从化网站开发公司,网站在布局,企业管理系统的功能,wordpress 模版 psd【目标检测】DIOR遥感影像数据集#xff0c;转为yolo系列模型训练所需格式。 标签文件位于Annotations下#xff0c;格式为xml#xff0c;yolo系列模型训练所需格式为txt#xff0c;格式为
class_id x_center,y_center,w,h其中#xff0c;train#xff0c;text#xff…【目标检测】DIOR遥感影像数据集转为yolo系列模型训练所需格式。 标签文件位于Annotations下格式为xmlyolo系列模型训练所需格式为txt格式为
class_id x_center,y_center,w,h其中traintextval按照官方方式划分(DIOR/ImageSets/Main/train.txt)分别含影像5062,5063,11738张。 在DIOR/ImageSets/Main/xx.txt 路径中txt文件为不包含影像后缀的影像名称如下图 yolo训练中需要的train.txt文件内容需要是包括后缀的绝对路径
转换代码 转换中的outpath可以自定义为后续配置文件中的路径。
注意 1将DIOR的影像文件夹改名为images,注意全小写字母要对 2转换后的标签位于影像文件夹下的labels下不要修改 **images和labels两个文件夹名称不要修改不要修改否则会报错No labels in xx./train.cache
# -*- coding: utf-8 -*-
import xml.etree.ElementTree as ET
import os
from os import getcwdsets [train, val, test]# class names
classes [airplane, airport, baseballfield, basketballcourt, bridge, chimney, dam,Expressway-Service-area, Expressway-toll-station, golffield, groundtrackfield, harbor,overpass, ship, stadium, storagetank, tenniscourt, trainstation, vehicle, windmill] # 改成自己的类别
abs_path os.getcwd()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, h#修改路径-----------------------------
datasetpathE:/dataset/DIOR
imgpathE:/dataset/DIOR/images
outpathE:/dataset/DIOR/myyolodef convert_annotation(image_id):in_file open(datasetpath/Annotations/%s.xml % (image_id), encodingUTF-8)out_file open(datasetpath/labels/%s.txt % (image_id), w) #不要修改labels文件夹名称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:# continuecls obj.find(name).textif cls not in classes: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)wd getcwd()
for image_set in sets:if not os.path.exists(datasetpath/labels/):os.makedirs(datasetpath/labels/)image_ids open(datasetpath/ImageSets/Main/%s.txt % (image_set)).read().strip().split()if not os.path.exists(outpath):os.makedirs(outpath)list_file open(outpath/%s.txt % (image_set), w)for image_id in image_ids:list_file.write(imgpath/%s.jpg\n % (image_id))convert_annotation(image_id)list_file.close()转换后的text文件 建立数据集配置文件DIOR.yaml路径修改为outpath
train: E:/dataset/DIOR/myyolo/train.txt
val: E:/dataset/DIOR/myyolo/val.txt# number of classes
nc: 20# class names
names: [airplane, airport, baseballfield, basketballcourt, bridge, chimney, dam,Expressway-Service-area, Expressway-toll-station, golffield, groundtrackfield, harbor,overpass, ship, stadium, storagetank, tenniscourt, trainstation, vehicle, windmill]
在训练时将data参数设置为DIOR.yaml即可使用yolo系列模型训练DIOR。YOLOv5,v7,v8通用。
parser.add_argument(--data, typestr, defaultdata/DIOR.yaml, helpdata.yaml path)
