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在用CenterNet模型训练自己的数据集时，发现需要coco数据集格式，即需要labelme标注得到的json文件，但由于我是使用labelimg进行标注，所以只有xml文件。
于是开始寻找脚本进行转换，但发现网上的都没有办法读取imageData信息，得到json文件如下。
{
"version": "3.16.2",
"flags": {},
"shapes": [
{
"label": "test class",
"points": [
[
631.0,
275.0
],
[
714.0,
509.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
}
],
"imagePath": "000000000000.jpg",
"imageData": null,
"imageHeight": 800,
"imageWidth": 800
}
imageData的值为null，于是我开始找labelme读取图片信息时是怎么读取imageData的。最后找到了这篇。
https://blog.csdn.net/nodototao/article/details/123800645?spm=1001.2101.3001.6650.2&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-2.pc_relevant_default&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-2.pc_relevant_default&utm_relevant_index=5 https://blog.csdn.net/nodototao/article/details/123800645?spm=1001.2101.3001.6650.2&utm_medium=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-2.pc_relevant_default&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2%7Edefault%7ECTRLIST%7Edefault-2.pc_relevant_default&utm_relevant_index=5然后我将代码改了改，变成下面的代码。
# --- utf-8 ---
# --- function: 将Labeling标注的格式转化为Labelme标注格式，并读取imageData ---
import os
import glob
import shutil
import xml.etree.ElementTree as ET
import json
from base64 import b64encode
from json import dumps
def get(root, name):
return root.findall(name)
# 检查读取xml文件是否出错
def get_and_check(root, name, length):
vars = root.findall(name)
if len(vars) == 0:
raise NotImplementedError('Can not fing %s in %s.' % (name, root.tag))
if length > 0 and len(vars) != length:
raise NotImplementedError('The size of %s is supposed to be %d, but is %d.' % (name, length, len(vars)))
if length == 1:
vars = vars[0]
return vars
def convert(xml_file, json_file, save_dir, name, data):
# 定义通过Labelme标注后生成的json文件
json_dict = {"version": "3.16.2", "flags": {}, "shapes": [], "imagePath": "", "imageData": None,
"imageHeight": 0, "imageWidth": 0}
# img_name = xml_file.split('.')[0]
img_path = name + '.jpg'
json_dict["imagePath"] = img_path
tree = ET.parse(xml_file) # 读取xml文件
root = tree.getroot()
size = get_and_check(root, 'size', 1) # 读取xml中size字段中的内容
# 读取二进制图片，获得原始字节码
with open(data, 'rb') as jpg_file:
byte_content = jpg_file.read()
# 把原始字节码编码成base64字节码
base64_bytes = b64encode(byte_content)
# 把base64字节码解码成utf-8格式的字符串
base64_string = base64_bytes.decode('utf-8')
# 用字典的形式保存数据
json_dict["imageData"] = base64_string
# 获取图片的长宽信息
width = int(get_and_check(size, 'width', 1).text)
height = int(get_and_check(size, 'height', 1).text)
json_dict["imageHeight"] = height
json_dict["imageWidth"] = width
# 当标注中有多个目标时全部读取出来
for obj in get(root, 'object'):
# 定义图片的标注信息
img_mark_inf = {"label": "", "points": [], "group_id": None, "shape_type": "rectangle", "flags": {}}
category = get_and_check(obj, 'name', 1).text # 读取当前目标的类别
img_mark_inf["label"] = category
bndbox = get_and_check(obj, 'bndbox', 1) # 获取标注宽信息
xmin = float(get_and_check(bndbox, 'xmin', 1).text)
ymin = float(get_and_check(bndbox, 'ymin', 1).text)
xmax = float(get_and_check(bndbox, 'xmax', 1).text)
ymax = float(get_and_check(bndbox, 'ymax', 1).text)
img_mark_inf["points"].append([xmin, ymin])
img_mark_inf["points"].append([xmax, ymax])
# print(img_mark_inf["points"])
json_dict["shapes"].append(img_mark_inf)
# print("{}".format(json_dict))
save = save_dir + json_file # json文件的路径地址
json_fp = open(save, 'w') #
json_str = json.dumps(json_dict, indent=4) # 缩进，不需要的可以将indent=4去掉
json_fp.write(json_str) # 保存
json_fp.close()
# print("{}, {}".format(width, height))
def do_transformation(xml_dir, save_path):
cnt = 0
for fname in os.listdir(xml_dir):
name = fname.split(".")[0] # 获取图片名字
path = os.path.join(xml_dir, fname) # 文件路径
save_json_name = name + '.json'
data = img + name + '.jpg' # xml文件对应的图片路径
convert(path, save_json_name, save_path, name, data)
cnt += 1
if __name__ == '__main__':
img = "D:/test/VOCdevkit/VOC2007/JPEGImages/" # xml对应图片文件夹
xml_path = "D:/test/VOCdevkit/VOC2007/Annotations" # xml文件夹
save_json_path = "D:/test/12345/" # 存放json文件夹
if not os.path.exists(save_json_path):
os.makedirs(save_json_path)
do_transformation(xml_path, save_json_path)
# xml = "2007_000039.xml"
# xjson = "2007_000039.json"
# convert(xml, xjson)
最后就能将数据集在labelimg标注得到的xml文件转为labelme标注的json文件，且还读取到了imageData，大功告成。
测试图片
000000000000.jpg
D:\test\000000000000.jpg
Unknown
800
800
3
0
test class
Unspecified
0
0
631
275
714
509
{
"version": "3.16.2",
"flags": {},
"shapes": [
{
"label": "test class",
"points": [
[
631.0,
275.0
],
[
714.0,
509.0
]
],
"group_id": null,
"shape_type": "rectangle",
"flags": {}
}
],
"imagePath": "000000000000.jpg",
"imageData": "/9j/4AAQSkZJRgABAQAAAQABAAD/......",
"imageHeight": 800,
"imageWidth": 800
}
以上就是转换结果，imageData太长了就不在这显示了。
代码参考
https://blog.csdn.net/Xiao_ZhiJ/article/details/122918983https://blog.csdn.net/Xiao_ZhiJ/article/details/122918983https://blog.csdn.net/nodototao/article/details/123800645?spm=1001.2101.3001.6650.2&utm_medium=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~default-2.pc_relevant_default&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~default-2.pc_relevant_default&utm_relevant_index=5https://blog.csdn.net/nodototao/article/details/123800645?spm=1001.2101.3001.6650.2&utm_medium=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~default-2.pc_relevant_default&depth_1-utm_source=distribute.pc_relevant.none-task-blog-2~default~CTRLIST~default-2.pc_relevant_default&utm_relevant_index=5
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