voc 的xml数据转换成txt格式

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  • Post category:其他


代码

import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join

classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant",
         "sheep", "sofa", "train", "tvmonitor"] # 换上你标签

# 改变坐标格式
def convert(size, box):
    dw = 1. / (size[0])
    dh = 1. / (size[1])
    x = (box[0] + box[1]) / 2.0
    y = (box[2] + box[3]) / 2.0
    w = box[1] - box[0]
    h = box[3] - box[2]
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)


xml_path = "D:/2022.11/MetaPruning-master/VOCdevkit/VOC2007/Annotations/"
txt_path = "D:/2022.11/MetaPruning-master/VOCdevkit/VOC2007/AnnotationsTXT/"


def convert_annotation(xml_path, txt_path):
    if not os.path.exists(txt_path):
        os.mkdir(txt_path)
    for image_id in os.listdir(xml_path):
        #         print(xml_path+image_id)
        xml_file = open(xml_path + image_id)
        txt_file = open(txt_path + image_id[:-3] + "txt", "a")
        tree = ET.parse(xml_file)
        root = tree.getroot()
        size = root.find('size')
        w = int(size.find('width').text)
        h = int(size.find('height').text)
        for obj in root.iter('object'):
            difficult = obj.find('difficult').text
            cls = obj.find('name').text
            cls_id = classes.index(cls)
            xmlbox = obj.find('bndbox')
            b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
                 float(xmlbox.find('ymax').text))
            bb = convert((w, h), b)
            txt_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


convert_annotation(xml_path, txt_path)

输出

//2007_000346.txt

15 0.274000 0.208000 0.994000 1.000000

15 0.178000 0.538667 0.258000 0.658667

15 0.144000 0.557333 0.222000 0.690667

5 0.248000 0.285333 0.460000 0.914667

核心代码参考

使用python将voc类型标注xml文件对图片进行目标还原,以及批量裁剪特定类_三寸光阴___的博客-CSDN博客_剪切voc标注图像