深度学习必备知识——模型数据集Yolo与Voc格式文件相互转化

在深度学习中，第一步要做的往往就是处理数据集,尤其是学习百度飞桨PaddlePaddle的小伙伴，数据集经常要用Voc格式的，比如性能突出的ppyolo等模型。所以学会数据集转化的本领是十分必要的。这篇博客就带你一起进行Yolo与Voc格式的相互转化，附详细代码！

YOLO数据集介绍

Yolo数据集主要是txt文件，一般包括train文件夹和val文件夹，每一个文件夹下有与图片同名的txt文件，基本结构如下：

|–image

• ||–train
• ||–val

|–label

• ||–train
• ||–val

txt的标签如下图所示：

第一列为目标类别，后面四个数字为方框左上角与右下角的坐标，可以看到都是小于1的数字，是因为对应的整张图片的比例，所以就算图像被拉伸放缩，这种txt格式的标签也可以找到相应的目标。

VOC数据集介绍

VOC格式数据集一般有着如下的目录结构：

VOC_ROOT     #根目录├── JPEGImages         # 存放源图片│     ├── aaaa.jpg     │     ├── bbbb.jpg  │     └── cccc.jpg├── Annotations        # 存放xml文件，与JPEGImages中的图片一一对应，解释图片的内容等等│     ├── aaaa.xml │     ├── bbbb.xml │     └── cccc.xml └── ImageSets          └── Main├── train.txt    # txt文件中每一行包含一个图片的名称└── val.txt

其中JPEGImages目录中存放的是源图片的数据，(当然图片并不一定要是.jpg格式的，只是规定文件夹名字叫JPEGImages)；Annotations目录中存放的是标注数据，VOC的标注是xml格式的，文件名与JPEGImages中的图片一一对应。
重点看下xml格式的标注格式：

<annotation><folder>VOC_ROOT</folder>                           <filename>aaaa.jpg</filename>  # 文件名<size>                         # 图像尺寸（长宽以及通道数）                      <width>500</width><height>332</height><depth>3</depth></size><segmented>1</segmented>       # 是否用于分割（在图像物体识别中无所谓）<object>                       # 检测到的物体<name>horse</name>         # 物体类别<pose>Unspecified</pose>   # 拍摄角度，如果是自己的数据集就Unspecified<truncated>0</truncated>   # 是否被截断（0表示完整)<difficult>0</difficult>   # 目标是否难以识别（0表示容易识别）<bndbox>                   # bounding-box（包含左下角和右上角xy坐标）<xmin>100</xmin><ymin>96</ymin><xmax>355</xmax><ymax>324</ymax></bndbox></object><object>                       # 检测到多个物体<name>person</name><pose>Unspecified</pose><truncated>0</truncated><difficult>0</difficult><bndbox><xmin>198</xmin><ymin>58</ymin><xmax>286</xmax><ymax>197</ymax></bndbox></object>
</annotation>

Yolo转VOC

文件结构如下：

Yolo转VOC     #根目录├── dataset         │     ├── Annotations     │     ├── image  └──image图像│     └── label└──txt文件├── Yolo转VOC.py        # 代码文件

具体代码:

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：Yolo与VOC转化 
@File    ：Yolo转Voc.py
@IDE     ：PyCharm 
@Author  ：咋
@Date    ：2023/3/6 16:45 
"""
from xml.dom.minidom import Document
import os
import cv2# def makexml(txtPath, xmlPath, picPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径
def makexml(picPath, txtPath, xmlPath):  # txt所在文件夹路径，xml文件保存路径，图片所在文件夹路径"""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"""dic = {'0': "blue",  # 创建字典用来对类型进行转换'1': "red",  # 此处的字典要与自己的classes.txt文件中的类对应，且顺序要一致}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]])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()if __name__ == "__main__":picPath = "dataset/JPEGImages/"  # 图片所在文件夹路径，后面的/一定要带上txtPath = "dataset/YOLOLables/"  # txt所在文件夹路径，后面的/一定要带上xmlPath = "dataset/annotations/"  # xml文件保存路径，后面的/一定要带上makexml(picPath, txtPath, xmlPath)

VOC转Yolo

相当于上述操作的逆运算，这里直接给出代码：
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile

from lxml import etree

#自己的类别
classes = [“0”, “1”,‘2’,‘3’,‘person’]

classes=[“ball”]

TRAIN_RATIO = 80 #训练集比例

def clear_hidden_files(path):
dir_list = os.listdir(path)
for i in dir_list:
abspath = os.path.join(os.path.abspath(path), i)
if os.path.isfile(abspath):
if i.startswith(“._”):
os.remove(abspath)
else:
clear_hidden_files(abspath)

#数据转换
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)

#编写格式
def convert_annotation(image_id):
in_file = open(‘./dataset/annotations/%s.xml’ % image_id)
out_file = open(‘./dataset/YOLOLabels/%s.txt’ % image_id, ‘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').textcls = obj.find('name').textif 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))bb = convert((w, h), b)out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
in_file.close()
out_file.close()

#创建上述目录结构
wd = os.getcwd()

work_sapce_dir = os.path.join(wd, “dataset/”)
if not os.path.isdir(work_sapce_dir):
os.mkdir(work_sapce_dir)
annotation_dir = os.path.join(work_sapce_dir, “annotations/”)
if not os.path.isdir(annotation_dir):
os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, “JPEGImages/”)
if not os.path.isdir(image_dir):
os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, “YOLOLabels/”)
if not os.path.isdir(yolo_labels_dir):
os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)
yolov5_images_dir = os.path.join(work_sapce_dir, “images/”)
if not os.path.isdir(yolov5_images_dir):
os.mkdir(yolov5_images_dir)
clear_hidden_files(yolov5_images_dir)
yolov5_labels_dir = os.path.join(work_sapce_dir, “labels/”)
if not os.path.isdir(yolov5_labels_dir):
os.mkdir(yolov5_labels_dir)
clear_hidden_files(yolov5_labels_dir)
yolov5_images_train_dir = os.path.join(yolov5_images_dir, “train/”)
if not os.path.isdir(yolov5_images_train_dir):
os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_images_test_dir = os.path.join(yolov5_images_dir, “val/”)
if not os.path.isdir(yolov5_images_test_dir):
os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_train_dir = os.path.join(yolov5_labels_dir, “train/”)
if not os.path.isdir(yolov5_labels_train_dir):
os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)
yolov5_labels_test_dir = os.path.join(yolov5_labels_dir, “val/”)
if not os.path.isdir(yolov5_labels_test_dir):
os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)

#创建两个记录照片名字的文件
train_file = open(os.path.join(yolov5_images_dir, “yolov5_train.txt”), ‘w’)
test_file = open(os.path.join(yolov5_images_dir, “yolov5_val.txt”), ‘w’)
train_file.close()
test_file.close()
train_file = open(os.path.join(yolov5_images_dir, “yolov5_train.txt”), ‘a’)
test_file = open(os.path.join(yolov5_images_dir, “yolov5_val.txt”), ‘a’)

#随机划分
list_imgs = os.listdir(image_dir) # list image files
prob = random.randint(1, 100)
print(“Probability: %d” % prob)
for i in range(0, len(list_imgs)):
path = os.path.join(image_dir, list_imgs[i])
if os.path.isfile(path):
image_path = image_dir + list_imgs[i]
voc_path = list_imgs[i]
(nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))
(voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))
annotation_name = nameWithoutExtention + ‘.xml’
annotation_path = os.path.join(annotation_dir, annotation_name)
label_name = nameWithoutExtention + ‘.txt’
label_path = os.path.join(yolo_labels_dir, label_name)
prob = random.randint(1, 100)
print(“Probability: %d” % prob)
if (prob < TRAIN_RATIO): # train dataset
if os.path.exists(annotation_path):
train_file.write(image_path + ‘\n’)
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_train_dir + voc_path)
copyfile(label_path, yolov5_labels_train_dir + label_name)
else: # test dataset
if os.path.exists(annotation_path):
test_file.write(image_path + ‘\n’)
convert_annotation(nameWithoutExtention) # convert label
copyfile(image_path, yolov5_images_test_dir + voc_path)
copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()