YOLOv9图像标注和格式转换

一、软件安装

labelimg安装（anaconda）

方法一、

pip install labelImg

方法二、

pip install PyQt5 -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install pyqt5-tools -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install lxml -i https://pypi.tuna.tsinghua.edu.cn/simple/
pip install labelImg -i https://pypi.tuna.tsinghua.edu.cn/simple/

二、软件标注

打开labelimg环境,运行如下命令即可：

conda activate labelimg
labelimg

 三、格式转换

        标签文件.xml转换为.txt格式

import xml.etree.ElementTree as ET
import os
from os import getcwdclasses = ["pikaqiu",'apple']   # 改成自己的类别
abs_path = os.getcwd()
print(abs_path)def convert(size, box):dw = 1. / (size[0])dh = 1. / (size[1])x = (box[0] + box[1]) / 2.0 - 1y = (box[2] + box[3]) / 2.0 - 1w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn x, y, w, hdef convert_annotation(xml_id_path,txtpath):in_file = open(xml_id_path, encoding='UTF-8')     txtpath1=os.path.join(txtpath,xml_id_path.split('\\')[-1].split('.')[0]+'.txt')out_file = open(txtpath1, 'w')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#difficult = obj.find('Difficult').textcls = obj.find('name').text        if cls not in classes or int(difficult) == 1:continuecls_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))b1, b2, b3, b4 = b# 标注越界修正if b2 > w:b2 = wif b4 > h:b4 = hb = (b1, b2, b3, b4)bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')#xml标签文档地址          
xmlpath=r'C:\Users\admin\xxxxxxxx\labels_xml'
listxmls=os.listdir(xmlpath)
#txt文档保存地址
txtpath=r'C:\Users\admin\xxxxxxxx\labels_txt'
for xml_id in listxmls:xml_id_path=os.path.join(xmlpath,xml_id)convert_annotation(xml_id_path,txtpath)

四、训练数据参数设置

训练代码链接：项目目录预览 - yolov9 - GitCode

到yolov9主目录下的./data路径下重新编写一个.yaml文件

pikaqiu.yaml

train: /media/hadoop/xxxxxxx/VOC/images/train 
val: /media/hadoop/xxxxxxxxx/VOC/images/val
test: /media/hadoop/xxxxxxxx/VOC/images/test
# number of classes
nc: 2
# class names
names: [ 'pikaqiu','apple']

将该文件填入到训练文件train.py中的数据参数设置--data中: