（18）目标检测算法之数据集标签格式转换：json2txt、xml2txt

目标检测算法之数据集标签格式转换：json2txt、xml2txt

目标检测最常见的模型：YOLO，常见的几种标注方式：矩形框、旋转矩形框、实例分割中的多边形标注等类型，根据其标注标签，目标检测主要有以下两种转换方式：

• 1. 通过labelme标注的矩形框标签为.json格式 - > yolo模型需要的.txt格式
• 2. 通过labelimg标注的矩形框标签为.xml格式 -> yolo模型需要的.txt格式

---

下边详细给出转换demo：

1. json2txt

• labelme安装

pip labelme
pip pyqt5

标注：鼠标在图像上右键选择create Rectangle即可创建矩形框进行标注

• 下边是将.json标签文件转换为.txt格式demo：

# 处理labelme多边形矩阵的标注  json转化txt
import json
import os

name2id = {
    
    'peanuthull': 0, 'kernel': 1}


def convert(img_size, box):
    dw = 1. / (img_size[0])
    dh = 1. / (img_size[1])
    x = (box[0] + box[2]) / 2.0
    y = (box[1] + box[3]) / 2.0
    w = abs(box[2] - box[0])
    h = abs(box[3] - box[1])
    x = x * dw
    w = w * dw
    y = y * dh
    h = h * dh
    return (x, y, w, h)


def decode_json(json_floder_path, txt_outer_path, json_name):
    #  json_floder_path='E:\\Python_package\\itesjson\\'
    # json_name='V1125.json'
    txt_name = txt_outer_path + json_name[:-5] + '.txt'
    with open(txt_name, 'w') as f:
        json_path = os.path.join(json_floder_path, json_name)  # os路径融合
        data = json.load(open(json_path, 'r', encoding='gb2312', errors='ignore'))
        img_w = data['imageWidth']  # 图片的高
        img_h = data['imageHeight']  # 图片的宽
        isshape_type = data['shapes'][0]['shape_type']
        print(isshape_type)
        # print(isshape_type)
        # print('下方判断根据这里的值可以设置为你自己的类型，我这里是polygon'多边形)
        # len(data['shapes'])
        for i in data['shapes']:
            label_name = i['label']  # 得到json中你标记的类名
            if (i['shape_type'] == 'polygon'):  # 数据类型为多边形 需要转化为矩形
                x_max = 0
                y_max = 0
                x_min = 100000
                y_min = 100000
                for lk in range(len(i['points'])):
                    x1 = float(i['points'][lk][0])
                    y1 = float(i['points'][lk][1])
                    # print(x1)
                    if x_max < x1:
                        x_max = x1
                    if y_max < y1:
                        y_max = y1
                    if y_min > y1:
                        y_min = y1
                    if x_min > x1:
                        x_min = x1
                bb = (x_min, y_max, x_max, y_min)
            if (i['shape_type'] == 'rectangle'):  # 为矩形不需要转换
                x1 = float(i['points'][0][0])
                y1 = float(i['points'][0][1])
                x2 = float(i['points'][1][0])
                y2 = float(i['points'][1][1])
                bb = (x1, y1, x2, y2)
            bbox = convert((img_w, img_h), bb)
            try:
                f.write(str(name2id[label_name]) + " " + " ".join([str(a) for a in bbox]) + '\n')
            except:
                pass


if __name__ == "__main__":
    json_floder_path = 'data_\\jsons\\'  # 存放json的文件夹的绝对路径
    txt_outer_path = 'data_\\txts\\'  # 存放txt的文件夹绝对路径
    json_names = os.listdir(json_floder_path)
    print("共有：{}个文件待转化".format(len(json_names)))
    flagcount = 0
    for json_name in json_names:
        decode_json(json_floder_path, txt_outer_path, json_name)
        flagcount += 1
        print("还剩下{}个文件未转化".format(len(json_names) - flagcount))

    # break
    print('转化全部完毕')

转换后生成.txt如下：

• 当出现了以下异常的标签时，可同时将图像复制到jpgs文件夹中，将标签复制到jsons文件夹中，并修改程序如下：
  

****************************修改图像尺寸异常****************************
        img_w = data['imageWidth']  # 图片的高
        img_h = data['imageHeight']  # 图片的宽
        imgpath=data['imagePath']
        img=cv2.imread(json_floder_path+imgpath)
        if img_h==0:
            img_h=img.shape[0]
        if img_w==0:
            img_w=img.shape[1]
***********************修改路径********************************
     jpgs_path='./jpgs/'
    json_floder_path = '.\\jsons\\'  # 存放json的文件夹的绝对路径
    txt_outer_path = '.\\txts\\'  # 存放txt的文件夹绝对路径
    json_names = os.listdir(json_floder_path)
    print("共有：{}个文件待转化".format(len(json_names)))
    flagcount = 0
    for json_name in json_names:
        decode_json(json_floder_path, txt_outer_path, json_name)
        flagcount += 1
        print("还剩下{}个文件未转化".format(len(json_names) - flagcount))

2. xml2txt

• labelimg安装

pip labelimg
pip pyqt5

• 标注：左键选择create RectBox即可拉框标注
  
• 下边给出.xml转为.txt的demo：

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


def convert(size, box):
    x_center = (box[0] + box[1]) / 2.0
    y_center = (box[2] + box[3]) / 2.0
    x = x_center / size[0]
    y = y_center / size[1]

    w = (box[1] - box[0]) / size[0]
    h = (box[3] - box[2]) / size[1]

    return (x, y, w, h)


def convert_annotation(xml_files_path, save_txt_files_path, classes):
    xml_files = os.listdir(xml_files_path)
    print(xml_files)
    for xml_name in xml_files:
        print(xml_name)
        xml_file = os.path.join(xml_files_path, xml_name)
        out_txt_path = os.path.join(save_txt_files_path, xml_name.split('.')[0] + '.txt')
        out_txt_f = open(out_txt_path, 'w')
        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
            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))
            # b=(xmin, xmax, ymin, ymax)
            print(w, h, b)
            bb = convert((w, h), b)
            out_txt_f.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


if __name__ == "__main__":
    classes = ['person', 'face','hand','garb','larwas','conwas','foowas','recyc'] #8类
    # 1、voc格式的xml标签文件路径
    xml_files1 = r'F:\Deeplearning\yolov5-master\mytrain\xmls'
    # 2、转化为yolo格式的txt标签文件存储路径
    save_txt_files1 = r'F:\Deeplearning\yolov5-master\mytrain\labels'

    convert_annotation(xml_files1, save_txt_files1, classes)