Python修改XML文件节点值的方法详解

Theo

发布时间：2026-10-13 | 6人浏览过

原创

本文介绍如何使用Python的ElementTree库修改XML文件中的节点值，重点演示在目标检测数据预处理中，根据图像缩放比例调整边界框坐标的方法。通过遍历XML节点并更新text属性，实现图像尺寸与标注信息的同步变更，适用于YOLO等格式的数据增强场景。

首先展示一个典型的XML文件结构示例，该文件通常用于存储图像标注信息，包含文件名、路径、图像尺寸以及多个目标对象的边界框坐标等关键数据。

<?xml version='1.0' encoding='utf-8'?>
<annotation>
 <folder>JPEGImages</folder>
 <filename>train_2018-05-08_1000.jpg</filename>
 <path>D:\all_data\2018-05-08\JPEGImages\train_2018-05-08_1000.jpg</path>
 <source>
 <database>Unknown</database>
 </source>
 <size>
 <width>4032</width>
 <height>3024</height>
 <depth>3</depth>
 </size>
 <segmented>0</segmented>
 <object>
 <name>yl-ylhzdhmbbz-gz-hm-280g</name>
 <pose>Unspecified</pose>
 <truncated>0</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>1863</xmin>
  <ymin>355</ymin>
  <xmax>2512</xmax>
  <ymax>902</ymax>
 </bndbox>
 </object>
 <object>
 <name>hy-hybfbgz-hz-xcw-200ml</name>
 <pose>Unspecified</pose>
 <truncated>0</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>1076</xmin>
  <ymin>1602</ymin>
  <xmax>1648</xmax>
  <ymax>2105</ymax>
 </bndbox>
 </object>
 <object>
 <name>ys-zzyspyz-gz-yw-245ml</name>
 <pose>Unspecified</pose>
 <truncated>1</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>2017</xmin>
  <ymin>2475</ymin>
  <xmax>2681</xmax>
  <ymax>3024</ymax>
 </bndbox>
 </object>
 <object>
 <name>mn-zgl-hz-cmw-250ml</name>
 <pose>Unspecified</pose>
 <truncated>0</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>1849</xmin>
  <ymin>1207</ymin>
  <xmax>2242</xmax>
  <ymax>2047</ymax>
 </bndbox>
 </object>
 <object>
 <name>qc-qckf-pz-shnt-268ml</name>
 <pose>Unspecified</pose>
 <truncated>0</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>480</xmin>
  <ymin>1213</ymin>
  <xmax>1308</xmax>
  <ymax>1544</ymax>
 </bndbox>
 </object>
 <object>
 <name>wt-wtcyl-gz-nm-310ml</name>
 <pose>Unspecified</pose>
 <truncated>0</truncated>
 <difficult>0</difficult>
 <bndbox>
  <xmin>867</xmin>
  <ymin>488</ymin>
  <xmax>1527</xmax>
  <ymax>938</ymax>
 </bndbox>
 </object>

</annotation>

接下来，我们的目标是实现图像尺寸和目标边界框的同步修改。这通常发生在数据增强过程中，例如对原始图像进行缩放后，需要相应地调整标注文件中记录的坐标值，以保持标注与图像的一致性。

__author__ = 'Sam'
 
import cv2
import xml.etree.ElementTree as ET
import os
import sys
import lxml
import shutil
 
#user input files path
path="E:/test_folder"
image_path = path + "/Annotations/" #image path with .jpg ending
label_path = path + "/JPEGImages/" #label path with .xml ending
min_size=800
 
def search_jpg_xml(image_dir,label_dir):
  #find out all of sepecified file
  image_ext='.jpg'
  img=[fn for fn in os.listdir(image_dir) if fn.endswith(image_ext)]
  label_ext='.xml'
  label=[fn for fn in os.listdir(label_dir) if fn.endswith(label_ext)]
  return img, label
 
def copyfile():
  if "Annotations_temp" in os.listdir(path):
    shutil.rmtree(path+"/Annotations_temp")
  if "JPEGImages_temp" in os.listdir(path):
    shutil.rmtree(path+"/JPEGImages_temp")
  save_annotation_path=path+"/Annotations_temp/"
  save_jpg_path=path+"/JPEGImages_temp/"
  shutil.copytree(path + "/Annotations",save_annotation_path)
  shutil.copytree(path + "/JPEGImages", save_jpg_path)
  return save_jpg_path ,save_annotation_path
 
def write_xml_jpg(jpg_path,annotation_path):
  img,label=search_jpg_xml(jpg_path,annotation_path)
  sorted(img)
  sorted(label)
  print(img)
  print(label)
  if "Annotations_1" not in os.listdir(path):
    os.mkdir(path+"/Annotations_1")
  if "JPEGImages_1" not in os.listdir(path):
    os.mkdir(path+"/JPEGImages_1")
  new_image_path=path+"/JPEGImages_1/"
  new_annotation_path=path+"/Annotations_1/"
  for index,file in enumerate(label):
    cur_img = cv2.imread(jpg_path+img[index])
    width=cur_img.shape[1]
    height=cur_img.shape[0]
    if width<height:
      new_width=min_size
      new_height=int(min_size*height/width)
      w_ratio=new_width/width
      h_ratio=new_height/height
    elif width>height:
      new_width=int(min_size*width/height)
      new_height=min_size
      w_ratio=new_width/width
      h_ratio=new_height/height
    elif width==height:
      new_width=min_size
      new_height=min_size
      w_ratio=new_width/width
      h_ratio=new_height/height
    cur_img = cv2.resize(cur_img, (new_width, new_height))
    cv2.imwrite(new_image_path+img[index],cur_img)
    cur_xml = ET.parse(annotation_path+file)
    root = cur_xml.getroot()
    for node in root:
      if node.tag=='size':
        node[0].text=str(new_width)
        node[1].text=str(new_height)
      elif node.tag=='object':
         xmin=int(node[4][0].text)#bbox position
         ymin=int(node[4][1].text)
         xmax=int(node[4][2].text)
         ymax=int(node[4][3].text)
         node[4][0].text=str(int(xmin*w_ratio))
         node[4][1].text=str(int(ymin*h_ratio))
         node[4][2].text=str(int(xmax*w_ratio))
         node[4][3].text=str(int(ymax*h_ratio))
    cur_xml.write(new_annotation_path+file)
  shutil.rmtree(path+"/JPEGImages_temp")
  shutil.rmtree(path+"/Annotations_temp")
 
 
if __name__ == "__main__":
  jpg_path,annotation_path=copyfile()
  write_xml_jpg(jpg_path,annotation_path)

实现这一功能的核心代码逻辑如下：通过解析XML文档，定位到包含坐标信息的节点，计算缩放比例，并将新的坐标值写入对应的节点中。

关键语句为：node[4][3].text=str(int(ymax*h_ratio))。这里需要特别注意，XML节点中的text属性是字符串类型，因此在赋值前必须将计算得到的浮点数或整数转换为字符串格式，否则会导致写入错误或数据格式异常。

以上关于使用Python修改XML文件节点值的详细方法就介绍到这里。希望这个示例能帮助读者理解如何利用ElementTree库处理XML数据，特别是在目标检测数据预处理中的应用。如果有任何疑问或建议，欢迎交流讨论。