将数据集标注的json格式文件转化成xml格式文件

之前训练的Faster R-CNN的标签用的是json格式的文件，现在训练SSD模型需要xml格式的文件。

1.新建存放jpg原图和json标签的两个文件夹

（PS：所有图片都必须是jpg格式，如果是png、jpeg等其他格式会报错）
如下图所示
2.在labelmedataset文件夹的同级目录中新建py工程文件
运行如下程序，即可

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.存放的json标签路径
labelme_path = "labelmedataset/labels/"

# 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('labelmedataset/images/' + 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("labelmedataset/images/" + "*.jpg")
print("copy image files to VOC007/JPEGImages/")
for image in image_files:
    shutil.copy(image, saved_path + "JPEGImages/")

# 6.拆分训练集、测试集、验证集
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()

3.效果

Annatations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
ImageSets文件夹：Main存放的是分割好了的测试集、训练集等
JPEGImages文件夹：存放的原来的图像