yolo数据集 标签格式转换以及数据集划分_yolo的标签

1 xml格式转txt格式

import xml.etree.ElementTree as ET
 
import pickle
import os
from os import listdir, getcwd
from os.path import join
import glob
 
classes = ["", "", "", ""]      #这里是需要改的第一个地方，写入自己的标签类型，不可多写，不可少写
 
 
def convert(size, box):
    dw = 1.0 / size[0]
    dh = 1.0 / 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)
 
 
def convert_annotation(image_name):
    in_file = open("D:\\data set\\Pepper_3.0\\test\\xmllabels\\" + image_name[:-3] + 'xml')  # 这里是需要改的第二个地方，输入xml文件的路径
    out_file = open("D:\\data set\\Pepper_3.0\\test\\labels\\" + image_name[:-3] + 'txt', 'w')  # 这里是需要改的第三个地方，输入用于存放转换后的txt文件的路径
 
    f = open("D:\\data set\\Pepper_3.0\\test\\xmllabels\\"+ image_name[:-3] + 'xml')
    xml_text = f.read()
    root = ET.fromstring(xml_text)
    f.close()
    size = root.find('size')
    w = int(size.find('width').text)
    h = int(size.find('height').text)
 
    for obj in root.iter('object'):
        cls = obj.find('name').text
        if cls not in classes:
            print(cls)
            continue
        cls_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')
 
 
 
 
 
wd = getcwd()
 
if __name__ == '__main__':
 
    for image_path in glob.glob("D:\\data set\\Pepper_3.0\\test\\images\\*.jpg"):  # 这里是需要改的最后一个地方，输入图片的路径，每一张图图片都要有一个xml文件对应，没有对应xml文件的图片要删除，当然，这里图片的命名要和对应xml文件的命名一致，之后就可以输出对应命名的txt文件
        image_name = image_path.split('\\')[-1]
        convert_annotation(image_name)
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2 txt格式转xml格式

from xml.dom.minidom import Document
import os
import cv2
 
 
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
    """
    # 创建字典用来对类型进行转换，要与classes.txt文件中的类对应，且顺序要一致
    dic = {'0': "Capsicum anthracnose", '1': "Viral diseases", '2': "Bacterial diseases", '3': "Umbilical rot"}
    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")  # 注意这里的图片后缀，.jpg/.png
        Pheight, Pwidth, Pdepth = img.shape
 
        folder = xmlBuilder.createElement("folder")  # folder标签
        foldercontent = xmlBuilder.createTextNode("datasetRGB")
        folder.appendChild(foldercontent)
        annotation.appendChild(folder)
 
        filename = xmlBuilder.createElement("filename")  # filename标签
        filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")
        filename.appendChild(filenamecontent)
        annotation.appendChild(filename)
 
        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)
 
        height = xmlBuilder.createElement("height")  # size子标签height
        heightcontent = xmlBuilder.createTextNode(str(Pheight))
        height.appendChild(heightcontent)
        size.appendChild(height)
 
        depth = xmlBuilder.createElement("depth")  # size子标签depth
        depthcontent = xmlBuilder.createTextNode(str(Pdepth))
        depth.appendChild(depthcontent)
        size.appendChild(depth)
 
        annotation.appendChild(size)
 
        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)
 
            pose = xmlBuilder.createElement("pose")  # pose标签
            posecontent = xmlBuilder.createTextNode("Unspecified")
            pose.appendChild(posecontent)
            object.appendChild(pose)
 
            truncated = xmlBuilder.createElement("truncated")  # truncated标签
            truncatedContent = xmlBuilder.createTextNode("0")
            truncated.appendChild(truncatedContent)
            object.appendChild(truncated)
 
            difficult = xmlBuilder.createElement("difficult")  # difficult标签
            difficultcontent = xmlBuilder.createTextNode("0")
            difficult.appendChild(difficultcontent)
            object.appendChild(difficult)
 
            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)
 
            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)
 
            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)
 
            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)
 
            object.appendChild(bndbox)  # bndbox标签结束
 
            annotation.appendChild(object)
 
        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 = "D:\\data set\\d1_2.0\\d1_2.0\\"  # 图片所在文件夹路径，后面的\\一定要带上
    txtPath = "D:\\data set\\d1_2.0\\d1_2.0txt\\"  # txt所在文件夹路径，后面的\\一定要带上
    xmlPath = "D:\\data set\\d1_2.0\\d1_2.0xml\\"  # xml文件保存路径，后面的\\一定要带上
    makexml(picPath, txtPath, xmlPath)

3 数据集划分训练集验证集及测试集

import os
import shutil
import random
 
random.seed(0)
 
 
def split_data(file_path,xml_path, new_file_path, train_rate, val_rate, test_rate):
    each_class_image = []
    each_class_label = []
    for image in os.listdir(file_path):
        each_class_image.append(image)
    for label in os.listdir(xml_path):
        each_class_label.append(label)
    data=list(zip(each_class_image,each_class_label))
    total = len(each_class_image)
    random.shuffle(data)
    each_class_image,each_class_label=zip(*data)
    train_images = each_class_image[0:int(train_rate * total)]
    val_images = each_class_image[int(train_rate * total):int((train_rate + val_rate) * total)]
    test_images = each_class_image[int((train_rate + val_rate) * total):]
    train_labels = each_class_label[0:int(train_rate * total)]
    val_labels = each_class_label[int(train_rate * total):int((train_rate + val_rate) * total)]
    test_labels = each_class_label[int((train_rate + val_rate) * total):]
 
    for image in train_images:
        print(image)
        old_path = file_path + '/' + image
        new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + image
        shutil.copy(old_path, new_path)
 
    for label in train_labels:
        print(label)
        old_path = xml_path + '/' + label
        new_path1 = new_file_path + '/' + 'train' + '/' + 'labels'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + label
        shutil.copy(old_path, new_path)
 
    for image in val_images:
        old_path = file_path + '/' + image
        new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + image
        shutil.copy(old_path, new_path)
 
    for label in val_labels:
        old_path = xml_path + '/' + label
        new_path1 = new_file_path + '/' + 'val' + '/' + 'labels'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + label
        shutil.copy(old_path, new_path)
 
    for image in test_images:
        old_path = file_path + '/' + image
        new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + image
        shutil.copy(old_path, new_path)
 
    for label in test_labels:
        old_path = xml_path + '/' + label
        new_path1 = new_file_path + '/' + 'test' + '/' + 'labels'
        if not os.path.exists(new_path1):
            os.makedirs(new_path1)
        new_path = new_path1 + '/' + label
        shutil.copy(old_path, new_path)
 
 
if __name__ == '__main__':
    file_path = "D:/data set/Pepper_data/image"
    xml_path = "D:/data set/Pepper_data/label"
    new_file_path = "D:/data set/Pepper_3.0"
    split_data(file_path, xml_path, new_file_path, train_rate=0.6, val_rate=0.2, test_rate=0.2)