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。
