python script------convert json to xml or txt files in batches

Convert json to xml files in batches

import os
from json import loads
from dicttoxml import dicttoxml
from xml.dom.minidom import parseString


def jsonToXml(json_path, xml_path):  # 打开josn和xml文件夹，使用程序需要在指定位置新建一个xml文件夹。
    with open(json_path, 'r', encoding='UTF-8')as json_file:
        load_dict = loads(json_file.read())
    my_item_func = lambda x: 'Annotation'
    xml = dicttoxml(load_dict, custom_root='Annotations', item_func=my_item_func, attr_type=False)
    dom = parseString(xml)
    with open(xml_path, 'w', encoding='UTF-8')as xml_file:
        xml_file.write(dom.toprettyxml())


def json_to_xml(json_dir, xml_dir):  # 文件转换函数
    if (os.path.exists(xml_dir) == False):
        os.makedirs(xml_dir)
    dir = os.listdir(json_dir)
    for file in dir:
        file_list = file.split(".")
        if (file_list[-1] == 'json'):
            jsonToXml(os.path.join(json_dir, file), os.path.join(xml_dir, file_list[0] + '.xml'))
    print('==============')
    print('文件转换已完成')
    print('==============')


if __name__ == '__main__':
    json_path = r"F:\Desktop\myfile1"
    # 转换单个.json文件json_path = "E:/zhongnan_Data/open_fire_label/1.json"
    xml_path = "F:\Desktop\myfile2"
    # 保存单个.xml文件xml_path = "E:/zhongnan_Data/xml/1.xml"
    json_to_xml(json_path, xml_path)

Convert json to txt files in batches

# COCO 格式的数据集转化为 YOLO 格式的数据集
# --json_path 输入的json文件路径
# --save_path 保存的文件夹名字，默认为当前目录下的labels。

import os
import json
from tqdm import tqdm
import argparse

parser = argparse.ArgumentParser()
# 这里根据自己的json文件位置，换成自己的就行
parser.add_argument('--json_path',
                    default=r'D:\Dataset\UG2_train\annotations/rtts.json', type=str,
                    help="input: coco format(json)")
# 这里设置.txt文件保存位置
parser.add_argument('--save_path', default=r'D:\Dataset\UG2_train\RTTS_txt', type=str,
                    help="specify where to save the output dir of labels")
arg = parser.parse_args()


def convert(size, box):
    dw = 1. / (size[0])
    dh = 1. / (size[1])
    x = box[0] + box[2] / 2.0
    y = box[1] + box[3] / 2.0
    w = box[2]
    h = box[3]
    # round函数确定(xmin, ymin, xmax, ymax)的小数位数
    x = round(x * dw, 6)
    w = round(w * dw, 6)
    y = round(y * dh, 6)
    h = round(h * dh, 6)
    return (x, y, w, h)


if __name__ == '__main__':
    json_file = arg.json_path  # COCO Object Instance 类型的标注
    ana_txt_save_path = arg.save_path  # 保存的路径

    data = json.load(open(json_file, 'r'))
    if not os.path.exists(ana_txt_save_path):
        os.makedirs(ana_txt_save_path)

    id_map = {
    
    }  # coco数据集的id不连续！重新映射一下再输出！
    with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f:
        # 写入classes.txt
        for i, category in enumerate(data['categories']):
            f.write(f"{
      
      category['name']}\n")
            id_map[category['id']] = i
    print(id_map)
    # 这里需要根据自己的需要，更改写入图像相对路径的文件位置。
    list_file = open(os.path.join(ana_txt_save_path, 'train2017.txt'), 'w')
    for img in tqdm(data['images']):
        filename = img["file_name"]
        img_width = img["width"]
        img_height = img["height"]
        img_id = img["id"]
        head, tail = os.path.splitext(filename)
        ana_txt_name = head + ".txt"  # 对应的txt名字，与jpg一致
        f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
        for ann in data['annotations']:
            if ann['image_id'] == img_id:
                box = convert((img_width, img_height), ann["bbox"])
                f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
        f_txt.close()
        # 将图片的相对路径写入train2017或val2017的路径
        list_file.write('D:/Dataset/UG2_train/RTTS/%s.png\n' % (head))
    list_file.close()