数据集格式转化 xml2txt & 批量文件名修改 & json2txt & 随机选择（同名）文件
提示：本文用于学习记录
文章目录
- 前言
- 一、格式转化 xml2txt
-
- 1.1 代码示例如下：(方法1)
- 1.2 代码示例如下：(方法2)
- 1.3 代码示例如下：(方法3)
- 二、批量文件名修改
-
- 2.1 图片路径
- 2.2 方法一
- 2.3 方法二
- 2.4 修改结果
- 三、将 json 文件转化为 txt 文件的几种情况
-
- 3.1 样例1
- 3.1 样例2
- 3.3 样例3
- 四、随机选择一定数量文件
- 总结
前言
为了训练深度学习模型，要整理大量的标注数据，需统一不同格式的标注数据，一般情况下习惯读取 txt 格式的数据，但实际中经常遇到 XML 格式或者 json 格式的标注数据，需要对数据格式进行转化；在处理数据的时候，需要对大量图片和文本进行重命名。
一、格式转化 xml2txt
1.1 代码示例如下：(方法1)
import xml.etree.ElementTree as ET
import os
def convert(size, box):
x_center = (box[0] + box[1]) / 2.0
y_center = (box[2] + box[3]) / 2.0
x = x_center / size[0]
y = y_center / size[1]
w = (box[1] - box[0]) / size[0]
h = (box[3] - box[2]) / size[1]
return (x, y, w, h)
def convert_annotation(xml_files_path, save_txt_files_path, classes):
xml_files = os.listdir(xml_files_path)
print(xml_files)
for xml_name in xml_files:
print(xml_name)
xml_file = os.path.join(xml_files_path, xml_name)
out_txt_path = os.path.join(save_txt_files_path, xml_name.split('.')[0] + '.txt')
out_txt_f = open(out_txt_path, 'w')
tree = ET.parse(xml_file)
root = tree.getroot()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
# difficult = obj.find('difficult').text
if obj.find('difficult'):
difficult = float(obj.find('difficult').text)
else:
difficult = 0
cls = obj.find('name').text
if cls not in classes or int(difficult) == 1:
continue
cls_id = classes.index(cls)
xmlbox = obj.find('bndbox')
b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
float(xmlbox.find('ymax').text))
# b=(xmin, xmax, ymin, ymax)
print(w, h, b)
bb = convert((w, h), b)
out_txt_f.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
if __name__ == "__main__":
# 需要转换的类别，需要一一对应
classes1 = ['D00', 'D10', 'D20', 'D40']
# 2、voc格式的xml标签文件路径
xml_files1 = r'C:\Users\bistu\Desktop\RoadDamage\labels'
# 3、转化为yolo格式的txt标签文件存储路径
save_txt_files1 = r'C:\Users\bistu\Desktop\batch\label'
convert_annotation(xml_files1, save_txt_files1, classes1)
1.2 代码示例如下：(方法2)
import os
import xml.etree.ElementTree as ET
# xml文件存放目录（不要以\结尾）
input_dir = r'E:\PycharmProjects\yolov5-master\train\India\Annotations\xmls'
# 输出txt文件目录（不要以\结尾）
out_dir = r'E:\PycharmProjects\yolov5-master\India'
class_list = []
# 获取目录所有xml文件
def file_name(input_dir):
F = []
for root, dirs, files in os.walk(input_dir):
for file in files:
# print file.decode('gbk') #文件名中有中文字符时转码
if os.path.splitext(file)[1] == '.xml':
t = os.path.splitext(file)[0]
F.append(t) # 将所有的文件名添加到L列表中
return F # 返回L列表
# 获取所有分类
def get_class(filelist):
for i in filelist:
f_dir = input_dir + "\\" + i + ".xml"
in_file = open(f_dir, encoding='UTF-8')
filetree = ET.parse(in_file)
in_file.close()
root = filetree.getroot()
for obj in root.iter('object'):
# difficult = obj.find('difficult').text
cls = obj.find('name').text
if cls not in class_list:
class_list.append(cls)
def ConverCoordinate(imgshape, bbox):
# 将xml像素坐标转换为txt归一化后的坐标
xmin, xmax, ymin, ymax = bbox
width = imgshape[0]
height = imgshape[1]
dw = 1. / width
dh = 1. / height
x = (xmin + xmax) / 2.0
y = (ymin + ymax) / 2.0
w = xmax - xmin
h = ymax - ymin
# 归一化
x = x * dw
y = y * dh
w = w * dw
h = h * dh
return x, y, w, h
def readxml(i):
f_dir = input_dir + "\\" + i + ".xml"
txtresult = ''
outfile = open(f_dir, encoding='UTF-8')
filetree = ET.parse(outfile)
outfile.close()
root = filetree.getroot()
# 获取图片大小
size = root.find('size')
width = int(size.find('width').text)
height = int(size.find('height').text)
imgshape = (width, height)
# 转化为yolov5的格式
for obj in root.findall('object'):
# 获取类别名
obj_name = obj.find('name').text
obj_id = class_list.index(obj_name)
# 获取每个obj的bbox框的左上和右下坐标
bbox = obj.find('bndbox')
xmin = float(bbox.find('xmin').text)
xmax = float(bbox.find('xmax').text)
ymin = float(bbox.find('ymin').text)
ymax = float(bbox.find('ymax').text)
bbox_coor = (xmin, xmax, ymin, ymax)
x, y, w, h = ConverCoordinate(imgshape, bbox_coor)
txt = '{} {} {} {} {}\n'.format(obj_id, x, y, w, h)
txtresult = txtresult + txt
# print(txtresult)
f = open(out_dir + "\\" + i + ".txt", 'a')
f.write(txtresult)
f.close()
# 获取文件夹下的所有文件
filelist = file_name(input_dir)
# 获取所有分类
get_class(filelist)
# 打印class
print(class_list)
# xml转txt
for i in filelist:
readxml(i)
# 在out_dir下生成一个class文件
f = open(out_dir + "\\classes.txt", 'a')
classresult = ''
for i in class_list:
classresult = classresult + i + "\n"
f.write(classresult)
f.close()
1.3 代码示例如下：(方法3)
import os
import xml.etree.ElementTree as ET
def convert_coordinates(size, box):
dw = 1.0 / size[0]
dh = 1.0 / size[1]
x = (box[0] + box[1]) / 2.0
y = (box[2] + box[3]) / 2.0
w = box[1] - box[0]
h = box[3] - box[2]
x = x * dw
w = w * dw
y = y * dh
h = h * dh
return (x, y, w, h)
def convert_xml_to_txt(xml_folder, output_folder, classes):
os.makedirs(output_folder, exist_ok=True)
xml_files = [f for f in os.listdir(xml_folder) if f.endswith('.xml')]
for xml_file in xml_files:
tree = ET.parse(os.path.join(xml_folder, xml_file))
root = tree.getroot()
image_name = os.path.splitext(xml_file)[0]
txt_file_path = os.path.join(output_folder, f"{image_name}.txt")
with open(txt_file_path, 'w') as txt_file:
size = (int(root.find('size/width').text), int(root.find('size/height').text))
for obj in root.findall('object'):
class_name = obj.find('name').text
class_id = classes.index(class_name)
box = obj.find('bndbox')
xmin = int(box.find('xmin').text)
ymin = int(box.find('ymin').text)
xmax = int(box.find('xmax').text)
ymax = int(box.find('ymax').text)
bbox = (xmin, xmax, ymin, ymax)
yolo_bbox = convert_coordinates(size, bbox)
txt_file.write(f"{class_id} {yolo_bbox[0]} {yolo_bbox[1]} {yolo_bbox[2]} {yolo_bbox[3]}\n")
if __name__ == "__main__":
xml_folder = "Annotation/hx9"
output_folder = "labels/hx9"
classes = ['off-road vehicle', 'car', 'suv', 'large van', 'truck', 'flatbed',
'van'] # List of class names in your dataset
convert_xml_to_txt(xml_folder, output_folder, classes)
二、批量文件名修改
2.1 图片路径
2.2 方法一
代码示例如下：
import os
class BatchRename():
def rename(self):
path = r"C:\Users\bistu\Desktop\batch\batchs" # 图片所在路径
filelist = os.listdir(path)
total_num = len(filelist) # 计数，图片数量
i = 0 # 从零开始计数，可进行修改，比如315
for item in filelist:
if item.endswith('.jpg'): # 图片文件后缀名，区分大小写
src = os.path.join(os.path.abspath(path), item)
dst = os.path.join(os.path.abspath(path), '任意添加00/a'+str(i)+'.jpg')
try:
os.rename(src, dst)
i += 1
except:
continue
print('total %d to rename & converted %d png'%(total_num, i))
if __name__=='__main__':
demo = BatchRename()
demo.rename()
2.3 方法二
import os
import sys
def rename():
path=input("请输入路径(例如D:\\\\picture)：")
name=input("请输入开头名:")
startNumber=input("请输入开始数:")
fileType=input("请输入后缀名（如 .jpg、.txt等等）:")
print("正在生成以"+name+startNumber+fileType+"迭代的文件名")
count=0
filelist=os.listdir(path)
for files in filelist:
Olddir=os.path.join(path,files)
if os.path.isdir(Olddir):
continue
Newdir=os.path.join(path,name+str(count+int(startNumber))+fileType)
os.rename(Olddir,Newdir)
count+=1
print("一共修改了"+str(count)+"个文件")
rename()
2.4 修改结果
三、将 json 文件转化为 txt 文件的几种情况
3.1 样例1
数据集结构（batch 中存放图片）
json 文件内容如下：
代码转化前先创建所需 labels 文件夹与 batch_1~batch_15 文件夹
代码转化如下：
from pycocotools.coco import COCO
import numpy as np
import tqdm
import argparse
def arg_parser():
parser = argparse.ArgumentParser('code by rbj')
parser.add_argument('--annotation_path', type=str,
default='data/garbage1/annotations.json')
#生成的txt文件保存的目录
parser.add_argument('--save_base_path', type=str, default='data/garbage1/labels/')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = arg_parser()
annotation_path = args.annotation_path
save_base_path = args.save_base_path
data_source = COCO(annotation_file=annotation_path)
catIds = data_source.getCatIds()
categories = data_source.loadCats(catIds)
categories.sort(key=lambda x: x['id'])
classes = {}
coco_labels = {}
coco_labels_inverse = {}
for c in categories:
coco_labels[len(classes)] = c['id']
coco_labels_inverse[c['id']] = len(classes)
classes[c['name']] = len(classes)
img_ids = data_source.getImgIds()
for index, img_id in tqdm.tqdm(enumerate(img_ids), desc='change .json file to .txt file'):
img_info = data_source.loadImgs(img_id)[0]
file_name = img_info['file_name'].split('.')[0]
height = img_info['height']
width = img_info['width']
save_path = save_base_path + file_name + '.txt'
with open(save_path, mode='w') as fp:
annotation_id = data_source.getAnnIds(img_id)
boxes = np.zeros((0, 5))
if len(annotation_id) == 0:
fp.write('')
continue
annotations = data_source.loadAnns(annotation_id)
lines = ''
for annotation in annotations:
box = annotation['bbox']
# some annotations have basically no width / height, skip them
if box[2] < 1 or box[3] < 1:
continue
#top_x,top_y,width,height---->cen_x,cen_y,width,height
box[0] = round((box[0] + box[2] / 2) / width, 6)
box[1] = round((box[1] + box[3] / 2) / height, 6)
box[2] = round(box[2] / width, 6)
box[3] = round(box[3] / height, 6)
label = coco_labels_inverse[annotation['category_id']]
lines = lines + str(label)
for i in box:
lines += ' ' + str(i)
lines += '\n'
fp.writelines(lines)
print('finish')
3.1 样例2
数据集结构（转换单个 json 文件）
json 文件内容如下：
{
"exist": [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1],
"gt_rect": [
[298,247,44,34],
[298,245,41,34],
[299,246,43,34],
[299,247,43,33],
[297,247,43,36],
[299,247,42,35],
[300,250,40,31],
[298,249,43,33],
[298,248,43,35],
[300,248,40,35],
[300,251,42,31],
[301,249,40,35],
[300,251,39,32],
[299,251,41,32],
[301,251,41,34],
[300,251,41,32],
[301,252,39,31],
[301,250,40,35],
[299,250,43,32],
[298,251,43,32],
[298,251,44,33],
[297,250,42,34],
[298,248,40,35],
[299,248,40,35],
[297,250,42,33],
[296,250,46,34],
[298,251,42,30],
[297,250,42,34],
[296,251,39,32],
[296,250,39,33],
[297,251,40,32],
[296,249,41,34],
[295,250,44,33],
[294,249,42,34],
[295,250,41,33],
[295,248,41,36],
[294,252,41,32],
[295,249,39,34],
[291,251,44,33],
[292,251,42,31],
[293,251,41,32],
[293,251,42,32],
[293,251,41,32],
[293,252,43,31],
[293,252,41,31],
[295,252,41,31],
[295,252,40,32],
[293,250,43,34],
[294,252,42,32],
[293,252,43,32],
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]
}
创建所需 labels 文件夹与 IR_1.txt 文件，代码转化如下：
# -*- coding: utf-8 -*-
import json
import os
# 读取json文件
data = json.load(open('1.json', 'r', encoding='utf-8'))
data_gt_rect = data['gt_rect']
print(data_gt_rect[0])
# 保存成txt文件
def text_save(filename, data_gt_rect): # filename为写入CSV文件的路径，data为要写入数据列表.
file = open(filename, 'a')
for i in range(len(data_gt_rect)):
s = data_gt_rect[i]
strNums=[str(x) for x in s]
strNums = ",".join(strNums)
print(strNums)
file.write(strNums)
file.write('\n')
file.close()
print("保存文件成功")
text_save('data/Anti_UAV_test_dev/01_1667_0001-1500/labels/IR_1.txt', data_gt_rect)
3.3 样例3
数据集结构（批量转换文件夹下 json 文件）
json 文件内容（同上）如下：
代码转化如下：
# -*- coding: utf-8 -*-
import json
import os
# 保存成txt文件
def text_save(file_name, data_gt_rect): # filename为写入CSV文件的路径，data为要写入数据列表.
file = open(file_name, 'a')
for i in range(len(data_gt_rect)):
s = data_gt_rect[i]
strNums = [str(x) for x in s]
strNums = ",".join(strNums)
print(strNums)
file.write(strNums)
file.write('\n')
file.close()
print("保存文件成功")
def get_jsonfile(path, file_list):
dir_list = os.listdir(path)
for x in dir_list:
new_x = os.path.join(path, x)
if os.path.isdir(new_x):
get_jsonfile(new_x, file_list)
else:
file_tuple = os.path.splitext(new_x)
if file_tuple[1] == '.json':
file_list.append(new_x)
return file_list
# 读取json文件
files = []
path = 'data/Anti_UAV_test_dev1'
get_jsonfile(path, files)
for file_name in files:
data = json.load(open(file_name, 'r', encoding='utf-8'))
data_gt_rect = data['gt_rect']
print(data_gt_rect[0])
text_save(file_name + ".txt", data_gt_rect)
代码转化后结构：
四、随机选择一定数量文件
-
从目标文件夹随机选择一定数量图片
import os import random import shutil # 源文件夹和目标文件夹的路径 source_folder = r"/VIData/images/航线2可见光" destination_folder = "data/SG/VIData/images/train/hk2kj" # 选择的随机图片数量 num_images_to_extract = 200 # 获取源文件夹中所有图片文件的列表 image_files = [filename for filename in os.listdir(source_folder) if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))] # 随机选择一定数量的图片 selected_images = random.sample(image_files, num_images_to_extract) # 创建目标文件夹（如果不存在） if not os.path.exists(destination_folder): os.makedirs(destination_folder) # 复制选中的随机图片到目标文件夹 for image in selected_images: source_path = os.path.join(source_folder, image) destination_path = os.path.join(destination_folder, image) shutil.copy2(source_path, destination_path) print(f"{num_images_to_extract} random images extracted and saved to the destination folder.") -
遍历目标文件每个图片文件，寻找同名的文件，并将其复制到新的文件夹中
import os import shutil # 图片文件夹、txt文件夹和目标文件夹的路径 image_folder = "data/SG/VIData/images/train/hk2kj" txt_folder = "data/SG/VIData/labels2/labels/train/hx2kj" destination_folder = "data/SG/VIData/labels/train/hk2kj" # 获取图片文件夹中所有图片文件的列表 image_files = [filename for filename in os.listdir(image_folder) if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.gif'))] # 创建目标文件夹（如果不存在） if not os.path.exists(destination_folder): os.makedirs(destination_folder) # 遍历每个图片文件，寻找同名的txt文件，并将其复制到目标文件夹 for image_file in image_files: image_name, _ = os.path.splitext(image_file) # 获取图片文件名（不包含扩展名） txt_file_path = os.path.join(txt_folder, f"{image_name}.txt") if os.path.exists(txt_file_path): destination_path = os.path.join(destination_folder, f"{image_name}.txt") shutil.copy2(txt_file_path, destination_path) else: print(f"Could not find corresponding txt file for {image_file}.") print("Txt files extracted and saved to the destination folder.")
总结
以上就是Python xml2txt & 批量文件名修改 (rename) 的实现与总结 json 文件转化为 txt 文件的几种情况用于学习记录。