在用darknet训练voc数据集时，需要将xml格式的标签转换为txt格式的标签。
同时，用自定义数据集在darknet中进行训练时，如遇到xml格式转txt格式的问题，也可用本文方法。
废话不多，开始介绍。
新建文件夹VOCdevkit,文件结构为：
├── gen_files.py└── VOCdevkit└── VOC├── Annotations├── ImageSets├── JPEGImages└── labels
按照以上结构来建文件夹，并保持名字与上一致。
Annotations用来存放xml格式的标注文件；
JPEGImages存放图片数据集；
labels存放转换后的txt标注文件，目前是空文件夹。
将gen_files.py放至与VOCdevkit文件夹同级目录，代码内容如下：
import xml.etree.ElementTree as ETimport pickleimport osfrom os import listdir, getcwdfrom os.path import joinimport randomclasses=["class1","class2"]def 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):in_file = open('VOCdevkit/VOC/Annotations/%s.xml' %image_id)out_file = open('VOCdevkit/VOC/labels/%s.txt' %image_id, 'w')tree=ET.parse(in_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').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, "VOCdevkit/")if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)work_sapce_dir = os.path.join(work_sapce_dir, "VOC/")if not os.path.isdir(work_sapce_dir):os.mkdir(work_sapce_dir)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)VOC_file_dir = os.path.join(work_sapce_dir, "ImageSets/")if not os.path.isdir(VOC_file_dir):os.mkdir(VOC_file_dir)VOC_file_dir = os.path.join(VOC_file_dir, "Main/")if not os.path.isdir(VOC_file_dir):os.mkdir(VOC_file_dir)train_file = open(os.path.join(wd, "_train.txt"), 'w')test_file = open(os.path.join(wd, "_test.txt"), 'w')train_file.close()test_file.close()VOC_train_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/train.txt"), 'w')VOC_test_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/test.txt"), 'w')VOC_train_file.close()VOC_test_file.close()if not os.path.exists('VOCdevkit/VOC/labels'):os.makedirs('VOCdevkit/VOC/labels')train_file = open(os.path.join(wd, "_train.txt"), 'a')test_file = open(os.path.join(wd, "_test.txt"), 'a')VOC_train_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/train.txt"), 'a')VOC_test_file = open(os.path.join(work_sapce_dir, "ImageSets/Main/test.txt"), 'a')list = os.listdir(image_dir) # list image filesprobo = random.randint(1, 100)print("Probobility: %d" % probo)for i in range(0,len(list)):path = os.path.join(image_dir,list[i])if os.path.isfile(path):image_path = image_dir + list[i]voc_path = list[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)probo = random.randint(1, 100)print("Probobility: %d" % probo)if(probo < 75):if os.path.exists(annotation_path):train_file.write(image_path + '\n')VOC_train_file.write(voc_nameWithoutExtention + '\n')convert_annotation(nameWithoutExtention)else:if os.path.exists(annotation_path):test_file.write(image_path + '\n')VOC_test_file.write(voc_nameWithoutExtention + '\n')convert_annotation(nameWithoutExtention)train_file.close()test_file.close()VOC_train_file.close()VOC_test_file.close()
将classes=["class1","class2"]改成你要转换的数据集对应的类别即可（注意类别顺序），其他的默认即可。运行gen_files.py，之后会在labels文件夹生成转换成功的txt文件，同时会在根目录下生成train.txt test.txt。
