txt转换xml

注：文章中的代码是针对FLIR数据集转换为txt后，进一步转换xml使用的，当然可以根据自己的实际更改使用

文件位置（该文件夹与代码py文件放置于同一文件夹下）

• coco
  • images # 存放图像
  • labels #存放txt标签

代码如下：

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Aug 17 22:10:01 2018

@author: Caroline Pacheco do E. Silva
"""

import os
import cv2
from xml.dom.minidom import parseString
from lxml.etree import Element, SubElement, tostring
import numpy as np
from os.path import join

## coco classes
# YOLO_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
#                 'train', 'truck', 'boat', 'traffic light', 'fire', 'hydrant',
#                 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
#                 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra',
#                 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
#                 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
#                 'kite', 'baseball bat', 'baseball glove', 'skateboard',
#                 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
#                 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
#                 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
#                 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
#                 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
#                 'keyboard', 'cell phone', 'microwave oven', 'toaster', 'sink',
#                 'refrigerator', 'book', 'clock', 'vase', 'scissors',
#                 'teddy bear', 'hair drier', 'toothbrush')
YOLO_CLASSES = ('People', 'Bicycle', 'Cars')

## converts the normalized positions  into integer positions
def unconvert(class_id, width, height, x, y, w, h):

    xmax = int((x*width) + (w * width)/2.0)
    xmin = int((x*width) - (w * width)/2.0)
    ymax = int((y*height) + (h * height)/2.0)
    ymin = int((y*height) - (h * height)/2.0)
    class_id = int(class_id)
    return (class_id, xmin, xmax, ymin, ymax)


## path root folder
ROOT = 'coco'


## converts coco into xml 
def xml_transform(root, classes):  
    class_path  = join(root, 'labels')
    ids = list()
    l=os.listdir(class_path)
    
    check = '.DS_Store' in l
    if check == True:
        l.remove('.DS_Store')
        
    ids=[x.split('.')[0] for x in l]   

    annopath = join(root, 'labels', '%s.txt')
    imgpath = join(root, 'images', '%s.jpeg')
    
    os.makedirs(join(root, 'outputs'), exist_ok=True)
    outpath = join(root, 'outputs', '%s.xml')

    for i in range(len(ids)):
        img_id = ids[i]
        img= cv2.imread(imgpath % img_id)
        height, width, channels = img.shape # pega tamanhos e canais das images

        node_root = Element('annotation')
        node_folder = SubElement(node_root, 'folder')
        node_folder.text = 'VOC2007'
        img_name = img_id + '.jpeg'
    
        node_filename = SubElement(node_root, 'filename')
        node_filename.text = img_name
        
        node_source= SubElement(node_root, 'source')
        node_database = SubElement(node_source, 'database')
        node_database.text = 'Coco database'
        
        node_size = SubElement(node_root, 'size')
        node_width = SubElement(node_size, 'width')
        node_width.text = str(width)
    
        node_height = SubElement(node_size, 'height')
        node_height.text = str(height)

        node_depth = SubElement(node_size, 'depth')
        node_depth.text = str(channels)

        node_segmented = SubElement(node_root, 'segmented')
        node_segmented.text = '0'

        target = (annopath % img_id)
        if os.path.exists(target):
            label_norm= np.loadtxt(target).reshape(-1, 5)

            for i in range(len(label_norm)):
                labels_conv = label_norm[i]
                new_label = unconvert(labels_conv[0], width, height, labels_conv[1], labels_conv[2], labels_conv[3], labels_conv[4])
                node_object = SubElement(node_root, 'object')
                node_name = SubElement(node_object, 'name')
                node_name.text = classes[new_label[0]]
                
                node_pose = SubElement(node_object, 'pose')
                node_pose.text = 'Unspecified'
                
                
                node_truncated = SubElement(node_object, 'truncated')
                node_truncated.text = '0'
                node_difficult = SubElement(node_object, 'difficult')
                node_difficult.text = '0'
                node_bndbox = SubElement(node_object, 'bndbox')
                node_xmin = SubElement(node_bndbox, 'xmin')
                node_xmin.text = str(new_label[1])
                node_ymin = SubElement(node_bndbox, 'ymin')
                node_ymin.text = str(new_label[3])
                node_xmax = SubElement(node_bndbox, 'xmax')
                node_xmax.text =  str(new_label[2])
                node_ymax = SubElement(node_bndbox, 'ymax')
                node_ymax.text = str(new_label[4])
                xml = tostring(node_root, pretty_print=True)  
                dom = parseString(xml)
        print(xml)  
        f =  open(outpath % img_id, "wb")
        #f = open(os.path.join(outpath, img_id), "w")
        #os.remove(target)
        f.write(xml)
        f.close()     
       

xml_transform(ROOT, YOLO_CLASSES)

如果是更改自己的数据集，应该YOLO_CLASSES变成自己的类，图像的格式也应该进行修改，代码中用的是jpeg格式