoU值
mIoUs = per_class_iu(hist)
mPA = per_class_PA(hist)
mIoU_one = 100 * np.nanmean(mIoUs[1])
mPA_one = 100 * np.nanmean(mPA[1])
# if mIoU_one<80:
# shutil.copy(pred_imgs[ind],lower_iou)
# count=count+1
# else:
# shutil.copy(pred_imgs[ind],higher_iou)
img_name= png_name_list[ind]
# if ind > 0 and ind % 10 == 0:
#print('{:d} {}: Iou-{:0.2f}; PA-{:0.2f}'.format(ind,img_name,mIoU_one,mPA_one))
#print(hist)
m_iou.append(100 * np.nanmean(mIoUs[1]))
m_pa.append(100 * np.nanmean(mPA[1]))
hist_save.append(hist)
targ=os.path.join(pred_dir,os.path.pardir)
if ind==0:
with open(os.path.join(targ, weight_name[:-3]+'.txt'),'w') as f:
f.write(str(ind) +' '+ str(img_name) +' ')
f.write('Iou:'+str(round(mIoU_one,2))+' '+'PA:'+str(round(mPA_one,2))+'\n')
f.write(' '+'['+str(hist[0,0])+' '+str(hist[0,1])+'\n')
f.write(' '+' '+str(hist[1,0])+' '+str(hist[1,1])+']'+'\n')
with open(os.path.join(targ, weight_name[:-3]+'_excel.txt'),'w') as f:
f.write(str(ind) +' '+ str(img_name) +' ')
f.write(str(round(mIoU_one,2))+' '+str(round(mPA_one,2))+'\n')
else:
with open(os.path.join(targ, weight_name[:-3]+'.txt'),'a') as f:
f.write(str(ind) +' '+ str(img_name) +' ')
f.write('Iou:'+str(round(mIoU_one,2))+' '+'PA:'+str(round(mPA_one,2))+'\n')
f.write(' '+'['+str(hist[0,0])+' '+str(hist[0,1])+'\n')
f.write(' '+' '+str(hist[1,0])+' '+str(hist[1,1])+']'+'\n')
with open(os.path.join(targ, weight_name[:-3]+'_excel.txt'),'a') as f:
f.write(str(ind) +' '+ str(img_name) +' ')
f.write(str(round(mIoU_one,2))+' '+str(round(mPA_one,2))+'\n')
'''
plt.figure()
x=np.arange(len(m_iou))
plt.plot(x,m_iou)
plt.plot(x,m_pa)
plt.grid(True)
plt.xlabel('Order')
plt.ylabel('test mIOU & mPA')
plt.legend(['mIOU','mPA'],loc="upper right")
plt.savefig(os.path.join(pred_dir[0:-13], weight_name[:-3]+"_test_iou.png"))
'''
def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes,weight_name):
# print('Num classes', num_classes)
#-----------------------------------------#
# 創(chuàng)建一個(gè)全是0的矩陣，是一個(gè)混淆矩陣
#-----------------------------------------#
hist = np.zeros((num_classes, num_classes))
#------------------------------------------------#
# 獲得驗(yàn)證集標(biāo)簽路徑列表，方便直接讀取
# 獲得驗(yàn)證集圖像分割結(jié)果路徑列表，方便直接讀取
#------------------------------------------------#
gt_imgs = [join(gt_dir, x + "_mask.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=[]
#------------------------------------------------#
# 讀取每一個(gè)（圖片-標(biāo)簽）對
#------------------------------------------------#
for ind in range(len(gt_imgs)):
#------------------------------------------------#
# 讀取一張圖像分割結(jié)果，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
pred = np.array(Image.open(pred_imgs[ind]).convert('L'))
#------------------------------------------------#
# 讀取一張對應(yīng)的標(biāo)簽，轉(zhuǎn)化成numpy數(shù)組
#------------------------------------------------#
label = np.array(Image.open(gt_imgs[ind]).convert('L'))
# 如果圖像分割結(jié)果與標(biāo)簽的大小不一樣，這張圖片就不計(jì)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
#------------------------------------------------#
# 對一張圖片計(jì)算21×21的hist矩陣，并累加
#------------------------------------------------#
a=label.flatten()
a//=254
b=pred.flatten()
b//=254
hist += fast_hist(a, b,num_classes)
# # 每計(jì)算10張就輸出一下目前已計(jì)算的圖片中所有類別平均的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])))
print(hist)
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軸的刻度間隔設(shè)置為10，并存在變量里
# 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))
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