本篇文章主要介绍如何对初始数据集的图像数据打标签，以及如何将打标签之后的json格式的数据转化为可以直接进行训练的xml格式。
一、如何打标签
1、获得一份jpg格式的图像数据集
2、在电脑开始栏输入cmd，打开命令窗口，输入pip install labelme，等待安装完成，输入labelme，打开界面
3、点击打开目录，找到存储图像数据集的文件夹，右侧的文件列表显示该文件夹当中所有的图片
4、按住CTRL+R键，选择你要打标记的区域，点击两下鼠标左键，形成一个矩形框，对该分类进行命名，点击ok。点击文件→自动保存，生成的 json文件会自动保存在当前图像数据集的文件夹当中，点击下一幅，右侧文件列表会打勾，显示图像数据集的标注进度。重复此步骤，直到所有图像都被打上标签为止。最后，文件夹内每幅图像对应一个json文件
注：
Ⅰ 如果图像有多个类别，那么针对不同类别应归为不同标签之内
Ⅱ 可在属性当中查看文件夹中图像个数
二、如何进行数据格式转化
1、目标
获得一份拥有Annocations、ImageSets、JPEGImages三个子文件夹的VOCdevkit文件夹
2、python代码
import os
import numpy as np
import codecs
import json
import glob
import cv2
import shutil
from sklearn.model_selection import train_test_split
# 1.标签路径
labelme_path = "E:\\JPEGImages" # 原始labelme标注数据路径
saved_path = "E:\\VOCdevkit" # 保存路径
# 2.创建要求文件夹
dst_annotation_dir = os.path.join(saved_path, 'Annotations')
if not os.path.exists(dst_annotation_dir):
os.makedirs(dst_annotation_dir)
dst_image_dir = os.path.join(saved_path, "JPEGImages")
if not os.path.exists(dst_image_dir):
os.makedirs(dst_image_dir)
dst_main_dir = os.path.join(saved_path, "ImageSets", "Main")
if not os.path.exists(dst_main_dir):
os.makedirs(dst_main_dir)
# 3.获取待处理文件
org_json_files = sorted(glob.glob(os.path.join(labelme_path, '*.json')))
org_json_file_names = [i.split("\\")[-1].split(".json")[0] for i in org_json_files]
org_img_files = sorted(glob.glob(os.path.join(labelme_path, '*.jpeg')))
org_img_file_names = [i.split("\\")[-1].split(".jpeg")[0] for i in org_img_files]
# 4.labelme file to voc dataset
for i, json_file_ in enumerate(org_json_files):
json_file = json.load(open(json_file_, "r", encoding="utf-8"))
image_path = os.path.join(labelme_path, org_json_file_names[i]+'.jpeg')
img = cv2.imread(image_path)
height, width, channels = img.shape
dst_image_path = os.path.join(dst_image_dir, "{:06d}.jpeg".format(i))
cv2.imwrite(dst_image_path, img)
dst_annotation_path = os.path.join(dst_annotation_dir, '{:06d}.xml'.format(i))
with codecs.open(dst_annotation_path, "w", "utf-8") as xml:
xml.write('<annotation>\n')
xml.write('\t<folder>' + 'Pin_detection' + '</folder>\n')
xml.write('\t<filename>' + "{:06d}.jpeg".format(i) + '</filename>\n')
# xml.write('\t<source>\n')
# xml.write('\t\t<database>The UAV autolanding</database>\n')
# xml.write('\t\t<annotation>UAV AutoLanding</annotation>\n')
# xml.write('\t\t<image>flickr</image>\n')
# xml.write('\t\t<flickrid>NULL</flickrid>\n')
# xml.write('\t</source>\n')
# xml.write('\t<owner>\n')
# xml.write('\t\t<flickrid>NULL</flickrid>\n')
# xml.write('\t\t<name>ChaojieZhu</name>\n')
# xml.write('\t</owner>\n')
xml.write('\t<size>\n')
xml.write('\t\t<width>' + str(width) + '</width>\n')
xml.write('\t\t<height>' + str(height) + '</height>\n')
xml.write('\t\t<depth>' + str(channels) + '</depth>\n')
xml.write('\t</size>\n')
xml.write('\t\t<segmented>0</segmented>\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<object>\n')
xml.write('\t\t<name>' + label + '</name>\n')
xml.write('\t\t<pose>Unspecified</pose>\n')
xml.write('\t\t<truncated>1</truncated>\n')
xml.write('\t\t<difficult>0</difficult>\n')
xml.write('\t\t<bndbox>\n')
xml.write('\t\t\t<xmin>' + str(xmin) + '</xmin>\n')
xml.write('\t\t\t<ymin>' + str(ymin) + '</ymin>\n')
xml.write('\t\t\t<xmax>' + str(xmax) + '</xmax>\n')
xml.write('\t\t\t<ymax>' + str(ymax) + '</ymax>\n')
xml.write('\t\t</bndbox>\n')
xml.write('\t</object>\n')
print(json_file_, xmin, ymin, xmax, ymax, label)
xml.write('</annotation>')
# 5.split files for txt
train_file = os.path.join(dst_main_dir, 'train.txt')
trainval_file = os.path.join(dst_main_dir, 'trainval.txt')
val_file = os.path.join(dst_main_dir, 'val.txt')
test_file = os.path.join(dst_main_dir, 'test.txt')
ftrain = open(train_file, 'w')
ftrainval = open(trainval_file, 'w')
fval = open(val_file, 'w')
ftest = open(test_file, 'w')
total_annotation_files = glob.glob(os.path.join(dst_annotation_dir, "*.xml"))
total_annotation_names = [i.split("\\")[-1].split(".xml")[0] for i in total_annotation_files]
# test_filepath = ""
for file in total_annotation_names:
ftrainval.writelines(file + '\n')
# test
# for file in os.listdir(test_filepath):
# ftest.write(file.split(".jpg")[0] + "\n")
# split
train_files, val_files = train_test_split(total_annotation_names, test_size=0.2)
# 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()注：
Ⅰ 该代码需要修改的地方如下，需要将其更改为自己的文件路径
labelme_path = "E:\\JPEGImages" # 原始labelme标注数据路径
saved_path = "E:\\VOCdekit" # 保存路径Ⅱ 如果图像数据集为jpeg格式，需将代码中所有的"jpg"替换为"jpeg"
