1.txt—>xml转化代码

如果我们手里只有YOLO标签的数据集，我们要进行VOC格式数据集的制作首先要进行标签的转化，以下是标签转化的脚本。
其中picPath为图片所在文件夹路径；
txtPath为你的YOLO标签对应的txt文件所在路径；
xmlPath为要生成的xml的路径；
同时将脚本中的类别信息换成你自己的。

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
    在自己的标注图片文件夹下建三个子文件夹，分别命名为picture、txt、xml
    """
    # names: ['-1', 'bearing', 'bolt', 'flange', 'gear', 'nut', 'retaining_ring', 'spring', 'washer']
    dic = {'0': "Double hexagonal column",  # 创建字典用来对类型进行转换
           '1': "Flange nut",  # 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
           '2': "Hexagon nut",
           '3': "Hexagon pillar",
           '4': "Hexagon screw",
           '5': "Hexagonal steel columnc",
           '6': "Hexagonal steel column",
           '7': "Keybar",
           '8': 'Plastic cushion pillar',
           '9': 'Rectangular nut',
           '10': 'Round head screw',
           '11': 'Spring washer',
           '12': 'T-shaped screw',
           }
    files = os.listdir(txtPath)
    for i, name in enumerate(files):
        print(f"�� [{i + 1}/{len(files)}] 正在处理：{name}")
        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")

        # 自动尝试读取 .jpg, .png, .jpeg 图像
        img = None
        img_extensions = [".jpg", ".png", ".jpeg"]
        for ext in img_extensions:
            img_path = os.path.join(picPath, name[0:-4] + ext)
            if os.path.exists(img_path):
                img = cv2.imread(img_path)
                if img is not None:
                    break

        # 如果图片读取失败，则跳过该文件
        if img is None:
            print(f"❌ 无法读取图片：{name[0:-4]}（支持格式：.jpg/.png/.jpeg），跳过")
            continue
        else:
            print(f"✅ 成功读取图片：{name[0:-4]}")

        Pheight, Pwidth, Pdepth = img.shape

        folder = 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width
        widthcontent = 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height
        heightcontent = 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depth
        depthcontent = 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()
        print(f"✅ 已保存 XML 文件：{name[0:-4]}.xml\n")


if __name__ == "__main__":
    picPath = "/home/yu/wz/duibi/retinanet-pytorch-master/VOCdeckit/VOC2007/JPEGImages/"  # 图片所在文件夹路径，后面的/一定要带上
    txtPath = "/home/yu/wz/duibi/retinanet-pytorch-master/VOCdeckit/VOC2007/YOLO/"  # txt所在文件夹路径，后面的/一定要带上
    xmlPath = "/home/yu/wz/duibi/retinanet-pytorch-master/VOCdeckit/VOC2007/Annotations/"  # xml文件保存路径，后面的/一定要带上
    makexml(picPath, txtPath, xmlPath)

2.VOC格式数据集处理

VOC格式数据集文件夹存放格式：
ImageSets里面等会是通过脚本生成的，前期只需要整理Annotation和JPEGImages
开始时候：

import os
import random
import xml.etree.ElementTree as ET

import numpy as np

from utils.utils import get_classes

#--------------------------------------------------------------------------------------------------------------------------------#
#   annotation_mode用于指定该文件运行时计算的内容
#   annotation_mode为0代表整个标签处理过程，包括获得VOCdevkit/VOC2007/ImageSets里面的txt以及训练用的2007_train.txt、2007_val.txt
#   annotation_mode为1代表获得VOCdevkit/VOC2007/ImageSets里面的txt
#   annotation_mode为2代表获得训练用的2007_train.txt、2007_val.txt
#--------------------------------------------------------------------------------------------------------------------------------#
annotation_mode     = 0
#-------------------------------------------------------------------#
#   必须要修改，用于生成2007_train.txt、2007_val.txt的目标信息
#   与训练和预测所用的classes_path一致即可
#   如果生成的2007_train.txt里面没有目标信息
#   那么就是因为classes没有设定正确
#   仅在annotation_mode为0和2的时候有效
#-------------------------------------------------------------------#
classes_path        = 'model_data/voc_classes.txt'
#--------------------------------------------------------------------------------------------------------------------------------#
#   trainval_percent用于指定(训练集+验证集)与测试集的比例，默认情况下 (训练集+验证集):测试集 = 9:1
#   train_percent用于指定(训练集+验证集)中训练集与验证集的比例，默认情况下 训练集:验证集 = 9:1
#   仅在annotation_mode为0和1的时候有效
#--------------------------------------------------------------------------------------------------------------------------------#
trainval_percent    = 1.0
train_percent       = 0.8
#-------------------------------------------------------#
#   指向VOC数据集所在的文件夹
#   默认指向根目录下的VOC数据集
#-------------------------------------------------------#
VOCdevkit_path  = 'VOCdevkit'

VOCdevkit_sets  = [('2007', 'train'), ('2007', 'val')]
classes, _      = get_classes(classes_path)

#-------------------------------------------------------#
#   统计目标数量
#-------------------------------------------------------#
photo_nums  = np.zeros(len(VOCdevkit_sets))
nums        = np.zeros(len(classes))
def convert_annotation(year, image_id, list_file):
    in_file = open(os.path.join(VOCdevkit_path, 'VOC%s/Annotations/%s.xml'%(year, image_id)), encoding='utf-8')
    tree=ET.parse(in_file)
    root = tree.getroot()

    for obj in root.iter('object'):
        difficult = 0 
        if obj.find('difficult')!=None:
            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 = (int(float(xmlbox.find('xmin').text)), int(float(xmlbox.find('ymin').text)), int(float(xmlbox.find('xmax').text)), int(float(xmlbox.find('ymax').text)))
        list_file.write(" " + ",".join([str(a) for a in b]) + ',' + str(cls_id))
        
        nums[classes.index(cls)] = nums[classes.index(cls)] + 1
        
if __name__ == "__main__":
    random.seed(0)
    if " " in os.path.abspath(VOCdevkit_path):
        raise ValueError("数据集存放的文件夹路径与图片名称中不可以存在空格，否则会影响正常的模型训练，请注意修改。")

    if annotation_mode == 0 or annotation_mode == 1:
        print("Generate txt in ImageSets.")
        xmlfilepath     = os.path.join(VOCdevkit_path, 'VOC2007/Annotations')
        saveBasePath    = os.path.join(VOCdevkit_path, 'VOC2007/ImageSets/Main')
        temp_xml        = os.listdir(xmlfilepath)
        total_xml       = []
        for xml in temp_xml:
            if xml.endswith(".xml"):
                total_xml.append(xml)

        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)  
        
        print("train and val size",tv)
        print("train size",tr)
        ftrainval   = open(os.path.join(saveBasePath,'trainval.txt'), 'w')  
        ftest       = open(os.path.join(saveBasePath,'test.txt'), 'w')  
        ftrain      = open(os.path.join(saveBasePath,'train.txt'), 'w')  
        fval        = open(os.path.join(saveBasePath,'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("Generate txt in ImageSets done.")

    if annotation_mode == 0 or annotation_mode == 2:
        print("Generate 2007_train.txt and 2007_val.txt for train.")
        type_index = 0
        for year, image_set in VOCdevkit_sets:
            image_ids = open(os.path.join(VOCdevkit_path, 'VOC%s/ImageSets/Main/%s.txt'%(year, image_set)), encoding='utf-8').read().strip().split()
            list_file = open('%s_%s.txt'%(year, image_set), 'w', encoding='utf-8')
            for image_id in image_ids:
                list_file.write('%s/VOC%s/JPEGImages/%s.jpg'%(os.path.abspath(VOCdevkit_path), year, image_id))

                convert_annotation(year, image_id, list_file)
                list_file.write('\n')
            photo_nums[type_index] = len(image_ids)
            type_index += 1
            list_file.close()
        print("Generate 2007_train.txt and 2007_val.txt for train done.")
        
        def printTable(List1, List2):
            for i in range(len(List1[0])):
                print("|", end=' ')
                for j in range(len(List1)):
                    print(List1[j][i].rjust(int(List2[j])), end=' ')
                    print("|", end=' ')
                print()

        str_nums = [str(int(x)) for x in nums]
        tableData = [
            classes, str_nums
        ]
        colWidths = [0]*len(tableData)
        len1 = 0
        for i in range(len(tableData)):
            for j in range(len(tableData[i])):
                if len(tableData[i][j]) > colWidths[i]:
                    colWidths[i] = len(tableData[i][j])
        printTable(tableData, colWidths)

        if photo_nums[0] <= 500:
            print("训练集数量小于500，属于较小的数据量，请注意设置较大的训练世代（Epoch）以满足足够的梯度下降次数（Step）。")

        if np.sum(nums) == 0:
            print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
            print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
            print("在数据集中并未获得任何目标，请注意修改classes_path对应自己的数据集，并且保证标签名字正确，否则训练将会没有任何效果！")
            print("（重要的事情说三遍）。")

修改代码中的：

voc_classes.txt存放自己的标签：

运行脚本最后生成：

控制台输出每个类别的信息则运行成功，

同时项目的根目录下生成：

打开这两个文件夹看一下是否包含自己的数据集信息：

至此，我们的数据集就制作完毕！