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：
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import os
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from typing import List, Any
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import numpy as np
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import codecs
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import json
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from glob import glob
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import cv2
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import shutil
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from sklearn.model_selection import train_test_split
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# 1.标签路径
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labelme_path = "LabelmeData/" # 原始labelme标注数据路径
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saved_path = "VOC2007/" # 保存路径
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isUseTest=True#是否创建test集
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# 2.创建要求文件夹
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if not os.path.exists(saved_path + "Annotations"):
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os.makedirs(saved_path + "Annotations")
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if not os.path.exists(saved_path + "JPEGImages/"):
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os.makedirs(saved_path + "JPEGImages/")
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if not os.path.exists(saved_path + "ImageSets/Main/"):
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os.makedirs(saved_path + "ImageSets/Main/")
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# 3.获取待处理文件
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files = glob(labelme_path + "*.json")
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files = [i.replace("\\","/").split("/")[-1].split(".json")[0] for i in files]
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print(files)
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# 4.读取标注信息并写入 xml
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for json_file_ in files:
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json_filename = labelme_path + json_file_ + ".json"
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json_file = json.load(open(json_filename, "r", encoding="utf-8"))
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height, width, channels = cv2.imread(labelme_path + json_file_ + ".jpg").shape
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with codecs.open(saved_path + "Annotations/" + json_file_ + ".xml", "w", "utf-8") as xml:
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xml.write('<annotation>\n')
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xml.write('\t<folder>' + 'WH_data' + '</folder>\n')
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xml.write('\t<filename>' + json_file_ + ".jpg" + '</filename>\n')
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xml.write('\t<source>\n')
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xml.write('\t\t<database>WH Data</database>\n')
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xml.write('\t\t<annotation>WH</annotation>\n')
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xml.write('\t\t<image>flickr</image>\n')
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xml.write('\t\t<flickrid>NULL</flickrid>\n')
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xml.write('\t</source>\n')
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xml.write('\t<owner>\n')
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xml.write('\t\t<flickrid>NULL</flickrid>\n')
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xml.write('\t\t<name>WH</name>\n')
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xml.write('\t</owner>\n')
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xml.write('\t<size>\n')
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xml.write('\t\t<width>' + str(width) + '</width>\n')
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xml.write('\t\t<height>' + str(height) + '</height>\n')
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xml.write('\t\t<depth>' + str(channels) + '</depth>\n')
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xml.write('\t</size>\n')
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xml.write('\t\t<segmented>0</segmented>\n')
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for multi in json_file["shapes"]:
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points = np.array(multi["points"])
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labelName=multi["label"]
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xmin = min(points[:, 0])
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xmax = max(points[:, 0])
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ymin = min(points[:, 1])
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ymax = max(points[:, 1])
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label = multi["label"]
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if xmax <= xmin:
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pass
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elif ymax <= ymin:
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pass
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else:
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xml.write('\t<object>\n')
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xml.write('\t\t<name>' + labelName+ '</name>\n')
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xml.write('\t\t<pose>Unspecified</pose>\n')
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xml.write('\t\t<truncated>1</truncated>\n')
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xml.write('\t\t<difficult>0</difficult>\n')
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xml.write('\t\t<bndbox>\n')
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xml.write('\t\t\t<xmin>' + str(int(xmin)) + '</xmin>\n')
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xml.write('\t\t\t<ymin>' + str(int(ymin)) + '</ymin>\n')
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xml.write('\t\t\t<xmax>' + str(int(xmax)) + '</xmax>\n')
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xml.write('\t\t\t<ymax>' + str(int(ymax)) + '</ymax>\n')
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xml.write('\t\t</bndbox>\n')
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xml.write('\t</object>\n')
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print(json_filename, xmin, ymin, xmax, ymax, label)
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xml.write('</annotation>')
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# 5.复制图片到 VOC2007/JPEGImages/下
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image_files = glob(labelme_path + "*.jpg")
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print("copy image files to VOC007/JPEGImages/")
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for image in image_files:
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shutil.copy(image, saved_path + "JPEGImages/")
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# 6.split files for txt
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txtsavepath = saved_path + "ImageSets/Main/"
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ftrainval = open(txtsavepath + '/trainval.txt', 'w')
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ftest = open(txtsavepath + '/test.txt', 'w')
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ftrain = open(txtsavepath + '/train.txt', 'w')
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fval = open(txtsavepath + '/val.txt', 'w')
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total_files = glob("./VOC2007/Annotations/*.xml")
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total_files = [i.replace("\\","/").split("/")[-1].split(".xml")[0] for i in total_files]
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trainval_files=[]
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test_files=[]
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if isUseTest:
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trainval_files, test_files = train_test_split(total_files, test_size=0.15, random_state=55)
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else:
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trainval_files=total_files
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for file in trainval_files:
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ftrainval.write(file + "\n")
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# split
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train_files, val_files = train_test_split(trainval_files, test_size=0.15, random_state=55)
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# train
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for file in train_files:
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ftrain.write(file + "\n")
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# val
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for file in val_files:
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fval.write(file + "\n")
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for file in test_files:
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print(file)
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ftest.write(file + "\n")
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ftrainval.close()
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ftrain.close()
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fval.close()
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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。
文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。
原文链接：wanghao.blog.csdn.net/article/details/105766915
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