目标检测标注文件xml，json，txt相互转换

前言

在目标检测中我们可能会碰到很多种不同格式的标注文件，比如xml，txt，json等格式文件。这个时候就会涉及到不同格式之间的数据进行转换。笔者自己最近也在搞这些相关的数据处理，整理总结了一下。

json转xml

用labelme可以直接标注成json格式文件，是coco数据集的标注格式。针对每个json格式文件，读取标注信息，然后根据每张图片生成对应的xml格式文件。

import os
import numpy as np
import codecs
import json
from glob import glob
import cv2
import shutil
from sklearn.model_selection import train_test_split
#1.标签路径
labelme_path = "./labelme/"              #使用labelme打的标签（包含每一张照片和对应json格式的标签）
saved_path = "./VOCdevkit/VOC2007/"                #保存路径

#2.voc格式的文件夹，如果没有，就创建一个
if not os.path.exists(saved_path + "Annotations"):
    os.makedirs(saved_path + "Annotations")
if not os.path.exists(saved_path + "JPEGImages/"):
    os.makedirs(saved_path + "JPEGImages/")
if not os.path.exists(saved_path + "ImageSets/Main/"):
    os.makedirs(saved_path + "ImageSets/Main/")
    
#3.获取json文件
files = glob(labelme_path + "*.json")
files = [i.split("/")[-1].split(".json")[0] for i in files]  #获取每一个json文件名

#4.读取每一张照片和对应标签，生成xml
for json_file_ in files:
    json_filename = labelme_path + json_file_ + ".json"
    json_file = json.load(open(json_filename,"r",encoding="utf-8"))
    height, width, channels = cv2.imread(labelme_path + json_file_ +".jpg").shape
    with codecs.open(saved_path + "Annotations/"+json_file_ + ".xml","w","utf-8") as xml:
        xml.write('\n')
        xml.write('\t' + 'UAV_data' + '\n')
        xml.write('\t' + json_file_ + ".jpg" + '\n')
        xml.write('\t\n')
        xml.write('\t\tThe UAV autolanding\n')
        xml.write('\t\tUAV AutoLanding\n')
        xml.write('\t\tflickr\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\tNULL\n')
        xml.write('\t\tChaojieZhu\n')
        xml.write('\t\n')
        xml.write('\t\n')
        xml.write('\t\t'+ str(width) + '\n')
        xml.write('\t\t'+ str(height) + '\n')
        xml.write('\t\t' + str(channels) + '\n')
        xml.write('\t\n')
        xml.write('\t\t0\n')
        for multi in json_file["shapes"]:
            points = np.array(multi["points"])
            xmin = min(points[:,0])
            xmax = max(points[:,0])
            ymin = min(points[:,1])
            ymax = max(points[:,1])
            label = multi["label"]
            if xmax <= xmin:
                pass
            elif ymax <= ymin:
                pass
            else:
                xml.write('\t\n')
                print(json_filename,xmin,ymin,xmax,ymax,label)
        xml.write('')
        
#5.复制图片到 VOC2007/JPEGImages/下
image_files = glob(labelme_path + "*.jpg")
print("copy image files to VOC007/JPEGImages/")
for image in image_files:
    shutil.copy(image,saved_path +"JPEGImages/")
    
#6.划分train，test，val格式数据集
txtsavepath = saved_path + "ImageSets/Main/"
ftrainval = open(txtsavepath+'/trainval.txt', 'w')
ftest = open(txtsavepath+'/test.txt', 'w')
ftrain = open(txtsavepath+'/train.txt', 'w')
fval = open(txtsavepath+'/val.txt', 'w')
total_files = glob("./VOC2007/Annotations/*.xml")
total_files = [i.split("/")[-1].split(".xml")[0] for i in total_files]
#test_filepath = ""
for file in total_files:
    ftrainval.write(file + "\n")
#test
#for file in os.listdir(test_filepath):
#    ftest.write(file.split(".jpg")[0] + "\n")

#split，根据test_size这个参数来确定test的数量
train_files,val_files = train_test_split(total_files,test_size=0.15,random_state=42)
#train
for file in train_files:
    ftrain.write(file + "\n")
#val
for file in val_files:
    fval.write(file + "\n")

ftrainval.close()
ftrain.close()
fval.close()
#ftest.close()

xml转txt

用labelimg可以直接标注成xml格式，xml是voc格式的数据，也是我们一般的标注格式。txt是yolo和大部分数据集的标注格式，所以xml文件转成txt格式就很关键。

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

sets=['train', 'test']

classes = ["safety_helmet", "head"]#这里输入你的数据集类别


def convert(size, box):
    #读取xml文件中的数据，xywh
    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)

def convert_annotation(image_id):
    in_file = open('police_labels/%s.xml'%(image_id))#这里是读取xml的文件夹
    out_file = open('labels/%s.txt'%(image_id), 'w')#存入txt文件的文件夹
    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').text
        cls = obj.find('name').text
        if cls not in classes or int(difficult) == 1:
            continue
        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)
        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')

wd = getcwd()

for image_set in sets:
    # labels文件夹专门存储转换好的txt文件，如果没有则反注释下面的代码
    # if not os.path.exists('labels/'):
    #     os.makedirs('labels/')
    image_ids = open('ImageSets/Main/%s.txt'%(image_set)).read().strip().split()#读取train.txt或者test.txt从而找到每个xml文件的文件名，这里的train.txt中仅包含文件名，不包好路径。
    list_file = open('%s.txt'%(image_set), 'w')
    for image_id in image_ids:
        list_file.write('/root/object-detection/yolov5-master/data/police_obj/images/%s.jpg\n'%(image_id))#从写train.txt或者test.txt文件，把图片文件的绝对路径写入，方便读取图片
        convert_annotation(image_id)
    list_file.close()

txt转xml格式

有的时候也需要把txt格式转换成coco格式，但是由于txt格式文件是不带标签名的，所以我们先转成xml，再由xml转成coco。

import cv2
import os

labels = ['A', 'B', 'C']  # 数据集类别名
xml_head = '''
    VOC2007
    
    {}.
    
        The VOC2007 Database
        PASCAL VOC2007
        flickr
        325991873
    
    
        null
        null
        
    
        {}
        {}
        {}
    
    0
    '''
xml_obj = '''
    
    '''

xml_end = '''
'''
cnt = 0
with open('train.txt', 'r') as train_list: # 训练数据train.txt或test.txt，其中包含图片路径
    for lst in train_list.readlines():
        lst = lst.strip()
        jpg = lst  # image path
        txt1 = lst.replace('images', 'labels')
        txt = lst.replace('.jpg', '.txt')  # yolo label txt path
        xml_path1 = jpg.replace('images', 'xmllabels')
        xml_path = jpg.replace('.jpg', '.xml') 
        # xml保存路径，此时images，labels，xmllabels必须在一个文件目录下，images存放图片，labels存放txt文件。

        obj = ''

        img = cv2.imread(jpg)
        img_h, img_w = img.shape[0], img.shape[1]
        head = xml_head.format(str(jpg), str(img_w), str(img_h))
        with open(txt, 'r') as f:
            for line in f.readlines():
                yolo_datas = line.strip().split(' ')
                label = int(float(yolo_datas[0].strip()))
                center_x = round(float(str(yolo_datas[1]).strip()) * img_w)
                center_y = round(float(str(yolo_datas[2]).strip()) * img_h)
                bbox_width = round(float(str(yolo_datas[3]).strip()) * img_w)
                bbox_height = round(float(str(yolo_datas[4]).strip()) * img_h)

                xmin = str(int(center_x - bbox_width / 2))
                ymin = str(int(center_y - bbox_height / 2))
                xmax = str(int(center_x + bbox_width / 2))
                ymax = str(int(center_y + bbox_height / 2))

                obj += xml_obj.format(labels[label], xmin, ymin, xmax, ymax)
        with open(xml_path, 'w') as f_xml:
            f_xml.write(head + obj + xml_end)
        cnt += 1
        print(cnt)

xml转json

目的是把xml数据和图片都存到当前目录的annotations中。

import os
import glob
import json
import shutil
import numpy as np
import xml.etree.ElementTree as ET
 
 
 
path2 = "."#当前该文件路径
 
 
START_BOUNDING_BOX_ID = 1
 
 
def get(root, name):
    return root.findall(name)
 
 
def get_and_check(root, name, length):
    vars = root.findall(name)
    if len(vars) == 0:
        raise NotImplementedError('Can not find %s in %s.'%(name, root.tag))
    if length > 0 and len(vars) != length:
        raise NotImplementedError('The size of %s is supposed to be %d, but is %d.'%(name, length, len(vars)))
    if length == 1:
        vars = vars[0]
    return vars
 
 
def convert(xml_list, json_file):
    json_dict = {"images": [], "type": "instances", "annotations": [], "categories": []}
    categories = pre_define_categories.copy()
    bnd_id = START_BOUNDING_BOX_ID
    all_categories = {}
    for index, line in enumerate(xml_list):
        # print("Processing %s"%(line))
        xml_f = line
        tree = ET.parse(xml_f)
        root = tree.getroot()
        
        filename = os.path.basename(xml_f)[:-4] + ".jpg"
        image_id = 1 + index
        size = get_and_check(root, 'size', 1)
        width = int(get_and_check(size, 'width', 1).text)
        height = int(get_and_check(size, 'height', 1).text)
        image = {'file_name': filename, 'height': height, 'width': width, 'id':image_id}
        json_dict['images'].append(image)
        ## Cruuently we do not support segmentation
        #  segmented = get_and_check(root, 'segmented', 1).text
        #  assert segmented == '0'
        for obj in get(root, 'object'):
            category = get_and_check(obj, 'name', 1).text
            if category in all_categories:
                all_categories[category] += 1
            else:
                all_categories[category] = 1
            if category not in categories:
                if only_care_pre_define_categories:
                    continue
                new_id = len(categories) + 1
                print("[warning] category '{}' not in 'pre_define_categories'({}), create new id: {} automatically".format(category, pre_define_categories, new_id))
                categories[category] = new_id
            category_id = categories[category]
            bndbox = get_and_check(obj, 'bndbox', 1)
            xmin = int(float(get_and_check(bndbox, 'xmin', 1).text))
            ymin = int(float(get_and_check(bndbox, 'ymin', 1).text))
            xmax = int(float(get_and_check(bndbox, 'xmax', 1).text))
            ymax = int(float(get_and_check(bndbox, 'ymax', 1).text))
            assert(xmax > xmin), "xmax <= xmin, {}".format(line)
            assert(ymax > ymin), "ymax <= ymin, {}".format(line)
            o_width = abs(xmax - xmin)
            o_height = abs(ymax - ymin)
            ann = {'area': o_width*o_height, 'iscrowd': 0, 'image_id':
                   image_id, 'bbox':[xmin, ymin, o_width, o_height],
                   'category_id': category_id, 'id': bnd_id, 'ignore': 0,
                   'segmentation': []}
            json_dict['annotations'].append(ann)#将生成的json文件加入annotations文件夹
            bnd_id = bnd_id + 1
 
    for cate, cid in categories.items():
        cat = {'supercategory': 'none', 'id': cid, 'name': cate}
        json_dict['categories'].append(cat)
    json_fp = open(json_file, 'w')
    json_str = json.dumps(json_dict)
    json_fp.write(json_str)
    json_fp.close()
    print("------------create {} done--------------".format(json_file))
    print("find {} categories: {} -->>> your pre_define_categories {}: {}".format(len(all_categories), all_categories.keys(), len(pre_define_categories), pre_define_categories.keys()))
    print("category: id --> {}".format(categories))
    print(categories.keys())
    print(categories.values())
 
 
if __name__ == '__main__':
    classes = ['person']
    pre_define_categories = {}
    for i, cls in enumerate(classes):
        pre_define_categories[cls] = i + 1
    # pre_define_categories = {'a1': 1, 'a3': 2, 'a6': 3, 'a9': 4, "a10": 5}
    only_care_pre_define_categories = True
    # only_care_pre_define_categories = False
 
    train_ratio = 1#控制train和val的比例，train_ratio=1是全部生成为train数据
    save_json_train = 'instances_train2014.json'#生成训练集json文件名
    save_json_val = 'instances_val2014.json'
    xml_dir = "Annotations"     #存放xml文件的文件夹
 
    xml_list = glob.glob(xml_dir + "/*.xml")
    xml_list = np.sort(xml_list)
    np.random.seed(100)
    np.random.shuffle(xml_list)
 
    train_num = int(len(xml_list)*train_ratio)
    xml_list_train = xml_list[:train_num]
    xml_list_val = xml_list[train_num:]
 
    convert(xml_list_train, save_json_train)
    convert(xml_list_val, save_json_val)
 
    if os.path.exists(path2 + "/annotations"):
        shutil.rmtree(path2 + "/annotations")
    os.makedirs(path2 + "/annotations")
    if os.path.exists(path2 + "/images/train2014"):
        shutil.rmtree(path2 + "/images/train2014")
    os.makedirs(path2 + "/images/train2014")
    if os.path.exists(path2 + "/images/val2014"):
        shutil.rmtree(path2 +"/images/val2014")
    os.makedirs(path2 + "/images/val2014")
 
    f1 = open("train.txt", "w")
    for xml in xml_list_train:
        img = xml[:-4] + ".jpg"#根据xml文件路径获取图片路径，此时图片和xml文件都在annotations文件夹中
        f1.write(os.path.basename(xml)[:-4] + "\n")
        shutil.copyfile(img, path2 + "/images/train2014/" + os.path.basename(img))#将用于训练的图片存入训练集
 
    f2 = open("test.txt", "w")
    for xml in xml_list_val:
        img = xml[:-4] + ".jpg"
        f2.write(os.path.basename(xml)[:-4] + "\n") 
        shutil.copyfile(img, path2 + "/images/val2014/" + os.path.basename(img))#将用于测试的图片存入测试集
    f1.close()
    f2.close()
    print("-------------------------------")
    print("train number:", len(xml_list_train))
    print("val number:", len(xml_list_val))