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把LabelImg标注的YOLO格式标签转化为VOC格式标签

程序员文章站 2024-03-07 21:58:57
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把LabelImg标注的YOLO格式标签转化为VOC格式标签
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1 用LabelImgvoc和yolo标注标签格式说明

关于LabelImg工具的使用参考

1.1 LabelImg标注的VOC数据格式

VOC数据格式,会直接把每张图片标注的标签信息保存到一个xml文件中

例如:我们上面标注的JPEGImage/000001.jpg图片,标注的标签信息会保存到Annotation/000001.xml文件中,000001.xml中的信息如下:

<annotation>
	<folder>JPEGImage</folder>
	<filename>000000.jpg</filename>
	<path>D:\ZF\2_ZF_data\3_stamp_data\标注公章数据\JPEGImage\000000.jpg</path>
	<source>
		<database>Unknown</database>
	</source>
	<size>
		<width>500</width>
		<height>402</height>
		<depth>3</depth>
	</size>
	<segmented>0</segmented>
	<object>
		<name>circle_red</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>168</xmin>
			<ymin>2</ymin>
			<xmax>355</xmax>
			<ymax>186</ymax>
		</bndbox>
	</object>
	<object>
		<name>circle_red</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>2</xmin>
			<ymin>154</ymin>
			<xmax>208</xmax>
			<ymax>367</ymax>
		</bndbox>
	</object>
	<object>
		<name>circle_red</name>
		<pose>Unspecified</pose>
		<truncated>0</truncated>
		<difficult>0</difficult>
		<bndbox>
			<xmin>305</xmin>
			<ymin>174</ymin>
			<xmax>493</xmax>
			<ymax>364</ymax>
		</bndbox>
	</object>
</annotation>

xml中的关键信息说明:

  • 图片的名字
  • 每个目标的标定框坐标:即左上角的坐标右下角的坐标
    • xmin
    • ymin
    • xmax
    • ymax

1.2 LabelImg标注的YOLO数据格式

YOLO数据格式,会直接把每张图片标注的标签信息保存到一个txt文件中

例如:我们上面标注的JPEGImage/000001.jpg图片,标注的标签信息会保存到Annotation/000001.txt文件中(同时会生成一个classes.txt文件,也保存到Annotation/classes.txt),000001.txt中的信息如下:

0 0.521000 0.235075 0.362000 0.450249
0 0.213000 0.645522 0.418000 0.519900
0 0.794000 0.665423 0.376000 0.470149

txt中信息说明:

  • 每一行代表标注的一个目标
  • 第一个数代表标注目标的标签,第一目标circle_red,对应数字就是0
  • 后面的四个数代表标注框的中心坐标和标注框的相对宽和高(进行了归一化,如何归一化可以参考我的这篇博客中的介绍

同时会生成一个Annotation/classes.txt实际类别文件classes.txt,里面的内容如下:

circle_red
circle_gray
rectangle_red
rectangle_gray
fingeprint_red
fingeprint_gray
other

2 voc转换为yolo格式计算

标注好的VOC格式的标签xml文件,存储的主要信息为:

  • 图片的名字
  • 图片的高height、宽width、通道depth
  • 标定框的坐标位置:xmin、ymin、xmax、ymax

例如下图代表的是一样图片:

  • 红框代表的是原图大小:height=8,width=8
  • 蓝框代表的是标注物体的框:左上角坐标为 (xmin, ymin)=(2,2),右下角的坐标为 (xmax, ymax)=(6,6)
    把LabelImg标注的YOLO格式标签转化为VOC格式标签
    voc_label.py目的就是把标注为VOC格式数据转化为标注为yolo格式数据
  • VOC格式标签:图片的实际宽和高,标注框的左上角和右下角坐标
  • YOLO格式标签:标注框的中心坐标(归一化的),标注框的宽和高(归一化的)

VOC格式标签转换为YOLO格式标签计算公式:

框中心的实际坐标(x, y):(一般可能还会在后面减去1)
x_center=xmax+xmin2=6+22=4x\_center=\frac{xmax+xmin}{2}=\frac{6+2}{2}=4
y_center=ymax+ymin2=6+22=4y\_center=\frac{ymax+ymin}{2}=\frac{6+2}{2}=4

框归一化后的中心坐标(x, y):
x=x_centerwidth=48=0.5x=\frac{x\_center}{width}=\frac{4}{8}=0.5
y=y_centerheight=48=0.5y=\frac{y\_center}{height}=\frac{4}{8}=0.5

框的高和框(归一化的):
w=xmaxxminwidth=628=0.5w=\frac{xmax-xmin}{width}=\frac{6-2}{8}=0.5
h=ymaxyminheight=628=0.5h=\frac{ymax-ymin}{height}=\frac{6-2}{8}=0.5

3 yolo转换为voc格式计算

voc中保存的坐标信息为:xmin, ymin, xmax, ymax,所以只要根据上面的公式,推导出这四个值即可,推导如下:


推导:xmin, xmax
{xmax+xmin=2x_centerxmaxxmin=wwidth\begin{cases} xmax+xmin=2x\_center\\ xmax-xmin=w*width \end{cases}

{2xmax=2x_center+wwidth=>xmax=x_center+12wwidth2xmin=2x_centerwwidth=>xmin=x_center12wwidth\begin{cases} 2xmax=2x\_center+w*width=>xmax=x\_center+\frac{1}{2}*w*width\\ 2xmin=2x\_center-w*width=>xmin=x\_center-\frac{1}{2}*w*width \end{cases}

推导:ymin, ymax
{ymax+ymin=2y_centerymaxymin=yheight\begin{cases} ymax+ymin=2y\_center\\ ymax-ymin=y*height \end{cases}

{2ymax=2y_center+hheight=>ymax=y_center+12hheight2ymin=2y_centerhheight=>ymin=y_center12hheight\begin{cases} 2ymax=2y\_center+h*height=>ymax=y\_center+\frac{1}{2}*h*height\\ 2ymin=2y\_center-h*height=>ymin=y\_center-\frac{1}{2}*h*height \end{cases}

4 yolo格式标签转化为voc格式标签代码

  • 代码是把txt标签转化为voc标签
  • 代码支持一个标签文件中有多个目标
__Author__ = "Shliang"
__Email__ = "aaa@qq.com"

import os
import xml.etree.ElementTree as ET
from xml.dom.minidom import Document
import cv2


'''
import xml
xml.dom.minidom.Document().writexml()
def writexml(self,
             writer: Any,
             indent: str = "",
             addindent: str = "",
             newl: str = "",
             encoding: Any = None) -> None
'''

class YOLO2VOCConvert:
    def __init__(self, txts_path, xmls_path, imgs_path):
        self.txts_path = txts_path   # 标注的yolo格式标签文件路径
        self.xmls_path = xmls_path   # 转化为voc格式标签之后保存路径
        self.imgs_path = imgs_path   # 读取读片的路径个图片名字,存储到xml标签文件中
        self.classes = ["shirt", "non_shirt", "western_style_clothes", "coat", "down_filled_coat",
                        "cotton", "sweater", "silk_scarf", "tie", "bow_tie"]

    # 从所有的txt文件中提取出所有的类别, yolo格式的标签格式类别为数字 0,1,...
    # writer为True时,把提取的类别保存到'./Annotations/classes.txt'文件中
    def search_all_classes(self, writer=False):
        # 读取每一个txt标签文件,取出每个目标的标注信息
        all_names = set()
        txts = os.listdir(self.txts_path)
        # 使用列表生成式过滤出只有后缀名为txt的标签文件
        txts = [txt for txt in txts if txt.split('.')[-1] == 'txt']
        print(len(txts), txts)
        # 11 ['0002030.txt', '0002031.txt', ... '0002039.txt', '0002040.txt']
        for txt in txts:
            txt_file = os.path.join(self.txts_path, txt)
            with open(txt_file, 'r') as f:
                objects = f.readlines()
                for object in objects:
                    object = object.strip().split(' ')
                    print(object)  # ['2', '0.506667', '0.553333', '0.490667', '0.658667']
                    all_names.add(int(object[0]))
            # print(objects)  # ['2 0.506667 0.553333 0.490667 0.658667\n', '0 0.496000 0.285333 0.133333 0.096000\n', '8 0.501333 0.412000 0.074667 0.237333\n']

        print("所有的类别标签:", all_names, "共标注数据集:%d张" % len(txts))

        # 把从xmls标签文件中提取的类别写入到'./Annotations/classes.txt'文件中
        # if writer:
        #     with open('./Annotations/classes.txt', 'w') as f:
        #         for label in all_names:
        #             f.write(label + '\n')

        return list(all_names)

    def yolo2voc(self):
        # 创建一个保存xml标签文件的文件夹
        if not os.path.exists(self.xmls_path):
            os.mkdir(self.xmls_path)

        # # 读取每张图片,获取图片的尺寸信息(shape)
        # imgs = os.listdir(self.imgs_path)
        # for img_name in imgs:
        #     img = cv2.imread(os.path.join(self.imgs_path, img_name))
        #     height, width, depth = img.shape
        #     # print(height, width, depth)   # h 就是多少行(对应图片的高度), w就是多少列(对应图片的宽度)
        #
        # # 读取每一个txt标签文件,取出每个目标的标注信息
        # all_names = set()
        # txts = os.listdir(self.txts_path)
        # # 使用列表生成式过滤出只有后缀名为txt的标签文件
        # txts = [txt for txt in txts if txt.split('.')[-1] == 'txt']
        # print(len(txts), txts)
        # # 11 ['0002030.txt', '0002031.txt', ... '0002039.txt', '0002040.txt']
        # for txt_name in txts:
        #     txt_file = os.path.join(self.txts_path, txt_name)
        #     with open(txt_file, 'r') as f:
        #         objects = f.readlines()
        #         for object in objects:
        #             object = object.strip().split(' ')
        #             print(object)  # ['2', '0.506667', '0.553333', '0.490667', '0.658667']

        # 把上面的两个循环改写成为一个循环:
        imgs = os.listdir(self.imgs_path)
        txts = os.listdir(self.txts_path)
        txts = [txt for txt in txts if not txt.split('.')[0] == "classes"]  # 过滤掉classes.txt文件
        print(txts)
        # 注意,这里保持图片的数量和标签txt文件数量相等,且要保证名字是一一对应的   (后面改进,通过判断txt文件名是否在imgs中即可)
        if len(imgs) == len(txts):   # 注意:./Annotation_txt 不要把classes.txt文件放进去
            map_imgs_txts = [(img, txt) for img, txt in zip(imgs, txts)]
            txts = [txt for txt in txts if txt.split('.')[-1] == 'txt']
            print(len(txts), txts)
            for img_name, txt_name in map_imgs_txts:
                # 读取图片的尺度信息
                print("读取图片:", img_name)
                img = cv2.imread(os.path.join(self.imgs_path, img_name))
                height_img, width_img, depth_img = img.shape
                print(height_img, width_img, depth_img)   # h 就是多少行(对应图片的高度), w就是多少列(对应图片的宽度)

                # 获取标注文件txt中的标注信息
                all_objects = []
                txt_file = os.path.join(self.txts_path, txt_name)
                with open(txt_file, 'r') as f:
                    objects = f.readlines()
                    for object in objects:
                        object = object.strip().split(' ')
                        all_objects.append(object)
                        print(object)  # ['2', '0.506667', '0.553333', '0.490667', '0.658667']

                # 创建xml标签文件中的标签
                xmlBuilder = Document()
                # 创建annotation标签,也是根标签
                annotation = xmlBuilder.createElement("annotation")

                # 给标签annotation添加一个子标签
                xmlBuilder.appendChild(annotation)

                # 创建子标签folder
                folder = xmlBuilder.createElement("folder")
                # 给子标签folder中存入内容,folder标签中的内容是存放图片的文件夹,例如:JPEGImages
                folderContent = xmlBuilder.createTextNode(self.imgs_path.split('/')[-1])  # 标签内存
                folder.appendChild(folderContent)  # 把内容存入标签
                annotation.appendChild(folder)   # 把存好内容的folder标签放到 annotation根标签下

                # 创建子标签filename
                filename = xmlBuilder.createElement("filename")
                # 给子标签filename中存入内容,filename标签中的内容是图片的名字,例如:000250.jpg
                filenameContent = xmlBuilder.createTextNode(txt_name.split('.')[0] + '.jpg')  # 标签内容
                filename.appendChild(filenameContent)
                annotation.appendChild(filename)

                # 把图片的shape存入xml标签中
                size = xmlBuilder.createElement("size")
                # 给size标签创建子标签width
                width = xmlBuilder.createElement("width")  # size子标签width
                widthContent = xmlBuilder.createTextNode(str(width_img))
                width.appendChild(widthContent)
                size.appendChild(width)   # 把width添加为size的子标签
                # 给size标签创建子标签height
                height = xmlBuilder.createElement("height")  # size子标签height
                heightContent = xmlBuilder.createTextNode(str(height_img))  # xml标签中存入的内容都是字符串
                height.appendChild(heightContent)
                size.appendChild(height)  # 把width添加为size的子标签
                # 给size标签创建子标签depth
                depth = xmlBuilder.createElement("depth")  # size子标签width
                depthContent = xmlBuilder.createTextNode(str(depth_img))
                depth.appendChild(depthContent)
                size.appendChild(depth)  # 把width添加为size的子标签
                annotation.appendChild(size)   # 把size添加为annotation的子标签

                # 每一个object中存储的都是['2', '0.506667', '0.553333', '0.490667', '0.658667']一个标注目标
                for object_info in all_objects:
                    # 开始创建标注目标的label信息的标签
                    object = xmlBuilder.createElement("object")  # 创建object标签
                    # 创建label类别标签
                    # 创建name标签
                    imgName = xmlBuilder.createElement("name")  # 创建name标签
                    imgNameContent = xmlBuilder.createTextNode(self.classes[int(object_info[0])])
                    imgName.appendChild(imgNameContent)
                    object.appendChild(imgName)  # 把name添加为object的子标签

                    # 创建pose标签
                    pose = xmlBuilder.createElement("pose")
                    poseContent = xmlBuilder.createTextNode("Unspecified")
                    pose.appendChild(poseContent)
                    object.appendChild(pose)  # 把pose添加为object的标签

                    # 创建truncated标签
                    truncated = xmlBuilder.createElement("truncated")
                    truncatedContent = xmlBuilder.createTextNode("0")
                    truncated.appendChild(truncatedContent)
                    object.appendChild(truncated)

                    # 创建difficult标签
                    difficult = xmlBuilder.createElement("difficult")
                    difficultContent = xmlBuilder.createTextNode("0")
                    difficult.appendChild(difficultContent)
                    object.appendChild(difficult)

                    # 先转换一下坐标
                    # (objx_center, objy_center, obj_width, obj_height)->(xmin,ymin, xmax,ymax)
                    x_center = float(object_info[1])*width_img + 1
                    y_center = float(object_info[2])*height_img + 1
                    xminVal = int(x_center - 0.5*float(object_info[3])*width_img)   # object_info列表中的元素都是字符串类型
                    yminVal = int(y_center - 0.5*float(object_info[4])*height_img)
                    xmaxVal = int(x_center + 0.5*float(object_info[3])*width_img)
                    ymaxVal = int(y_center + 0.5*float(object_info[4])*height_img)



                    # 创建bndbox标签(三级标签)
                    bndbox = xmlBuilder.createElement("bndbox")
                    # 在bndbox标签下再创建四个子标签(xmin,ymin, xmax,ymax) 即标注物体的坐标和宽高信息
                    # 在voc格式中,标注信息:左上角坐标(xmin, ymin) (xmax, ymax)右下角坐标
                    # 1、创建xmin标签
                    xmin = xmlBuilder.createElement("xmin")  # 创建xmin标签(四级标签)
                    xminContent = xmlBuilder.createTextNode(str(xminVal))
                    xmin.appendChild(xminContent)
                    bndbox.appendChild(xmin)
                    # 2、创建ymin标签
                    ymin = xmlBuilder.createElement("ymin")  # 创建ymin标签(四级标签)
                    yminContent = xmlBuilder.createTextNode(str(yminVal))
                    ymin.appendChild(yminContent)
                    bndbox.appendChild(ymin)
                    # 3、创建xmax标签
                    xmax = xmlBuilder.createElement("xmax")  # 创建xmax标签(四级标签)
                    xmaxContent = xmlBuilder.createTextNode(str(xmaxVal))
                    xmax.appendChild(xmaxContent)
                    bndbox.appendChild(xmax)
                    # 4、创建ymax标签
                    ymax = xmlBuilder.createElement("ymax")  # 创建ymax标签(四级标签)
                    ymaxContent = xmlBuilder.createTextNode(str(ymaxVal))
                    ymax.appendChild(ymaxContent)
                    bndbox.appendChild(ymax)

                    object.appendChild(bndbox)
                    annotation.appendChild(object)  # 把object添加为annotation的子标签
                f = open(os.path.join(self.xmls_path, txt_name.split('.')[0]+'.xml'), 'w')
                xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
                f.close()








if __name__ == '__main__':
    txts_path1 = './Annotations_txt'
    xmls_path1 = './Annotations_xml'
    imgs_path1 = './JPEGImages'

    yolo2voc_obj1 = YOLO2VOCConvert(txts_path1, xmls_path1, imgs_path1)
    labels = yolo2voc.search_all_classes()
    print('labels: ', labels)
    yolo2voc_obj1.yolo2voc()

把LabelImg标注的YOLO格式标签转化为VOC格式标签



把LabelImg标注的YOLO格式标签转化为VOC格式标签


把LabelImg标注的YOLO格式标签转化为VOC格式标签
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