使用Python将YOLO的XML标注文件转换为TXT文件格式

使用Python将YOLO的XML标注文件转换为TXT文件格式，并划分数据集

import xml.etree.ElementTree as ET
import os
from os import listdir, getcwd
from os.path import join
import random
from shutil import copyfile
from PIL import Image# 只要改下面的CLASSES和PATH就可以了，其他的不用改，这个脚本会自动划分数据集，生成YOLO格式的标签文件# 分类名称  这里改成数据集的分类名称，一定要改！！！请查看数据集目录下的txt文件
CLASSES = ["car", "person"]
# 数据集目录 这里改成数据集的根目录，VOC目录下有两个文件夹Annotations和JPEGImages，一定要改！！！
PATH = r'D:\\VOC\\'
# 训练集占比80% 训练集:验证集=8:2 这里划分数据集 不用改
TRAIN_RATIO = 80def clear_hidden_files(path):dir_list = os.listdir(path)for i in dir_list:abspath = os.path.join(os.path.abspath(path), i)if os.path.isfile(abspath):if i.startswith("._"):os.remove(abspath)else:clear_hidden_files(abspath)def convert(size, box):dw = 1. / size[0]dh = 1. / size[1]x = (box[0] + box[1]) / 2.0y = (box[2] + box[3]) / 2.0w = box[1] - box[0]h = box[3] - box[2]x = x * dww = w * dwy = y * dhh = h * dhreturn (x, y, w, h)def convert_annotation(image_id):# Assuming the image format is jpgimage_path = os.path.join(image_dir, f"{image_id}.jpg")img = Image.open(image_path)w, h = img.sizein_file = open(PATH+'/Annotations/%s.xml' % image_id, encoding='utf-8')out_file = open(PATH+'/YOLOLabels/%s.txt' %image_id, 'w', encoding='utf-8')tree = ET.parse(in_file)root = tree.getroot()size = root.find('size')# w = int(size.find('width').text)# h = int(size.find('height').text)difficult = 0for obj in root.iter('object'):if obj.find('difficult'):difficult = obj.find('difficult').textcls = obj.find('name').textif cls not in CLASSES or int(difficult) == 1:continuecls_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)out_file.write(str(cls_id) + " " +" ".join([str(a) for a in bb]) + '\n')in_file.close()out_file.close()wd = os.getcwd()
wd = os.getcwd()work_sapce_dir = os.path.join(wd, PATH+"/")annotation_dir = os.path.join(work_sapce_dir, "Annotations/")
if not os.path.isdir(annotation_dir):os.mkdir(annotation_dir)
clear_hidden_files(annotation_dir)
image_dir = os.path.join(work_sapce_dir, "JPEGImages/")
if not os.path.isdir(image_dir):os.mkdir(image_dir)
clear_hidden_files(image_dir)
yolo_labels_dir = os.path.join(work_sapce_dir, "YOLOLabels/")
if not os.path.isdir(yolo_labels_dir):os.mkdir(yolo_labels_dir)
clear_hidden_files(yolo_labels_dir)yolov5_train_dir = os.path.join(work_sapce_dir, "train/")
if not os.path.isdir(yolov5_train_dir):os.mkdir(yolov5_train_dir)
clear_hidden_files(yolov5_train_dir)
yolov5_images_train_dir = os.path.join(yolov5_train_dir, "images/")
if not os.path.isdir(yolov5_images_train_dir):os.mkdir(yolov5_images_train_dir)
clear_hidden_files(yolov5_images_train_dir)
yolov5_labels_train_dir = os.path.join(yolov5_train_dir, "labels/")
if not os.path.isdir(yolov5_labels_train_dir):os.mkdir(yolov5_labels_train_dir)
clear_hidden_files(yolov5_labels_train_dir)yolov5_test_dir = os.path.join(work_sapce_dir, "val/")
if not os.path.isdir(yolov5_test_dir):os.mkdir(yolov5_test_dir)
clear_hidden_files(yolov5_test_dir)
yolov5_images_test_dir = os.path.join(yolov5_test_dir, "images/")
if not os.path.isdir(yolov5_images_test_dir):os.mkdir(yolov5_images_test_dir)
clear_hidden_files(yolov5_images_test_dir)
yolov5_labels_test_dir = os.path.join(yolov5_test_dir, "labels/")
if not os.path.isdir(yolov5_labels_test_dir):os.mkdir(yolov5_labels_test_dir)
clear_hidden_files(yolov5_labels_test_dir)train_file = open(os.path.join(wd, "yolov5_train.txt"), 'w', encoding='utf-8')
test_file = open(os.path.join(wd, "yolov5_valid.txt"), 'w', encoding='utf-8')
train_file.close()
test_file.close()
train_file = open(os.path.join(wd, "yolov5_train.txt"), 'a', encoding='utf-8')
test_file = open(os.path.join(wd, "yolov5_valid.txt"), 'a', encoding='utf-8')
list_imgs = os.listdir(image_dir)  # list image files
prob = random.randint(1, 100)
print("数据集: %d个" % len(list_imgs))
for i in range(0, len(list_imgs)):path = os.path.join(image_dir, list_imgs[i])if os.path.isfile(path):image_path = image_dir + list_imgs[i]voc_path = list_imgs[i](nameWithoutExtention, extention) = os.path.splitext(os.path.basename(image_path))(voc_nameWithoutExtention, voc_extention) = os.path.splitext(os.path.basename(voc_path))annotation_name = nameWithoutExtention + '.xml'annotation_path = os.path.join(annotation_dir, annotation_name)label_name = nameWithoutExtention + '.txt'label_path = os.path.join(yolo_labels_dir, label_name)prob = random.randint(1, 100)print("Probability: %d" % prob, i, list_imgs[i])if (prob < TRAIN_RATIO):# train datasetif os.path.exists(annotation_path):train_file.write(image_path + '\n')convert_annotation(nameWithoutExtention)  # convert labelcopyfile(image_path, yolov5_images_train_dir + voc_path)copyfile(label_path, yolov5_labels_train_dir + label_name)else:# test datasetif os.path.exists(annotation_path):test_file.write(image_path + '\n')convert_annotation(nameWithoutExtention)  # convert labelcopyfile(image_path, yolov5_images_test_dir + voc_path)copyfile(label_path, yolov5_labels_test_dir + label_name)
train_file.close()
test_file.close()

以上代码需要修改：CLASSES变量为自己的类别，修改PATH变量为自己的数据集目录。