目标检测 | 常用数据集标注格式以及转换代码

本文主要是介绍目标检测 | 常用数据集标注格式以及转换代码，对大家解决编程问题具有一定的参考价值，需要的程序猿们随着小编来一起学习吧！

文章目录

1. COCO
- 1.1 COCO数据集格式
- 1.2 COCO转换脚本
2. VOC
- 2.1 VOC数据集格式
- 2.2 VOC转换脚本
3. YOLO
- 3.1 YOLO数据集格式
- 3.2 YOLO转换脚本

我的博客https://blog.justlovesmile.top/

目标检测是计算机视觉任务中的一个重要研究方向，其用于解决对数码图像中特定种类的可视目标实例的检测问题。目标检测作为计算机视觉的根本性问题之一，是其他诸多计算机视觉任务，例如图像描述生成，实例分割和目标跟踪的基础以及前提。而在解决此类问题时，我们常常需要使用自己的脚本或者利用标注工具生成数据集，数据集格式往往会多种多样，因此对于目标检测任务而言，为了更好地兼容训练，大多数目标检测模型框架会默认支持几种常用的数据集标注格式，常见的分别是COCO，Pascal VOC，YOLO等等。本文主要介绍上述几种数据集格式以及我写的Python脚本（一般需要根据实际情况再改改）。

1. COCO

1.1 COCO数据集格式

COCO（Common Objects in COtext）数据集，是一个大规模的，适用于目标检测，图像分割，Image Captioning任务的数据集，其标注格式是最常用的几种格式之一。目前使用较多的是COCO2017数据集。其官网为COCO - Common Objects in Context (cocodataset.org)。

COCO数据集主要包含图像（jpg或者png等等）和标注文件（json），其数据集格式如下(/代表文件夹)：

-coco/
    |-train2017/
    	|-1.jpg
    	|-2.jpg
    |-val2017/
    	|-3.jpg
    	|-4.jpg
    |-test2017/
    	|-5.jpg
    	|-6.jpg
    |-annotations/
    	|-instances_train2017.json
    	|-instances_val2017.json
    	|-*.json

train2017以及val2017这两个文件夹中存储的是训练集和验证集的图像，而test2017文件夹中存储的是测试集的信息，可以只是图像，也可以包含标注，一般是单独使用的。

annotations文件夹中的文件就是标注文件，如果你有xml文件，通常需要转换成json格式，其格式如下（更详细的可以参考官网）：

{
	"info": info, 
	"images": [image], //列表
	"annotations": [annotation], //列表
	"categories": [category], //列表
	"licenses": [license], //列表
}

其中info为整个数据集的信息，包括年份，版本，描述等等信息，如果只是完成训练任务，其实不太重要，如下所示：

//对于训练，不是那么的重要
info{
	"year": int, 
	"version": str, 
	"description": str, 
	"contributor": str, 
	"url": str, 
	"date_created": datetime,
}

其中的image为图像的基本信息，包括序号，宽高，文件名等等信息，其中的序号（id）需要和后面的annotations中的标注所属图片序号对应如下所示：

image{
	"id": int, //必要
	"width": int, //必要
	"height": int, //必要
	"file_name": str, //必要
	"license": int,
	"flickr_url": str,
	"coco_url": str,
	"date_captured": datetime, 
}

其中的annotation是最重要的标注信息，包括序号，所属图像序号，类别序号等等信息，如下所示：

annotation{
	"id": int, //标注id
	"image_id": int, //所属图像id
	"category_id": int, //类别id
	"segmentation": RLE or [polygon], //图像分割标注
	"area": float, //区域面积
	"bbox": [x,y,width,height], //目标框左上角坐标以及宽高
	"iscrowd": 0 or 1, //是否密集
}

其中的category代表类别信息，包括父类别，类别序号以及类别名称，如下所示：

category{
	"id": int, //类别序号
	"name": str, //类别名称
	"supercategory": str, //父类别
}

其中的license代表数据集的协议许可信息，包括序号，协议名称以及链接信息，如下所示：

//对于训练，不重要
license{
	"id": int, 
	"name": str, 
	"url": str,
}

接下来，我们来看一个简单的示例：

{
"info": {略}, "images": [{"id": 1, "file_name": "1.jpg", "height": 334, "width": 500}, {"id": 2, "file_name": "2.jpg", "height": 445, "width": 556}], "annotations": [{"id": 1, "area": 40448, "iscrowd": 0, "image_id": 1, "bbox": [246, 61, 128, 316], "category_id": 3, "segmentation": []}, {"id": 2, "area": 40448, "iscrowd": 0, "image_id": 1, "bbox": [246, 61, 128, 316], "category_id": 2, "segmentation": []}, {"id": 3, "area": 40448, "iscrowd": 0, "image_id": 2, "bbox": [246, 61, 128, 316], "category_id": 1, "segmentation": []}], "categories": [{"supercategory": "none", "id": 1, "name": "liner"},{"supercategory": "none", "id": 2, "name": "containership"},{"supercategory": "none", "id": 3, "name": "bulkcarrier"}], "licenses": [{略}]
}

1.2 COCO转换脚本

Python转换脚本如下所示，需要准备图像和xml标注文件：

# -*- coding: utf-8 -*-
# @Author    : justlovesmile
# @Date      : 2021/9/8 15:36
import os, random, json
import shutil as sh
from tqdm.auto import tqdm
import xml.etree.ElementTree as xmlET

def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)
        return True
    else:
        print(f"The path ({path}) already exists.")
        return False

def readxml(file):
    tree = xmlET.parse(file)
    #图片尺寸字段
    size = tree.find('size')
    width = int(size.find('width').text)
    height = int(size.find('height').text)
    #目标字段
    objs = tree.findall('object')
    bndbox = []
    for obj in objs:
        label = obj.find("name").text
        bnd = obj.find("bndbox")
        xmin = int(bnd.find("xmin").text)
        ymin = int(bnd.find("ymin").text)
        xmax = int(bnd.find("xmax").text)
        ymax = int(bnd.find("ymax").text)
        bbox = [xmin, ymin, xmax, ymax, label]
        bndbox.append(bbox)
    return [[width, height], bndbox]

def tococo(xml_root, image_root, output_root,classes={},errorId=[],train_percent=0.9):
    # assert
    assert train_percent<=1 and len(classes)>0
    # define the root path
    train_root = os.path.join(output_root, "train2017")
    val_root = os.path.join(output_root, "val2017")
    ann_root = os.path.join(output_root, "annotations")
    # initialize train and val dict
    train_content = {
        "images": [],  # {"file_name": "09780.jpg", "height": 334, "width": 500, "id": 9780}
        "annotations": [],# {"area": 40448, "iscrowd": 0, "image_id": 1, "bbox": [246, 61, 128, 316], "category_id": 5, "id": 1, "segmentation": []}
        "categories": []  # {"supercategory": "none", "id": 1, "name": "liner"}
    }
    val_content = {
        "images": [],  # {"file_name": "09780.jpg", "height": 334, "width": 500, "id": 9780}
        "annotations": [],# {"area": 40448, "iscrowd": 0, "image_id": 1, "bbox": [246, 61, 128, 316], "category_id": 5, "id": 1, "segmentation": []}
        "categories": []  # {"supercategory": "none", "id": 1, "name": "liner"}
    }
    train_json = 'instances_train2017.json'
    val_json = 'instances_val2017.json'
    # divide the trainset and valset
    images = os.listdir(image_root)
    total_num = len(images)
    train_percent = train_percent
    train_num = int(total_num * train_percent)
    train_file = sorted(random.sample(images, train_num))
    if mkdir(output_root):
        if mkdir(train_root) and mkdir(val_root) and mkdir(ann_root):
            idx1, idx2, dx1, dx2 = 0, 0, 0, 0
            for file in tqdm(images):
                name=os.path.splitext(os.path.basename(file))[0]
                if name not in errorId:
                    res = readxml(os.path.join(xml_root, name + '.xml'))
                    if file in train_file:
                        idx1 += 1
                        sh.copy(os.path.join(image_root, file), train_root)
                        train_content['images'].append(
                            {"file_name": file, "width": res[0][0], "height": res[0][1], "id": idx1})
                        for b in res[1]:
                            dx1 += 1
                            x = b[0]
                            y = b[1]
                            w = b[2] - b[0]
                            h = b[3] - b[1]
                            train_content['annotations'].append(
                                {"area": w * h, "iscrowd": 0, "image_id": idx1, "bbox": [x, y, w, h],
                                 "category_id": classes[b[4]], "id": dx1, "segmentation": []})
                    else:
                        idx2 += 1
                        sh.copy(os.path.join(image_root, file), val_root)
                        val_content['images'].append(
                            {"file_name": file, "width": res[0][0], "height": res[0][1], "id": idx2})
                        for b in res[1]:
                            dx2 += 1
                            x = b[0]
                            y = b[1]
                            w = b[2] - b[0]
                            h = b[3] - b[1]
                            val_content['annotations'].append(
                                {"area": w * h, "iscrowd": 0, "image_id": idx2, "bbox": [x, y, w, h],
                                 "category_id": classes[b[4]], "id": dx2, "segmentation": []})
            for i, j in classes.items():
                train_content['categories'].append({"supercategory": "none", "id": j, "name": i})
                val_content['categories'].append({"supercategory": "none", "id": j, "name": i})
            with open(os.path.join(ann_root, train_json), 'w') as f:
                json.dump(train_content, f)
            with open(os.path.join(ann_root, val_json), 'w') as f:
                json.dump(val_content, f)
    print("Number of Train Images:", len(os.listdir(train_root)))
    print("Number of Val Images:", len(os.listdir(val_root)))
    
    
def test():
    box_root = "E:/MyProject/Dataset/hwtest/annotations" #xml文件夹
    image_root = "E:/MyProject/Dataset/hwtest/images" #image文件夹
    output_root = "E:/MyProject/Dataset/coco" #输出文件夹
    classes = {"liner": 0,"bulk carrier": 1,"warship": 2,"sailboat": 3,"canoe": 4,"container ship": 5,"fishing boat": 6} #类别字典
    errorId = [] #脏数据id
    train_percent = 0.9 #训练集和验证集比例
    tococo(box_root, image_root, output_root,classes=classes,errorId=errorId,train_percent=train_percent)

if __name__ == "__main__":
    test()

2. VOC

2.1 VOC数据集格式

VOC（Visual Object Classes）数据集来源于PASCAL VOC挑战赛，其主要任务有Object Classification 、Object Detection、Object Segmentation、Human Layout、Action Classification。其官网为The PASCAL Visual Object Classes Homepage (ox.ac.uk)。其主要数据集有VOC2007以及VOC2012。

VOC数据集主要包含图像（jpg或者png等等）和标注文件（xml），其数据集格式如下(/代表文件夹)：

-VOC/
	|-JPEGImages/
		|-1.jpg
		|-2.jpg
	|-Annotations/
		|-1.xml
		|-2.xml
	|-ImageSets/
		|-Layout/
			|-*.txt
		|-Main/
			|-train.txt
			|-val.txt
			|-trainval.txt
			|-test.txt
		|-Segmentation/
			|-*.txt
		|-Action/
			|-*.txt
	|-SegmentationClass/
	|-SegmentationObject/

其中对于目标检测任务而言，最常用的以及必须的文件夹包括：JPEGImages，Annotations，ImageSets/Main。

JPEGImages里存放的是图像，而Annotations里存放的是xml标注文件，文件内容如下：

<annotation>
	<folder>VOC</folder>            # 图像所在文件夹
	<filename>000032.jpg</filename> # 图像文件名
	<source>                        # 图像源
		<database>The VOC Database</database>
		<annotation>PASCAL VOC</annotation>
		<image>flickr</image>
	</source>
	<size>                          # 图像尺寸信息
		<width>500</width>    # 图像宽度
		<height>281</height>  # 图像高度
		<depth>3</depth>      # 图像通道数
	</size>
	<segmented>0</segmented>  # 图像是否用于分割，0代表不适用，对目标检测而言没关系
	<object>                  # 一个目标对象的信息
		<name>aeroplane</name>    # 目标的类别名
		<pose>Frontal</pose>      # 拍摄角度，若无一般为Unspecified
		<truncated>0</truncated>  # 是否被截断，0表示完整未截断
		<difficult>0</difficult>  # 是否难以识别，0表示不难识别
		<bndbox>            # 边界框信息
			<xmin>104</xmin>  # 左上角x
			<ymin>78</ymin>   # 左上角y
			<xmax>375</xmax>  # 右下角x
			<ymax>183</ymax>  # 右下角y
		</bndbox>
	</object>
    # 下面是其他目标的信息，这里略掉
	<object>
        其他object信息，这里省略
	</object>
</annotation>

2.2 VOC转换脚本

下面这个脚本，只适用于有图像和xml文件的情况下，coco转voc格式以后有需要再写：

# -*- coding: utf-8 -*-
# @Author    : justlovesmile
# @Date      : 2021/9/8 21:01
import os,random
from tqdm.auto import tqdm
import shutil as sh

def mkdir(path):
    if not os.path.exists(path):
        os.mkdir(path)
        return True
    else:
        print(f"The path ({path}) already exists.")
        return False

def tovoc(xmlroot,imgroot,saveroot,errorId=[],classes={},tvp=1.0,trp=0.9):
    '''
    参数：
        root：数据集存放根目录
    功能：
        加载数据，并保存为VOC格式
    加载后的格式：
    VOC/
      Annotations/
        - **.xml
      JPEGImages/
        - **.jpg
      ImageSets/
        Main/
          - train.txt
          - test.txt
          - val.txt
          - trainval.txt
    '''
    # assert
    assert len(classes)>0
    # init path
    VOC = saveroot
    ann_path = os.path.join(VOC, 'Annotations')
    img_path = os.path.join(VOC,'JPEGImages')
    set_path = os.path.join(VOC,'ImageSets')
    txt_path = os.path.join(set_path,'Main')
    # mkdirs 
    if mkdir(VOC):
        if mkdir(ann_path) and mkdir(img_path) and mkdir(set_path):
            mkdir(txt_path)

    images = os.listdir(imgroot)
    list_index = range(len(images))
    #test and trainval set
    trainval_percent = tvp
    train_percent = trp
    val_percent = 1 - train_percent if train_percent<1 else 0.1
    total_num = len(images)
    trainval_num = int(total_num*trainval_percent)
    train_num = int(trainval_num*train_percent)
    val_num = int(trainval_num*val_percent) if train_percent<1 else 0

    trainval = random.sample(list_index,trainval_num)
    train = random.sample(list_index,train_num)
    val = random.sample(list_index,val_num)
    
    for i in tqdm(list_index):
        imgfile = images[i]
        img_id = os.path.splitext(os.path.basename(imgfile))[0]
        xmlfile = img_id+".xml"
        sh.copy(os.path.join(imgroot,imgfile),os.path.join(img_path,imgfile))
        sh.copy(os.path.join(xmlroot,xmlfile),os.path.join(ann_path,xmlfile))
        if img_id not in errorId:
            if i in trainval:
                with open(os.path.join(txt_path,'trainval.txt'),'a') as f:
                    f.write(img_id+'\n')
                if i in train:
                    with open(os.path.join(txt_path,'train.txt'),'a') as f:
                        f.write(img_id+'\n')
                else:
                    with open(os.path.join(txt_path,'val.txt'),'a') as f:
                        f.write(img_id+'\n')
                if train_percent==1 and i in val:
                    with open(os.path.join(txt_path,'val.txt'),'a') as f:
                        f.write(img_id+'\n')          
            else:
                with open(os.path.join(txt_path,'test.txt'),'a') as f:
                    f.write(img_id+'\n')
    
    # end
    print("Dataset to VOC format finished!")

def test():
    box_root = "E:/MyProject/Dataset/hwtest/annotations"
    image_root = "E:/MyProject/Dataset/hwtest/images"
    output_root = "E:/MyProject/Dataset/voc"
    classes = {"liner": 0,"bulk carrier": 1,"warship": 2,"sailboat": 3,"canoe": 4,"container ship": 5,"fishing boat": 6}
    errorId = []
    train_percent = 0.9
    tovoc(box_root,image_root,output_root,errorId,classes,trp=train_percent)

if __name__ == "__main__":
    test()

3. YOLO

3.1 YOLO数据集格式

YOLO数据集格式的出现主要是为了训练YOLO模型，其文件格式没有固定的要求，因为可以通过修改模型的配置文件进行数据加载，唯一需要注意的是YOLO数据集的标注格式是将目标框的位置信息进行归一化处理（此处归一化指的是除以图片宽和高），如下所示：

{目标类别} {归一化后的目标中心点x坐标} {归一化后的目标中心点y坐标} {归一化后的目标框宽度w} {归一化后的目标框高度h}

3.2 YOLO转换脚本

Python转换脚本如下所示：

# -*- coding: utf-8 -*-
# @Author    : justlovesmile
# @Date      : 2021/9/8 20:28
import os
import random
from tqdm.auto import tqdm
import shutil as sh
try:
    import xml.etree.cElementTree as et
except ImportError:
    import xml.etree.ElementTree as et

def mkdir(path):
    if not os.path.exists(path):
        os.makedirs(path)
        return True
    else:
        print(f"The path ({path}) already exists.")
        return False  

def xml2yolo(xmlpath,savepath,classes={}):
    namemap = classes
    #try:
    #    with open('classes_yolo.json','r') as f:
    #        namemap=json.load(f)
    #except:
    #    pass
    rt = et.parse(xmlpath).getroot()
    w = int(rt.find("size").find("width").text)
    h = int(rt.find("size").find("height").text)
    with open(savepath, "w") as f:
        for obj in rt.findall("object"):
            name = obj.find("name").text
            xmin = int(obj.find("bndbox").find("xmin").text)
            ymin = int(obj.find("bndbox").find("ymin").text)
            xmax = int(obj.find("bndbox").find("xmax").text)
            ymax = int(obj.find("bndbox").find("ymax").text)
            f.write(
                f"{namemap[name]} {(xmin+xmax)/w/2.} {(ymin+ymax)/h/2.} {(xmax-xmin)/w} {(ymax-ymin)/h}"
                + "\n"
            )

def trainval(xmlroot,imgroot,saveroot,errorId=[],classes={},tvp=1.0,trp=0.9):
    # assert
    assert tvp<=1.0 and trp <=1.0 and len(classes)>0
    # create dirs
    imglabel = ['images','labels']
    trainvaltest = ['train','val','test']
    mkdir(saveroot)
    for r in imglabel:
        mkdir(os.path.join(saveroot,r))
        for s in trainvaltest:
            mkdir(os.path.join(saveroot,r,s))
    #train / val
    trainval_percent = tvp
    train_percent = trp
    val_percent = 1 - train_percent if train_percent<1.0 else 0.15
    
    total_img = os.listdir(imgroot)
    num = len(total_img)
    list_index = range(num)
    tv = int(num * trainval_percent)
    tr = int(tv * train_percent)
    va = int(tv * val_percent)
    trainval = random.sample(list_index, tv) # trainset and valset
    train = random.sample(trainval, tr) # trainset
    val = random.sample(trainval, va) #valset, use it only when train_percent = 1 

    print(f"trainval_percent:{trainval_percent},train_percent:{train_percent},val_percent:{val_percent}")
    for i in tqdm(list_index):
        name = total_img[i]
        op = os.path.join(imgroot,name)
        file_id = os.path.splitext(os.path.basename(name))[0]
        if file_id not in errorId:
            xmlp = os.path.join(xmlroot,file_id+'.xml')
            if i in trainval:
                # trainset and valset
                if i in train:
                    sp = os.path.join(saveroot,"images","train",name)
                    xml2yolo(xmlp,os.path.join(saveroot,"labels","train",file_id+'.txt'),classes)
                    sh.copy(op,sp)
                else:
                    sp = os.path.join(saveroot,"images","val",name)
                    xml2yolo(xmlp,os.path.join(saveroot,"labels","val",file_id+'.txt'),classes)
                    sh.copy(op,sp)
                if (train_percent==1.0 and i in val):
                    sp = os.path.join(saveroot,"images","val",name)
                    xml2yolo(xmlp,os.path.join(saveroot,"labels","val",file_id+'.txt'),classes)
                    sh.copy(op,sp)
            else:
                # testset
                sp = os.path.join(saveroot,"images","test",name)
                xml2yolo(xmlp,os.path.join(saveroot,"labels","test",file_id+'.txt'),classes)
                sh.copy(op,sp)

def maketxt(dir,saveroot,filename):
    savetxt = os.path.join(saveroot,filename)
    with open(savetxt,'w') as f:
        for i in tqdm(os.listdir(dir)):
            f.write(os.path.join(dir,i)+'\n')
                           
def toyolo(xmlroot,imgroot,saveroot,errorId=[],classes={},tvp=1,train_percent=0.9):
    # toyolo main function
    trainval(xmlroot,imgroot,saveroot,errorId,classes,tvp,train_percent)
    maketxt(os.path.join(saveroot,"images","train"),saveroot,"train.txt")
    maketxt(os.path.join(saveroot,"images","val"),saveroot,"val.txt")
    maketxt(os.path.join(saveroot,"images","test"),saveroot,"test.txt")
    print("Dataset to yolo format success.")

def test():
    box_root = "E:/MyProject/Dataset/hwtest/annotations"
    image_root = "E:/MyProject/Dataset/hwtest/images"
    output_root = "E:/MyProject/Dataset/yolo"
    classes = {"liner": 0,"bulk carrier": 1,"warship": 2,"sailboat": 3,"canoe": 4,"container ship": 5,"fishing boat": 6}
    errorId = []
    train_percent = 0.9
    toyolo(box_root,image_root,output_root,errorId,classes,train_percent=train_percent)

if __name__ == "__main__":
    test()

按照此脚本，将会在输出文件夹中生成以下内容：

-yolo/
	|-images/
		|-train/
			|-1.jpg
			|-2.jpg
		|-test/
			|-3.jpg
			|-4.jpg
		|-val/
			|-5.jpg
			|-6.jpg
	|-labels/
		|-train/
			|-1.txt
			|-2.txt
		|-test/
			|-3.txt
			|-4.txt
		|-val/
			|-5.txt
			|-6.txt
	|-train.txt
	|-test.txt
	|-val.txt

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Java教程

目标检测 | 常用数据集标注格式以及转换代码

文章目录

1. COCO

1.1 COCO数据集格式

1.2 COCO转换脚本

2. VOC

2.1 VOC数据集格式

2.2 VOC转换脚本

3. YOLO

3.1 YOLO数据集格式

3.2 YOLO转换脚本

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