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：文章来源：https://www.toymoban.com/news/detail-400586.html
- 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官方开源代码文章来源地址https://www.toymoban.com/news/detail-400586.html
import cv2
import numpy as np
import pandas as pd
import pkg_resources as pkg
import torch
import torchvision
import yaml
def clip_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。
'''
if isinstance(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]) # y2
else: # 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, y2
def xyxy2xywh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(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] # height
return y
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(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 y
return y
def xywhn2xyxy(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() if isinstance(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 y
return y
def xyxy2xywhn(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-right
if clip:
clip_coords(x, (h - eps, w - eps)) # warning: inplace clip
y = x.clone() if isinstance(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 # height
return y
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
# Convert normalized segments into pixel segments, shape (n,2)
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = w * x[:, 0] + padw # top left x
y[:, 1] = h * x[:, 1] + padh # top left y
return y
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