1 在本博客上篇windows+label使用1后可以生成label的xml文件后：
2 在darket.exe所在的当前目录下，新建VOCdevkit文件夹
然后在VOCdevkit文件夹下新建文件夹VOC2018
然后在VOC2018文件夹下新建以下四个文件夹
将本博客第一步所生成的xml文件全部复制到Annotations里,所用到的图片都放在JPEGimages里
然后在imageSets里面新建三个文件夹
在Main文件家里新建
其中train.txt里写入每张图片的名字 比如00001.jpg这张图片就写为00001，如下图
3 将下面代码粘贴在一个.py文件里，运行即可得到每一个所对应的txt文件
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 30 10:43:13 2018
@author: Administrator
"""
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
#sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
#classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
sets=[('2018', 'train')]
classes = [ "peri","wolf"]
def convert(size, box):
dw = 1./size[0]
dh = 1./size[1]
x = (box[0] + box[1])/2.0
y = (box[2] + box[3])/2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x*dw
w = w*dw
y = y*dh
h = h*dh
return (x,y,w,h)
def convert_annotation(year, image_id):
in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id)) #（如果使用的不是VOC而是自设置数据集名字，则这里需要修改）
out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w') #（同上）
tree=ET.parse(in_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
bb = convert((w,h), b)
out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
wd = getcwd()
for year, image_set in sets:
if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
list_file = open('%s_%s.txt'%(year, image_set), 'w')
for image_id in image_ids:
list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
convert_annotation(year, image_id)
list_file.close()
这里包含了类别和对应归一化后的位置（i guess，如有错请指正）。同时在darknet.exe所在当前目录下应该也生成了2018_train.txt这个文件，里面包含了所有训练样本的绝对路径。
参考博客：https://yq.aliyun.com/wenji/273314
参考博客：https://www.cnblogs.com/antflow/p/7350274.html（yolo1（应该也可以）可能需要修改源代码.C文件（没尝试），yolo2只需要修改配置文件）