yolov8-制作数据集，数据集格式转换（yolo格式-voc格式）附完整代码-编程知识

yolo训练时可使用的数据集格式为yolo格式以及voc格式， voc格式的数据集在训练时需要先转换为yolo格式，然后根据自己的数据集的位置更改yaml配置文件的文件路径即可。基于目前对Yolo系列训练模型的讲解已经很全面，所以本文主要讲解yolo数据集与voc数据集之间的转换。

两种数据集形式的最大区别就是yolo是直接采用txt文件保存模型的labels标签，如下图，每一行都代表着该图像中的一个标签GT。

而voc格式的标签是保存在Annotations目录下的xml文件当中，除此之外无差别。

目前大部分数据集的保存都是以voc格式，在我们训练yolo时需要将voc转换成yolo，说白了就是将基于xml的标签转换为txt保存的标签。

为了深刻理解数据转换的步骤，接下来以coco数据集（yolo格式）为例，实现yolo格式->voc格式->yolo格式的转换。

yolo格式->voc格式

coco数据集：

我们创建一个voc格式的文件目录：

Annotations：存放标签的xml文件

images: 数据集图片.jpg

imageSets: 存放分好的数据集: 训练集，验证集，测试集。

labels: 给yolo模型训练的标签（txt格式）

设置好文件路径：

        # coco数据集路径pre_pic = "../coco128/images/train2017/"txtPath = "../coco128/labels/train2017/"# 新voc数据集保存路径xmlPath = "../my_datasets/Annotations/"  # xml标签after_pic = "../my_datasets/images/"  # 训练集图片

实现yolo->voc的转换：

        # 将标签转换为xml格式makexml(pre_pic, txtPath, xmlPath)

def makexml(picPath, txtPath, xmlPath):"""此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件Args:picPath:   图片所在路径txtPath:   txt格式labels标签xmlPath:   xml文件保存路径Returns:"""dic = config["names"]  # 数据集类别字典files = os.listdir(txtPath)for i, name in enumerate(files):xmlBuilder = Document()annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签xmlBuilder.appendChild(annotation)txtFile = open(txtPath + name)txtList = txtFile.readlines()img = cv2.imread(picPath + name[0:-4] + ".jpg")Pheight, Pwidth, Pdepth = img.shapefolder = xmlBuilder.createElement("folder")  # folder标签foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset")folder.appendChild(foldercontent)annotation.appendChild(folder)  # folder标签结束filename = xmlBuilder.createElement("filename")  # filename标签filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")filename.appendChild(filenamecontent)annotation.appendChild(filename)  # filename标签结束size = xmlBuilder.createElement("size")  # size标签width = xmlBuilder.createElement("width")  # size子标签widthwidthcontent = xmlBuilder.createTextNode(str(Pwidth))width.appendChild(widthcontent)size.appendChild(width)  # size子标签width结束height = xmlBuilder.createElement("height")  # size子标签heightheightcontent = xmlBuilder.createTextNode(str(Pheight))height.appendChild(heightcontent)size.appendChild(height)  # size子标签height结束depth = xmlBuilder.createElement("depth")  # size子标签depthdepthcontent = xmlBuilder.createTextNode(str(Pdepth))depth.appendChild(depthcontent)size.appendChild(depth)  # size子标签depth结束annotation.appendChild(size)  # size标签结束for j in txtList:oneline = j.strip().split(" ")object = xmlBuilder.createElement("object")  # object 标签picname = xmlBuilder.createElement("name")  # name标签namecontent = xmlBuilder.createTextNode(dic[oneline[0] if type(oneline[0]) == int else int(oneline[0])])picname.appendChild(namecontent)object.appendChild(picname)  # name标签结束pose = xmlBuilder.createElement("pose")  # pose标签posecontent = xmlBuilder.createTextNode("Unspecified")pose.appendChild(posecontent)object.appendChild(pose)  # pose标签结束truncated = xmlBuilder.createElement("truncated")  # truncated标签truncatedContent = xmlBuilder.createTextNode("0")truncated.appendChild(truncatedContent)object.appendChild(truncated)  # truncated标签结束difficult = xmlBuilder.createElement("difficult")  # difficult标签difficultcontent = xmlBuilder.createTextNode("0")difficult.appendChild(difficultcontent)object.appendChild(difficult)  # difficult标签结束bndbox = xmlBuilder.createElement("bndbox")  # bndbox标签xmin = xmlBuilder.createElement("xmin")  # xmin标签mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth)xminContent = xmlBuilder.createTextNode(str(mathData))xmin.appendChild(xminContent)bndbox.appendChild(xmin)  # xmin标签结束ymin = xmlBuilder.createElement("ymin")  # ymin标签mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight)yminContent = xmlBuilder.createTextNode(str(mathData))ymin.appendChild(yminContent)bndbox.appendChild(ymin)  # ymin标签结束xmax = xmlBuilder.createElement("xmax")  # xmax标签mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth)xmaxContent = xmlBuilder.createTextNode(str(mathData))xmax.appendChild(xmaxContent)bndbox.appendChild(xmax)  # xmax标签结束ymax = xmlBuilder.createElement("ymax")  # ymax标签mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight)ymaxContent = xmlBuilder.createTextNode(str(mathData))ymax.appendChild(ymaxContent)bndbox.appendChild(ymax)  # ymax标签结束object.appendChild(bndbox)  # bndbox标签结束annotation.appendChild(object)  # object标签结束f = open(xmlPath + name[0:-4] + ".xml", 'w')xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')f.close()print("txt2xml ok!")

注意：xml文件中保存的是标签框的左上角点和右下角点的坐标。

数据集划分

为了让Yolo模型能读取到标签数据，我们需要将voc格式的xml标签转换为txt标签，并且需要对数据集进行划分：

def splitxml(trainval_percent, train_percent):"""将数据集分为train,val,test, 并存放在'../my_datasets/ImageSets'这个路径当中Args:trainval_percent:  训练集和验证集的比例train_percent:  训练集比例Returns:"""xmlfilepath = '../my_datasets/Annotations/'txtsavepath = '../my_datasets/ImageSets'total_xml = os.listdir(xmlfilepath)num = len(total_xml)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(txtsavepath + '/trainval.txt', 'w')ftest = open(txtsavepath + '/test.txt', 'w')ftrain = open(txtsavepath + '/train.txt', 'w')fval = open(txtsavepath + '/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()print("split dataset_xml ok!")

这一步会在ImageSets目录下生成：

这几个文件，文件里存放划分后数据集的文件名

voc格式 -> yolo格式

这里有个要注意的地方就是，xml文件里真实框的表示形式是对角点的坐标， Yolo格式标签的预测框表示形式为xywh（中心点坐标和预测框的长宽）并且需要做归一化。

    # 将xml格式数据转换为txtxml2yolo()

def xml2yolo():sets = ['train', 'test', 'val']classes = list(dic.values())for image_set in sets:# 先找labels文件夹如果不存在则创建if not os.path.exists('../my_datasets/labels/'):os.makedirs('../my_datasets/labels/')image_ids = open('../my_datasets/ImageSets/%s.txt' % (image_set)).read().strip().split()list_file = open('../my_datasets/%s.txt' % (image_set), 'w')# 将对应的文件_id以及全路径写进去并换行for image_id in image_ids:list_file.write('my_datasets/images/%s.jpg\n' % (image_id))convert_annotation(image_id, classes)# 关闭文件list_file.close()print("xml2txt ok!")def convert_annotation(image_id, classes):# 对应的通过year 找到相应的文件夹，并且打开相应image_id的xml文件，其对应bund文件# print('my_datasets/Annotations/%s.xml' % (image_id))in_file = open('../my_datasets/Annotations/%s.xml' % (image_id), encoding='utf-8')# 准备在对应的image_id 中写入对应的label，分别为# <object-class> <x> <y> <width> <height>out_file = open('../my_datasets/labels/%s.txt' % (image_id), 'w', encoding='utf-8')# 解析xml文件tree = ET.parse(in_file)# 获得对应的键值对root = tree.getroot()# 获得图片的尺寸大小size = root.find('size')# 如果xml内的标记为空，增加判断条件if size != None:# 获得宽w = int(size.find('width').text)# 获得高h = int(size.find('height').text)# 遍历目标objfor obj in root.iter('object'):# 获得difficult ？？difficult = obj.find('difficult').text# 获得类别 =string 类型cls = obj.find('name').text# 如果类别不是对应在我们预定好的class文件中，或difficult==1则跳过# if cls not in classes or int(difficult) == 1:#     continue# 通过类别名称找到idcls_id = classes.index(cls)# 找到bndbox 对象xmlbox = obj.find('bndbox')# 获取对应的bndbox的数组 = ['xmin','xmax','ymin','ymax']b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text))# print(image_id, cls, b)# 带入进行归一化操作# w = 宽, h = 高， b= bndbox的数组 = ['xmin','xmax','ymin','ymax']bb = convert((w, h), b)# bb 对应的是归一化后的(x,y,w,h)# 生成 calss x y w h 在label文件中out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')# 进行归一化操作
def convert(size, box):  # size:(原图w,原图h) , box:(xmin,xmax,ymin,ymax)dw = 1. / size[0]  # 1/wdh = 1. / size[1]  # 1/hx = (box[0] + box[1]) / 2.0  # 物体在图中的中心点x坐标y = (box[2] + box[3]) / 2.0  # 物体在图中的中心点y坐标w = box[1] - box[0]  # 物体实际像素宽度h = box[3] - box[2]  # 物体实际像素高度x = x * dw  # 物体中心点x的坐标比(相当于 x/原图w)w = w * dw  # 物体宽度的宽度比(相当于 w/原图w)y = y * dh  # 物体中心点y的坐标比(相当于 y/原图h)h = h * dh  # 物体宽度的宽度比(相当于 h/原图h)return (x, y, w, h)  # 返回 相对于原图的物体中心点的x坐标比,y坐标比,宽度比,高度比,取值范围[0-1]

执行完代码后会在数据集目录下生成

test,train,val三个txt文件，里面存放着各数据集的路径，训练模型时只需要将这三个txt文件的路径写入my_datasets.yaml里，然后让Yolo模型读取这个yaml文件即可。

完整代码：

# -*- coding: utf-8 -*-
# xml解析包
from xml.dom.minidom import Document
import os
import cv2
import random
import xml.etree.ElementTree as ET
import yaml
import shutil# 将数据转换为xml格式
def makexml(picPath, txtPath, xmlPath):"""此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件Args:picPath:   图片所在路径txtPath:   txt格式labels标签xmlPath:   xml文件保存路径Returns:"""dic = config["names"]  # 数据集类别字典files = os.listdir(txtPath)for i, name in enumerate(files):xmlBuilder = Document()annotation = xmlBuilder.createElement("annotation")  # 创建annotation标签xmlBuilder.appendChild(annotation)txtFile = open(txtPath + name)txtList = txtFile.readlines()img = cv2.imread(picPath + name[0:-4] + ".jpg")Pheight, Pwidth, Pdepth = img.shapefolder = xmlBuilder.createElement("folder")  # folder标签foldercontent = xmlBuilder.createTextNode("driving_annotation_dataset")folder.appendChild(foldercontent)annotation.appendChild(folder)  # folder标签结束filename = xmlBuilder.createElement("filename")  # filename标签filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")filename.appendChild(filenamecontent)annotation.appendChild(filename)  # filename标签结束size = xmlBuilder.createElement("size")  # size标签width = xmlBuilder.createElement("width")  # size子标签widthwidthcontent = xmlBuilder.createTextNode(str(Pwidth))width.appendChild(widthcontent)size.appendChild(width)  # size子标签width结束height = xmlBuilder.createElement("height")  # size子标签heightheightcontent = xmlBuilder.createTextNode(str(Pheight))height.appendChild(heightcontent)size.appendChild(height)  # size子标签height结束depth = xmlBuilder.createElement("depth")  # size子标签depthdepthcontent = xmlBuilder.createTextNode(str(Pdepth))depth.appendChild(depthcontent)size.appendChild(depth)  # size子标签depth结束annotation.appendChild(size)  # size标签结束for j in txtList:oneline = j.strip().split(" ")object = xmlBuilder.createElement("object")  # object 标签picname = xmlBuilder.createElement("name")  # name标签namecontent = xmlBuilder.createTextNode(dic[oneline[0] if type(oneline[0]) == int else int(oneline[0])])picname.appendChild(namecontent)object.appendChild(picname)  # name标签结束pose = xmlBuilder.createElement("pose")  # pose标签posecontent = xmlBuilder.createTextNode("Unspecified")pose.appendChild(posecontent)object.appendChild(pose)  # pose标签结束truncated = xmlBuilder.createElement("truncated")  # truncated标签truncatedContent = xmlBuilder.createTextNode("0")truncated.appendChild(truncatedContent)object.appendChild(truncated)  # truncated标签结束difficult = xmlBuilder.createElement("difficult")  # difficult标签difficultcontent = xmlBuilder.createTextNode("0")difficult.appendChild(difficultcontent)object.appendChild(difficult)  # difficult标签结束bndbox = xmlBuilder.createElement("bndbox")  # bndbox标签xmin = xmlBuilder.createElement("xmin")  # xmin标签mathData = int(((float(oneline[1])) * Pwidth + 1) - (float(oneline[3])) * 0.5 * Pwidth)xminContent = xmlBuilder.createTextNode(str(mathData))xmin.appendChild(xminContent)bndbox.appendChild(xmin)  # xmin标签结束ymin = xmlBuilder.createElement("ymin")  # ymin标签mathData = int(((float(oneline[2])) * Pheight + 1) - (float(oneline[4])) * 0.5 * Pheight)yminContent = xmlBuilder.createTextNode(str(mathData))ymin.appendChild(yminContent)bndbox.appendChild(ymin)  # ymin标签结束xmax = xmlBuilder.createElement("xmax")  # xmax标签mathData = int(((float(oneline[1])) * Pwidth + 1) + (float(oneline[3])) * 0.5 * Pwidth)xmaxContent = xmlBuilder.createTextNode(str(mathData))xmax.appendChild(xmaxContent)bndbox.appendChild(xmax)  # xmax标签结束ymax = xmlBuilder.createElement("ymax")  # ymax标签mathData = int(((float(oneline[2])) * Pheight + 1) + (float(oneline[4])) * 0.5 * Pheight)ymaxContent = xmlBuilder.createTextNode(str(mathData))ymax.appendChild(ymaxContent)bndbox.appendChild(ymax)  # ymax标签结束object.appendChild(bndbox)  # bndbox标签结束annotation.appendChild(object)  # object标签结束f = open(xmlPath + name[0:-4] + ".xml", 'w')xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')f.close()print("txt2xml ok!")def moveimage(pre_path, after_path):path_dir = os.listdir(pre_path)for file in path_dir:shutil.copy(pre_path + file, after_path)# 将数据集分为train,val,test
def splitxml(trainval_percent, train_percent):"""将数据集分为train,val,test, 并存放在'../my_datasets/ImageSets'这个路径当中Args:trainval_percent:  训练集和验证集的比例train_percent:  训练集比例Returns:"""xmlfilepath = '../my_datasets/Annotations/'txtsavepath = '../my_datasets/ImageSets'total_xml = os.listdir(xmlfilepath)num = len(total_xml)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(txtsavepath + '/trainval.txt', 'w')ftest = open(txtsavepath + '/test.txt', 'w')ftrain = open(txtsavepath + '/train.txt', 'w')fval = open(txtsavepath + '/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()print("split dataset_xml ok!")def xml2yolo():sets = ['train', 'test', 'val']classes = list(dic.values())for image_set in sets:# 先找labels文件夹如果不存在则创建if not os.path.exists('../my_datasets/labels/'):os.makedirs('../my_datasets/labels/')image_ids = open('../my_datasets/ImageSets/%s.txt' % (image_set)).read().strip().split()list_file = open('../my_datasets/%s.txt' % (image_set), 'w')# 将对应的文件_id以及全路径写进去并换行for image_id in image_ids:list_file.write('my_datasets/images/%s.jpg\n' % (image_id))convert_annotation(image_id, classes)# 关闭文件list_file.close()print("xml2txt ok!")def convert_annotation(image_id, classes):# 找到相应的文件夹，并且打开相应image_id的xml文件# print('my_datasets/Annotations/%s.xml' % (image_id))in_file = open('../my_datasets/Annotations/%s.xml' % (image_id), encoding='utf-8')# 准备在对应的image_id 中写入对应的label，分别为# <object-class> <x> <y> <width> <height>out_file = open('../my_datasets/labels/%s.txt' % (image_id), 'w', encoding='utf-8')# 解析xml文件tree = ET.parse(in_file)# 获得对应的键值对root = tree.getroot()# 获得图片的尺寸大小size = root.find('size')# 如果xml内的标记为空，增加判断条件if size != None:# 获得宽w = int(size.find('width').text)# 获得高h = int(size.find('height').text)# 遍历目标objfor obj in root.iter('object'):# 获得difficult ？？difficult = obj.find('difficult').text# 获得类别 =string 类型cls = obj.find('name').text# 如果类别不是对应在我们预定好的class文件中，或difficult==1则跳过（一般用不到）# if cls not in classes or int(difficult) == 1:#     continue# 通过类别名称找到idcls_id = classes.index(cls)# 找到bndbox 对象xmlbox = obj.find('bndbox')# 获取对应的bndbox的数组 = ['xmin','xmax','ymin','ymax']b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),float(xmlbox.find('ymax').text))# print(image_id, cls, b)# 带入进行归一化操作# w = 宽, h = 高， b= bndbox的数组 = ['xmin','xmax','ymin','ymax']bb = convert((w, h), b)# bb 对应的是归一化后的(x,y,w,h)# 生成 calss x y w h 在label文件中out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')# 进行归一化操作
def convert(size, box):  # size:(原图w,原图h) , box:(xmin,xmax,ymin,ymax)dw = 1. / size[0]  # 1/wdh = 1. / size[1]  # 1/hx = (box[0] + box[1]) / 2.0  # 物体在图中的中心点x坐标y = (box[2] + box[3]) / 2.0  # 物体在图中的中心点y坐标w = box[1] - box[0]  # 物体实际像素宽度h = box[3] - box[2]  # 物体实际像素高度x = x * dw  # 物体中心点x的坐标比(相当于 x/原图w)w = w * dw  # 物体宽度的宽度比(相当于 w/原图w)y = y * dh  # 物体中心点y的坐标比(相当于 y/原图h)h = h * dh  # 物体宽度的宽度比(相当于 h/原图h)return (x, y, w, h)  # 返回 相对于原图的物体中心点的x坐标比,y坐标比,宽度比,高度比,取值范围[0-1]config_file = "../my_datasets/my_datasets.yaml"
with open(config_file, "r") as file:config = yaml.safe_load(file)
dic = config["names"]  # 类别字典
if __name__ == "__main__":isyolo = Trueif isyolo:# Yolo格式数据路径# coco数据集路径pre_pic = "../coco128/images/train2017/"txtPath = "../coco128/labels/train2017/"# 新voc数据集保存路径xmlPath = "../my_datasets/Annotations/"  # xml标签after_pic = "../my_datasets/images/"  # 训练集图片# 将标签转换为xml格式makexml(pre_pic, txtPath, xmlPath)# 将图像移动到images中moveimage(pre_pic, after_pic)# 将数据集分为train,val,testsplitxml(0.9, 0.9)# 将xml格式数据转换为txtxml2yolo()