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：
- 通过labelme标注的矩形框标签为
.json格式 - > yolo模型需要的.txt格式
- 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 osname2id = {'peanuthull': 0, 'kernel': 1}def convert(img_size, box):dw = 1. / (img_size[0])dh = 1. / (img_size[1])x = (box[0] + box[2]) / 2.0y = (box[1] + box[3]) / 2.0w = abs(box[2] - box[0])h = abs(box[3] - box[1])x = x * dww = w * dwy = y * dhh = h * dhreturn (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 = 0y_max = 0x_min = 100000y_min = 100000for 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 = x1if y_max < y1:y_max = y1if y_min > y1:y_min = y1if x_min > x1:x_min = x1bb = (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:passif __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 = 0for json_name in json_names:decode_json(json_floder_path, txt_outer_path, json_name)flagcount += 1print("还剩下{}个文件未转化".format(len(json_names) - flagcount))# breakprint('转化全部完毕')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 joindef convert(size, box):x_center = (box[0] + box[1]) / 2.0y_center = (box[2] + box[3]) / 2.0x = 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').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))# 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)
