https://github.com/ultralytics/JSON2YOLO
https://blog.csdn.net/qq_51831335/article/details/127237772
目标检测数据集标签转换COCO2VOC、YOLO2VOC、JSON2YOLO

<annotation><folder>VOC2007</folder><filename>000001.jpg</filename><source><database>The VOC2007 Database</database><annotation>PASCAL VOC2007</annotation><image>flickr</image><flickrid>341012865</flickrid></source><owner><flickrid>Fried Camels</flickrid><name>Jinky the Fruit Bat</name></owner><size><width>353</width><height>500</height><depth>3</depth></size><segmented>0</segmented><object><name>dog</name><pose>Left</pose><truncated>1</truncated><difficult>0</difficult><bndbox><xmin>48</xmin><ymin>240</ymin><xmax>195</xmax><ymax>371</ymax></bndbox></object><object><name>person</name><pose>Left</pose><truncated>1</truncated><difficult>0</difficult><bndbox><xmin>8</xmin><ymin>12</ymin><xmax>352</xmax><ymax>498</ymax></bndbox></object>
</annotation>

#定义一个voc到yolo数据格式的转换代码
#voc的数据标定的时候给定的是：左上角，右下角的坐标（xmin/ymin/xmax/ymax）
#yolo数据标定的时候给定的是：中心点坐标，宽度，高度（x/y/w/h）,并且是和原始图像width和height的百分比import os
from xml.etree import ElementTree as ET
import tqdmif __name__ == '__main__':label_2_id = {'plate':0}img_dir= “”voc_label_dir = ""yolo_label_dir = ""#读取所有voc_xml文件名称列表if not os.path.exists(yolo_label_dir):os.makedirs(yolo_label_dir):os.makedirs(yolo_label_dir)#遍历处理每个xml文件for voc_label_name in tqdm(voc_label_names):#1.解析xml文件tree = ET.parse(os.path.join(voc_label_dir,voc_label_name))#2.获取根节点的信息root = tree.getroot()#3.获取文件名称filename = root.find('filename').text#4.判断图像文件是是否存在img_file = os.path.join(img_dir,filename)if not os.path.exists(img_file)continue#5.将标注的box信息转换成yolo格式，然后保存到txt文件中#6.提取图像的大小size_obj = root.find('size')width = float(size_obj.find('width').text)height = float(size_obj.find('height').text)yolo_label_file = os.path.join(yolo_label_dir,f"{os.path.splitext(filename)[0]}.txt")with open(yolo_label_file,'w',encoding = 'UTF-8') as writer:for obj in root.findall('object'):#提取标签名称label = obj.find('name').text#提取左上角，右下角坐标bbox = obj.find('bndbox')xmin = float(bbox.find('xmin').text)xmax = float(bbox.find('xmin').text)ymin = float(bbox.find('xmin').text)ymax = float(bbox.find('xmin').text)#转换成中心点坐标，宽度，高度w = xmax-xminh = ymax -yminx = xmin + w/2.0y = ymin + h/2.0#需要相对宽度和高度w = w/widthh = h/heightx = x/widthy = y/height#输出writer.writelines(f"{label_2_id{label} {x} {y} {w} {h}\n}")#img_name = voc_label_name[:-3]#print(voc_label_names)