PASCAL VOC数据集训练集、验证集、测试集的划分和提取

参考  PASCAL VOC数据集训练集、验证集、测试集的划分和提取 - 云+社区 - 腾讯云

1、训练集、验证集、测试集按比例精确划分

#数据集划分
import os
import random

root_dir='./park_voc/VOC2007/'

## 0.7train 0.1val 0.2test
trainval_percent = 0.8
train_percent = 0.7
xmlfilepath = root_dir+'Annotations'
txtsavepath = root_dir+'ImageSets/Main'
total_xml = os.listdir(xmlfilepath)

num = len(total_xml)  # 100
list = range(num)
tv = int(num*trainval_percent)  # 80
tr = int(tv*train_percent)  # 80*0.7=56
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)

ftrainval = open(root_dir+'ImageSets/Main/trainval.txt', 'w')
ftest = open(root_dir+'ImageSets/Main/test.txt', 'w')
ftrain = open(root_dir+'ImageSets/Main/train.txt', 'w')
fval = open(root_dir+'ImageSets/Main/val.txt', 'w')

for i in list:
    name = total_xml[i][:-4]+'\n'
    if i in trainval:
        ftrainval.write(name)
        if i in train:
            ftrain.write(name)
        else:
            fval.write(name)
    else:
        ftest.write(name)

ftrainval.close()
ftrain.close()
fval.close()
ftest .close()

2、训练集、验证集和测试集提取(只给出trian文件的提取方法)

# -*- coding:UTF-8 -*-
import shutil

f_txt = open('D:\dataset\VOCdevkit\split\VOC2007\ImageSets\Main\\trainval.txt', 'r')
f_train = 'D:\dataset\VOCdevkit\VOC2007\\train'

context = list(f_txt)
for imagename in context:
    imagename = imagename[0:6]
    imagename = imagename + '.jpg'
    imagepath = 'D:\dataset\VOCdevkit\VOC2007\JPEGImages\\'+ imagename
    shutil.copy(imagepath,f_train)
    # 删除训练集和验证集，剩余图片为测试集
    # os.remove(imagepath)

#处理Annotations同理只需将.jpg改为.xml