python批量将json文件转换成xml文件

由于在使用SSD进行训练时，使用的是VOC数据格式，但是手上标注的数据集是labelme标注的json格式
首先这是文件夹的文件目录，其中labeljson文件夹下放标注的json文件，trainset文件夹下面放标注的jpg和json文件，最终生成的xml文件会放在Annotations下

代码如下：

#########################4、对.json格式的标签文件进行处理#######################
# coding=utf-8
import os
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 = "./labeljson/"                 # 原始xxx标注数据路径,需要更换成自己的数据集名称
saved_path = "./datasets/VOC2007/"      # 保存路径

#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=os.listdir(labelme_path)
files = [i.split("/")[-1].split(".json")[0] for i in 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("./trainset/"+json_file_ +".jpg").shape
    with codecs.open(saved_path + "Annotations/"+json_file_ + ".xml","w","utf-8") as xml:
        xml.write('\n')
        xml.write('\t' + 'JPEGImages' + '\n')#训练时我的训练图片是放在JPEGImages下的
        xml.write('\t' + json_file_ + ".jpg" + '\n')
        xml.write('\t\n')
        xml.write('\t\tThe Defect Detection\n')
        xml.write('\t\tDefect Detection\n')
        xml.write('\t\tflickr\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\tWZZ\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\t'+ str(width) + '\n')
        xml.write('\t\t'+ str(height) + '\n')
        xml.write('\t\t' + str(channels) + '\n')
        xml.write('\t\n')
        xml.write('\t\t0\n')
        for multi in json_file["shapes"]:
            points = np.array(multi["points"])
            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\n')
                print(json_filename,xmin,ymin,xmax,ymax,label)
        xml.write('')
        
# #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.split("/")[-1].split(".xml")[0] for i in total_files]
# test_filepath = "./test"
# for file in total_files:
#     ftrainval.write(file + "\n")
# #test
# for file in os.listdir(test_filepath):
#     ftest.write(file.split(".jpg")[0] + "\n")
# #split
# train_files,val_files = train_test_split(total_files,test_size=0.15,random_state=42)
# #train
# for file in train_files:
#     ftrain.write(file + "\n")
# #val
# for file in val_files:
#     fval.write(file + "\n")

# ftrainval.close()
# ftrain.close()
# fval.close()
# ftest.close()