目录

前言

一、VOC格式和YOLO格式介绍？

1.VOC格式

2.YOLO格式

二、使用步骤

1.引入库

2.设置文件所在地址和图像信息

3.遍历与解析YOLO文件

4.创建XML文件并写入信息

5.总代码

前言

该文章介绍了如何将YOLO格式转换为XML格式。XML格式转换为YOLO格式见另一篇文章。

一、VOC格式和YOLO格式介绍？

1.VOC格式

VOC数据集采用的格式为XML格式，下面为示例：

<annotation>
   <folder>img</folder>
   <filename>pikaqiu.jpg</filename>
   <path>E:\cv_code\image_processing\test\img\pikaqiu.jpg</path>
   <source>
      <database>Unknown</database>
   </source>
   <size>
      <width>1062</width>
      <height>974</height>
      <depth>3</depth>
   </size>
   <segmented>0</segmented>
   <object>
      <name>pikaqiu</name>
      <pose>Unspecified</pose>
      <truncated>0</truncated>
      <difficult>0</difficult>
      <bndbox>
         <xmin>83</xmin>
         <ymin>74</ymin>
         <xmax>987</xmax>
         <ymax>920</ymax>
      </bndbox>
   </object>
</annotation>

其中我们要用到的信息有图片名称： <filename>pikaqiu.jpg</filename>，图片的宽度、高度、通道数信息： <size> <width>1062</width> <height>974</height> <depth>3</depth> </size>，类别名字：<name>pikaqiu</name>，box信息： <xmin>83</xmin> <ymin>74</ymin> <xmax>987</xmax> <ymax>920</ymax>。

2.YOLO格式

（class,xCenter,yCenter,w,h）,其中分别代表内别、标注框中心坐标、标注框相对宽度和长度。

二、使用步骤

1.引入库

import xml.etree.ElementTree as ET
import os

2.设置文件所在地址和图像信息

# Yolo格式文件所在目录
yolo_dir = "E:/cv_code/image_processing/aug_datasets/label/"
# XML文件保存目录
xml_dir = "E:/cv_code/image_processing/aug_datasets/Annotations/"
# 图像尺寸
img_width, img_height = 1062, 974

# 类别数字标签和名称的映射
class_map = {"pikaqiu": 0}

3.遍历与解析YOLO文件

# 遍历Yolo格式文件夹中的所有文件
for yolo_file in os.listdir(yolo_dir):
    if not yolo_file.endswith(".txt"):
        continue

    # 解析Yolo格式文件
    with open(os.path.join(yolo_dir, yolo_file), "r") as f:
        lines = f.readlines()

    # 获取图像名称
    img_file = os.path.splitext(yolo_file)[0] + ".jpg"

4.创建XML文件并写入信息

$xmin=width*(x-0.5w)$

$ymin=height*(y-0.5h)$

$xmax=weight*(x+0.5w)$

$ymax=height*(y+0.5h)$

    # 创建XML文件
    #创建根节点
    root = ET.Element("annotation")
    #创建子节点
    filename = ET.SubElement(root, "filename")
    #添加文本
    filename.text = img_file
    size = ET.SubElement(root, "size")
    width = ET.SubElement(size, "width")
    width.text = str(img_width)
    height = ET.SubElement(size, "height")
    height.text = str(img_height)
    depth = ET.SubElement(size, "depth")
    depth.text = "3"

    # 遍历所有目标
    for line in lines:
        parts = line.strip().split()
        if len(parts) < 5:
            continue

        cls_id = int(parts[0])


        if cls_id  in class_map:
            continue

        for k, v in class_map.items():
            cls_name = None
            if v == cls_id:
                cls_name = k
                break

        # cls_name = class_map[cls_id]
        x = float(parts[1])
        y = float(parts[2])
        w = float(parts[3])
        h = float(parts[4])

        # 计算边界框坐标
        xmin = int((x - w / 2) * img_width)
        ymin = int((y - h / 2) * img_height)
        xmax = int((x + w / 2) * img_width)
        ymax = int((y + h / 2) * img_height)

        # 将信息写入XML文件
        obj = ET.SubElement(root, "object")
        name = ET.SubElement(obj, "name")
        name.text = cls_name
        bndbox = ET.SubElement(obj, "bndbox")
        xmin_node = ET.SubElement(bndbox, "xmin")
        xmin_node.text = str(xmin)
        ymin_node = ET.SubElement(bndbox, "ymin")
        ymin_node.text = str(ymin)
        xmax_node = ET.SubElement(bndbox, "xmax")
        xmax_node.text = str(xmax)
        ymax_node = ET.SubElement(bndbox, "ymax")
        ymax_node.text = str(ymax)
    #将树写入文件
    tree = ET.ElementTree(root)
    tree.write(xml_dir + os.path.splitext(yolo_file)[0] + ".xml")

5.总代码

import os
import xml.etree.ElementTree as ET

# Yolo格式文件所在目录
yolo_dir = "E:/cv_code/image_processing/aug_datasets/label/"
# XML文件保存目录
xml_dir = "E:/cv_code/image_processing/aug_datasets/Annotations/"
# 图像尺寸
img_width, img_height = 1062, 974

# 类别数字标签和名称的映射
class_map = {"pikaqiu": 0}

# 遍历Yolo格式文件夹中的所有文件
for yolo_file in os.listdir(yolo_dir):
    if not yolo_file.endswith(".txt"):
        continue

    # 解析Yolo格式文件
    with open(os.path.join(yolo_dir, yolo_file), "r") as f:
        lines = f.readlines()

    # 获取图像文件名
    img_file = os.path.splitext(yolo_file)[0] + ".jpg"

    # 创建XML文件
    #创建根节点
    root = ET.Element("annotation")
    #创建子节点
    filename = ET.SubElement(root, "filename")
    #添加文本
    filename.text = img_file
    size = ET.SubElement(root, "size")
    width = ET.SubElement(size, "width")
    width.text = str(img_width)
    height = ET.SubElement(size, "height")
    height.text = str(img_height)
    depth = ET.SubElement(size, "depth")
    depth.text = "3"

    # 遍历所有目标
    for line in lines:
        parts = line.strip().split()
        if len(parts) < 5:
            continue

        cls_id = int(parts[0])

        if cls_id  in class_map:
            continue

        for k, v in class_map.items():
            cls_name = None
            if v == cls_id:
                cls_name = k
                break

        x = float(parts[1])
        y = float(parts[2])
        w = float(parts[3])
        h = float(parts[4])

        # 计算边界框坐标
        xmin = int((x - w / 2) * img_width)
        ymin = int((y - h / 2) * img_height)
        xmax = int((x + w / 2) * img_width)
        ymax = int((y + h / 2) * img_height)

        # 将信息写入XML文件
        obj = ET.SubElement(root, "object")
        name = ET.SubElement(obj, "name")
        name.text = cls_name
        bndbox = ET.SubElement(obj, "bndbox")
        xmin_node = ET.SubElement(bndbox, "xmin")
        xmin_node.text = str(xmin)
        ymin_node = ET.SubElement(bndbox, "ymin")
        ymin_node.text = str(ymin)
        xmax_node = ET.SubElement(bndbox, "xmax")
        xmax_node.text = str(xmax)
        ymax_node = ET.SubElement(bndbox, "ymax")
        ymax_node.text = str(ymax)
    #将数据写入文件
    tree = ET.ElementTree(root)
    tree.write(xml_dir + os.path.splitext(yolo_file)[0] + ".xml")

YOLO格式转换为VOC格式

前言