hf.py+voc_label.py,划分标签文件名后缀为txt的label

hf.py

# -*- coding: utf-8 -*-
import os
import random
trainval_percent = 1
train_percent = 1
txtfilepath = 'labels'
txtsavepath = 'images'
total_txt = os.listdir(txtfilepath)
num = len(total_txt)
list = range(num)
tv = int(num * trainval_percent)
tr = int(tv * train_percent)
trainval = random.sample(list, tv)
train = random.sample(trainval, tr)
ftrainval = open('hf_txt/trainval.txt', 'w')
ftest = open('hf_txt/test.txt', 'w')
ftrain = open('hf_txt/train.txt', 'w')
fval = open('hf_txt/val.txt', 'w')
for i in list:
    name = total_txt[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()

voc_label.py

# -*- coding: utf-8 -*-

import xml.etree.ElementTree as ET
import os
from os import getcwd

sets = ['train', 'val', 'test']
classes = ["d00","d10","d20","d40"]
abs_path = os.getcwd()



wd = getcwd()
for image_set in sets:
    if not os.path.exists('labels/'):
        os.makedirs('labels/')
    image_ids = open('hf_txt/%s.txt' % (image_set)).read().strip().split()
    list_file = open('%s.txt' % (image_set), 'w')
    for image_id in image_ids:
        list_file.write(abs_path + '/images/%s.png\n' % (image_id))

    list_file.close()