Labelme标注的数据集转VOC2007格式的数据集。

VOC2007数据文件夹说明

1)JPEGImages文件夹

文件夹里包含了训练图片和测试图片，混放在一起

2)Annatations文件夹

文件夹存放的是xml格式的标签文件，每个xml文件都对应于JPEGImages文件夹的一张图片

3)ImageSets文件夹

Main存放的是图像物体识别的数据，Main里面有test.txt, train.txt, val.txt,trainval.txt.这四个文件我们后面会生成

XML说明

<?xml version="1.0" encoding="utf-8"?>

<annotation>

        <source>

                  <image>optic rs image</image>

                  <annotation>Lmars RSDS2016</annotation>

                  <flickrid>0</flickrid>

                  <database>Lmars Detection Dataset of RS</database>

        </source>

        <object>

<!--bounding box的四个坐标，分别为左上角和右下角的x，y坐标-->

                  <bndbox>

                           <xmin>690</xmin>

                           <ymin>618</ymin>

                           <ymax>678</ymax>

                           <xmax>748</xmax>

                  </bndbox>

<!--是否容易被识别，0表示容易，1表示困难-->

                  <difficult>0</difficult>

                  <pose>Left</pose>

<!--物体类别-->

                  <name>aircraft</name>

<!--是否被裁剪，0表示完整，1表示不完整-->

                  <truncated>1</truncated>

        </object>

        <filename>aircraft_773.jpg</filename>

        <!--是否用于分割，0表示用于，1表示不用于-->

        <segmented>0</segmented>

<!--图片所有者-->

        <owner>

                  <name>Lmars, Wuhan University</name>

                  <flickrid>I do not know</flickrid>

        </owner>

        <folder>RSDS2016</folder>

        <size>

                  <width>1044</width>

                  <depth>3</depth>

                  <height>915</height>

        </size>

</annotation>

完整代码：

import os
 
from typing import List, Any
 
 
 
import numpy as np
 
import codecs
 
import json
 
from glob import glob
 
import cv2
 
import shutil
 
from sklearn.model_selection import train_test_split
 
# 1.标签路径
 
labelme_path = "LabelmeData/"  # 原始labelme标注数据路径
 
saved_path = "VOC2007/"  # 保存路径
 
isUseTest=True#是否创建test集
 
# 2.创建要求文件夹
 
if not os.path.exists(saved_path + "Annotations"):
 
    os.makedirs(saved_path + "Annotations")
 
if not os.path.exists(saved_path + "JPEGImages/"):
 
    os.makedirs(saved_path + "JPEGImages/")
 
if not os.path.exists(saved_path + "ImageSets/Main/"):
 
    os.makedirs(saved_path + "ImageSets/Main/")
 
# 3.获取待处理文件
 
files = glob(labelme_path + "*.json")
 
files = [i.replace("\\","/").split("/")[-1].split(".json")[0] for i in files]
 
print(files)
 
# 4.读取标注信息并写入 xml
 
for json_file_ in files:
 
    json_filename = labelme_path + json_file_ + ".json"
 
    json_file = json.load(open(json_filename, "r", encoding="utf-8"))
 
    height, width, channels = cv2.imread(labelme_path + json_file_ + ".jpg").shape
 
    with codecs.open(saved_path + "Annotations/" + json_file_ + ".xml", "w", "utf-8") as xml:
 
        xml.write('<annotation>\n')
 
        xml.write('\t<folder>' + 'WH_data' + '</folder>\n')
 
        xml.write('\t<filename>' + json_file_ + ".jpg" + '</filename>\n')
 
        xml.write('\t<source>\n')
 
        xml.write('\t\t<database>WH Data</database>\n')
 
        xml.write('\t\t<annotation>WH</annotation>\n')
 
        xml.write('\t\t<image>flickr</image>\n')
 
        xml.write('\t\t<flickrid>NULL</flickrid>\n')
 
        xml.write('\t</source>\n')
 
        xml.write('\t<owner>\n')
 
        xml.write('\t\t<flickrid>NULL</flickrid>\n')
 
        xml.write('\t\t<name>WH</name>\n')
 
        xml.write('\t</owner>\n')
 
        xml.write('\t<size>\n')
 
        xml.write('\t\t<width>' + str(width) + '</width>\n')
 
        xml.write('\t\t<height>' + str(height) + '</height>\n')
 
        xml.write('\t\t<depth>' + str(channels) + '</depth>\n')
 
        xml.write('\t</size>\n')
 
        xml.write('\t\t<segmented>0</segmented>\n')
 
        for multi in json_file["shapes"]:
 
            points = np.array(multi["points"])
 
            labelName=multi["label"]
 
            xmin = min(points[:, 0])
 
            xmax = max(points[:, 0])
 
            ymin = min(points[:, 1])
 
            ymax = max(points[:, 1])
 
            label = multi["label"]
 
            if xmax <= xmin:
 
                pass
 
            elif ymax <= ymin:
 
                pass
 
            else:
 
                xml.write('\t<object>\n')
 
                xml.write('\t\t<name>' + labelName+ '</name>\n')
 
                xml.write('\t\t<pose>Unspecified</pose>\n')
 
                xml.write('\t\t<truncated>1</truncated>\n')
 
                xml.write('\t\t<difficult>0</difficult>\n')
 
                xml.write('\t\t<bndbox>\n')
 
                xml.write('\t\t\t<xmin>' + str(int(xmin)) + '</xmin>\n')
 
                xml.write('\t\t\t<ymin>' + str(int(ymin)) + '</ymin>\n')
 
                xml.write('\t\t\t<xmax>' + str(int(xmax)) + '</xmax>\n')
 
                xml.write('\t\t\t<ymax>' + str(int(ymax)) + '</ymax>\n')
 
                xml.write('\t\t</bndbox>\n')
 
                xml.write('\t</object>\n')
 
                print(json_filename, xmin, ymin, xmax, ymax, label)
 
        xml.write('</annotation>')
 
# 5.复制图片到 VOC2007/JPEGImages/下
 
image_files = glob(labelme_path + "*.jpg")
 
print("copy image files to VOC007/JPEGImages/")
 
for image in image_files:
 
    shutil.copy(image, saved_path + "JPEGImages/")
 
# 6.split files for txt
 
txtsavepath = saved_path + "ImageSets/Main/"
 
ftrainval = open(txtsavepath + '/trainval.txt', 'w')
 
ftest = open(txtsavepath + '/test.txt', 'w')
 
ftrain = open(txtsavepath + '/train.txt', 'w')
 
fval = open(txtsavepath + '/val.txt', 'w')
 
total_files = glob("./VOC2007/Annotations/*.xml")
 
total_files = [i.replace("\\","/").split("/")[-1].split(".xml")[0] for i in total_files]
 
trainval_files=[]
 
test_files=[]
 
if isUseTest:
 
    trainval_files, test_files = train_test_split(total_files, test_size=0.15, random_state=55)
 
else:
 
    trainval_files=total_files
 
for file in trainval_files:
 
    ftrainval.write(file + "\n")
 
# split
 
train_files, val_files = train_test_split(trainval_files, test_size=0.15, random_state=55)
 
# train
 
for file in train_files:
 
    ftrain.write(file + "\n")
 
# val
 
for file in val_files:
 
    fval.write(file + "\n")
 
for file in test_files:
 
    print(file)
 
    ftest.write(file + "\n")
 
ftrainval.close()
 
ftrain.close()
 
fval.close()
 
ftest.close()      

注：训练集和验证集的划分方法是采用 sklearn.model_selection.train_test_split 进行分割的。