YOLO，VOC数据集标注格式

目录

YOLO，VOC数据集标注格式

一、YOLO数据集标注格式

YOLO数据集txt标注格式：

每个标签有五个数据，依次代表：

• 所标注内容的类别，数字与类别一一对应
• 归一化后中心点的x坐标
• 归一化后中心点的y坐标
• 归一化后目标框的宽度w
• 归一化后目标框的高度h

这里归一化是指除以原始图片的宽和高 

二、VOC数据集标注格式

VOC数据集xml标注格式

<annotation><folder>VOC</folder><filename>bird_1.jpg</filename>    #图片名称以及图片格式
    <size>                             #图片的大小以及是否是rgb图片
        <width>1024</width><height>688</height><depth>3</depth></size><segmented>0</segmented><object>                             #多个目标可以有多个object
        <name>bird</name>                #图片中一个目标的类别
        <pose>Unspecified</pose>         #物体的姿态
        <truncated>0</truncated>         #物体是否被部分遮挡（>15%）
        <difficult>0</difficult>         #是否为难以辨识的物体， 主要指要结体背景才    
                                         #能判断出类别的物体。虽有标注， 但一般忽略这类物体
        <bndbox>                         #xmin和ymin是含目标框左上角的坐标
            <xmin>506</xmin>             #xmax和ymax是含目标框右下角的坐标
            <ymin>170</ymin><xmax>509</xmax><ymax>176</ymax></bndbox></object></annotation>

三、数据集格式转换

转换公式：

• VOC->YOLO

    norm_x=(xmin + xmax)/2/width
    norm_y=(ymin + ymax)/2/height
    norm_w=(xmax - xmin)/width
    norm_h=(ymax - ymin)/height

• YOLO->VOC

    xmin=width *(norm_x -0.5* norm_w)
    ymin=height *(norm_y -0.5* norm_h)

    xmax=width *(norm_x +0.5* norm_w)
    ymax=height *(norm_y +0.5* norm_h)

YOLO v5官方开源代码

import cv2
import numpy as np
import pandas as pd
import pkg_resources as pkg
import torch
import torchvision
import yaml

defclip_coords(boxes, shape):# Clip bounding xyxy bounding boxes to image shape (height, width)'''
    将boxes的坐标(x1y1x2y2左上角右下角)限定在图像的尺寸(img_shapehw)内，防止出界。
    '''ifisinstance(boxes, torch.Tensor):# faster individually
        boxes[:,0].clamp_(0, shape[1])# x1
        boxes[:,1].clamp_(0, shape[0])# y1
        boxes[:,2].clamp_(0, shape[1])# x2
        boxes[:,3].clamp_(0, shape[0])# y2else:# np.array (faster grouped)
        boxes[:,[0,2]]= boxes[:,[0,2]].clip(0, shape[1])# x1, x2
        boxes[:,[1,3]]= boxes[:,[1,3]].clip(0, shape[0])# y1, y2defxyxy2xywh(x):# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
    y = x.clone()ifisinstance(x, torch.Tensor)else np.copy(x)
    y[:,0]=(x[:,0]+ x[:,2])/2# x center
    y[:,1]=(x[:,1]+ x[:,3])/2# y center
    y[:,2]= x[:,2]- x[:,0]# width
    y[:,3]= x[:,3]- x[:,1]# heightreturn y

defxywh2xyxy(x):# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
    y = x.clone()ifisinstance(x, torch.Tensor)else np.copy(x)
    y[:,0]= x[:,0]- x[:,2]/2# top left x
    y[:,1]= x[:,1]- x[:,3]/2# top left y
    y[:,2]= x[:,0]+ x[:,2]/2# bottom right x
    y[:,3]= x[:,1]+ x[:,3]/2# bottom right yreturn y

defxywhn2xyxy(x, w=640, h=640, padw=0, padh=0):# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
    y = x.clone()ifisinstance(x, torch.Tensor)else np.copy(x)
    y[:,0]= w *(x[:,0]- x[:,2]/2)+ padw  # top left x
    y[:,1]= h *(x[:,1]- x[:,3]/2)+ padh  # top left y
    y[:,2]= w *(x[:,0]+ x[:,2]/2)+ padw  # bottom right x
    y[:,3]= h *(x[:,1]+ x[:,3]/2)+ padh  # bottom right yreturn y

defxyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-rightif clip:
        clip_coords(x,(h - eps, w - eps))# warning: inplace clip
    y = x.clone()ifisinstance(x, torch.Tensor)else np.copy(x)
    y[:,0]=((x[:,0]+ x[:,2])/2)/ w  # x center
    y[:,1]=((x[:,1]+ x[:,3])/2)/ h  # y center
    y[:,2]=(x[:,2]- x[:,0])/ w  # width
    y[:,3]=(x[:,3]- x[:,1])/ h  # heightreturn y

defxyn2xy(x, w=640, h=640, padw=0, padh=0):# Convert normalized segments into pixel segments, shape (n,2)
    y = x.clone()ifisinstance(x, torch.Tensor)else np.copy(x)
    y[:,0]= w * x[:,0]+ padw  # top left x
    y[:,1]= h * x[:,1]+ padh  # top left yreturn y