温州网络公司网站建设,网站续费 多久,网站建设中企,网站如何做延迟加载在深度学习中#xff0c;第一步要做的往往就是处理数据集,尤其是学习百度飞桨PaddlePaddle的小伙伴#xff0c;数据集经常要用Voc格式的#xff0c;比如性能突出的ppyolo等模型。所以学会数据集转化的本领是十分必要的。这篇博客就带你一起进行Yolo与Voc格式的相互转化… 在深度学习中第一步要做的往往就是处理数据集,尤其是学习百度飞桨PaddlePaddle的小伙伴数据集经常要用Voc格式的比如性能突出的ppyolo等模型。所以学会数据集转化的本领是十分必要的。这篇博客就带你一起进行Yolo与Voc格式的相互转化附详细代码 文章目录YOLO数据集介绍VOC数据集介绍Yolo转VOCVOC转Yolofrom lxml import etreeclasses[ball]YOLO数据集介绍
Yolo数据集主要是txt文件一般包括train文件夹和val文件夹每一个文件夹下有与图片同名的txt文件基本结构如下 |–image
||–train||–val
|–label
||–train||–val
txt的标签如下图所示 第一列为目标类别后面四个数字为方框左上角与右下角的坐标可以看到都是小于1的数字是因为对应的整张图片的比例所以就算图像被拉伸放缩这种txt格式的标签也可以找到相应的目标。
VOC数据集介绍
VOC格式数据集一般有着如下的目录结构
VOC_ROOT #根目录├── JPEGImages # 存放源图片│ ├── aaaa.jpg │ ├── bbbb.jpg │ └── cccc.jpg├── Annotations # 存放xml文件与JPEGImages中的图片一一对应解释图片的内容等等│ ├── aaaa.xml │ ├── bbbb.xml │ └── cccc.xml └── ImageSets └── Main├── train.txt # txt文件中每一行包含一个图片的名称└── val.txt
其中JPEGImages目录中存放的是源图片的数据(当然图片并不一定要是.jpg格式的只是规定文件夹名字叫JPEGImages)Annotations目录中存放的是标注数据VOC的标注是xml格式的文件名与JPEGImages中的图片一一对应。 重点看下xml格式的标注格式
annotationfolderVOC_ROOT/folder filenameaaaa.jpg/filename # 文件名size # 图像尺寸长宽以及通道数 width500/widthheight332/heightdepth3/depth/sizesegmented1/segmented # 是否用于分割在图像物体识别中无所谓object # 检测到的物体namehorse/name # 物体类别poseUnspecified/pose # 拍摄角度如果是自己的数据集就Unspecifiedtruncated0/truncated # 是否被截断0表示完整)difficult0/difficult # 目标是否难以识别0表示容易识别bndbox # bounding-box包含左下角和右上角xy坐标xmin100/xminymin96/yminxmax355/xmaxymax324/ymax/bndbox/objectobject # 检测到多个物体nameperson/nameposeUnspecified/posetruncated0/truncateddifficult0/difficultbndboxxmin198/xminymin58/yminxmax286/xmaxymax197/ymax/bndbox/object
/annotationYolo转VOC
文件结构如下
Yolo转VOC #根目录├── dataset │ ├── Annotations │ ├── image └──image图像│ └── label└──txt文件├── Yolo转VOC.py # 代码文件
具体代码:
#!/usr/bin/env python
# -*- coding: UTF-8 -*-Project Yolo与VOC转化
File Yolo转Voc.py
IDE PyCharm
Author 咋
Date 2023/3/6 16:45 from xml.dom.minidom import Document
import os
import cv2# def makexml(txtPath, xmlPath, picPath): # txt所在文件夹路径xml文件保存路径图片所在文件夹路径
def makexml(picPath, txtPath, xmlPath): # txt所在文件夹路径xml文件保存路径图片所在文件夹路径此函数用于将yolo格式txt标注文件转换为voc格式xml标注文件dic {0: blue, # 创建字典用来对类型进行转换1: red, # 此处的字典要与自己的classes.txt文件中的类对应且顺序要一致}files os.listdir(txtPath)for i, name in enumerate(files):xmlBuilder Document()annotation xmlBuilder.createElement(annotation) # 创建annotation标签xmlBuilder.appendChild(annotation)txtFile open(txtPath name)txtList txtFile.readlines()img cv2.imread(picPath name[0:-4] .jpg)Pheight, Pwidth, Pdepth img.shapefolder xmlBuilder.createElement(folder) # folder标签foldercontent xmlBuilder.createTextNode(driving_annotation_dataset)folder.appendChild(foldercontent)annotation.appendChild(folder) # folder标签结束filename xmlBuilder.createElement(filename) # filename标签filenamecontent xmlBuilder.createTextNode(name[0:-4] .jpg)filename.appendChild(filenamecontent)annotation.appendChild(filename) # filename标签结束size xmlBuilder.createElement(size) # size标签width xmlBuilder.createElement(width) # size子标签widthwidthcontent xmlBuilder.createTextNode(str(Pwidth))width.appendChild(widthcontent)size.appendChild(width) # size子标签width结束height xmlBuilder.createElement(height) # size子标签heightheightcontent xmlBuilder.createTextNode(str(Pheight))height.appendChild(heightcontent)size.appendChild(height) # size子标签height结束depth xmlBuilder.createElement(depth) # size子标签depthdepthcontent xmlBuilder.createTextNode(str(Pdepth))depth.appendChild(depthcontent)size.appendChild(depth) # size子标签depth结束annotation.appendChild(size) # size标签结束for j in txtList:oneline j.strip().split( )object xmlBuilder.createElement(object) # object 标签picname xmlBuilder.createElement(name) # name标签namecontent xmlBuilder.createTextNode(dic[oneline[0]])picname.appendChild(namecontent)object.appendChild(picname) # name标签结束pose xmlBuilder.createElement(pose) # pose标签posecontent xmlBuilder.createTextNode(Unspecified)pose.appendChild(posecontent)object.appendChild(pose) # pose标签结束truncated xmlBuilder.createElement(truncated) # truncated标签truncatedContent xmlBuilder.createTextNode(0)truncated.appendChild(truncatedContent)object.appendChild(truncated) # truncated标签结束difficult xmlBuilder.createElement(difficult) # difficult标签difficultcontent xmlBuilder.createTextNode(0)difficult.appendChild(difficultcontent)object.appendChild(difficult) # difficult标签结束bndbox xmlBuilder.createElement(bndbox) # bndbox标签xmin xmlBuilder.createElement(xmin) # xmin标签mathData int(((float(oneline[1])) * Pwidth 1) - (float(oneline[3])) * 0.5 * Pwidth)xminContent xmlBuilder.createTextNode(str(mathData))xmin.appendChild(xminContent)bndbox.appendChild(xmin) # xmin标签结束ymin xmlBuilder.createElement(ymin) # ymin标签mathData int(((float(oneline[2])) * Pheight 1) - (float(oneline[4])) * 0.5 * Pheight)yminContent xmlBuilder.createTextNode(str(mathData))ymin.appendChild(yminContent)bndbox.appendChild(ymin) # ymin标签结束xmax xmlBuilder.createElement(xmax) # xmax标签mathData int(((float(oneline[1])) * Pwidth 1) (float(oneline[3])) * 0.5 * Pwidth)xmaxContent xmlBuilder.createTextNode(str(mathData))xmax.appendChild(xmaxContent)bndbox.appendChild(xmax) # xmax标签结束ymax xmlBuilder.createElement(ymax) # ymax标签mathData int(((float(oneline[2])) * Pheight 1) (float(oneline[4])) * 0.5 * Pheight)ymaxContent xmlBuilder.createTextNode(str(mathData))ymax.appendChild(ymaxContent)bndbox.appendChild(ymax) # ymax标签结束object.appendChild(bndbox) # bndbox标签结束annotation.appendChild(object) # object标签结束f open(xmlPath name[0:-4] .xml, w)xmlBuilder.writexml(f, indent\t, newl\n, addindent\t, encodingutf-8)f.close()if __name__ __main__:picPath dataset/JPEGImages/ # 图片所在文件夹路径后面的/一定要带上txtPath dataset/YOLOLables/ # txt所在文件夹路径后面的/一定要带上xmlPath dataset/annotations/ # xml文件保存路径后面的/一定要带上makexml(picPath, txtPath, xmlPath)
VOC转Yolo
相当于上述操作的逆运算这里直接给出代码 import xml.etree.ElementTree as ET import pickle import os from os import listdir, getcwd from os.path import join import random from shutil import copyfile
from lxml import etree
#自己的类别 classes [“0”, “1”,‘2’,‘3’,‘person’]
classes[“ball”]
TRAIN_RATIO 80 #训练集比例
def clear_hidden_files(path): dir_list os.listdir(path) for i in dir_list: abspath os.path.join(os.path.abspath(path), i) if os.path.isfile(abspath): if i.startswith(“._”): os.remove(abspath) else: clear_hidden_files(abspath)
#数据转换 def convert(size, box): dw 1. / size[0] dh 1. / size[1] x (box[0] box[1]) / 2.0 y (box[2] box[3]) / 2.0 w box[1] - box[0] h box[3] - box[2] x x * dw w w * dw y y * dh h h * dh return (x, y, w, h)
#编写格式 def convert_annotation(image_id): in_file open(‘./dataset/annotations/%s.xml’ % image_id) out_file open(‘./dataset/YOLOLabels/%s.txt’ % image_id, ‘w’) tree ET.parse(in_file) root tree.getroot() size root.find(‘size’) w int(size.find(‘width’).text) h int(size.find(‘height’).text)
for obj in root.iter(object):difficult obj.find(difficult).textcls obj.find(name).textif cls not in classes or int(difficult) 1:continuecls_id classes.index(cls)xmlbox obj.find(bndbox)b (float(xmlbox.find(xmin).text), float(xmlbox.find(xmax).text), float(xmlbox.find(ymin).text),float(xmlbox.find(ymax).text))bb convert((w, h), b)out_file.write(str(cls_id) .join([str(a) for a in bb]) \n)
in_file.close()
out_file.close()#创建上述目录结构 wd os.getcwd()
work_sapce_dir os.path.join(wd, “dataset/”) if not os.path.isdir(work_sapce_dir): os.mkdir(work_sapce_dir) annotation_dir os.path.join(work_sapce_dir, “annotations/”) if not os.path.isdir(annotation_dir): os.mkdir(annotation_dir) clear_hidden_files(annotation_dir) image_dir os.path.join(work_sapce_dir, “JPEGImages/”) if not os.path.isdir(image_dir): os.mkdir(image_dir) clear_hidden_files(image_dir) yolo_labels_dir os.path.join(work_sapce_dir, “YOLOLabels/”) if not os.path.isdir(yolo_labels_dir): os.mkdir(yolo_labels_dir) clear_hidden_files(yolo_labels_dir) yolov5_images_dir os.path.join(work_sapce_dir, “images/”) if not os.path.isdir(yolov5_images_dir): os.mkdir(yolov5_images_dir) clear_hidden_files(yolov5_images_dir) yolov5_labels_dir os.path.join(work_sapce_dir, “labels/”) if not os.path.isdir(yolov5_labels_dir): os.mkdir(yolov5_labels_dir) clear_hidden_files(yolov5_labels_dir) yolov5_images_train_dir os.path.join(yolov5_images_dir, “train/”) if not os.path.isdir(yolov5_images_train_dir): os.mkdir(yolov5_images_train_dir) clear_hidden_files(yolov5_images_train_dir) yolov5_images_test_dir os.path.join(yolov5_images_dir, “val/”) if not os.path.isdir(yolov5_images_test_dir): os.mkdir(yolov5_images_test_dir) clear_hidden_files(yolov5_images_test_dir) yolov5_labels_train_dir os.path.join(yolov5_labels_dir, “train/”) if not os.path.isdir(yolov5_labels_train_dir): os.mkdir(yolov5_labels_train_dir) clear_hidden_files(yolov5_labels_train_dir) yolov5_labels_test_dir os.path.join(yolov5_labels_dir, “val/”) if not os.path.isdir(yolov5_labels_test_dir): os.mkdir(yolov5_labels_test_dir) clear_hidden_files(yolov5_labels_test_dir)
#创建两个记录照片名字的文件 train_file open(os.path.join(yolov5_images_dir, “yolov5_train.txt”), ‘w’) test_file open(os.path.join(yolov5_images_dir, “yolov5_val.txt”), ‘w’) train_file.close() test_file.close() train_file open(os.path.join(yolov5_images_dir, “yolov5_train.txt”), ‘a’) test_file open(os.path.join(yolov5_images_dir, “yolov5_val.txt”), ‘a’)
#随机划分 list_imgs os.listdir(image_dir) # list image files prob random.randint(1, 100) print(“Probability: %d” % prob) for i in range(0, len(list_imgs)): path os.path.join(image_dir, list_imgs[i]) if os.path.isfile(path): image_path image_dir list_imgs[i] voc_path list_imgs[i] (nameWithoutExtention, extention) os.path.splitext(os.path.basename(image_path)) (voc_nameWithoutExtention, voc_extention) os.path.splitext(os.path.basename(voc_path)) annotation_name nameWithoutExtention ‘.xml’ annotation_path os.path.join(annotation_dir, annotation_name) label_name nameWithoutExtention ‘.txt’ label_path os.path.join(yolo_labels_dir, label_name) prob random.randint(1, 100) print(“Probability: %d” % prob) if (prob TRAIN_RATIO): # train dataset if os.path.exists(annotation_path): train_file.write(image_path ‘\n’) convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_train_dir voc_path) copyfile(label_path, yolov5_labels_train_dir label_name) else: # test dataset if os.path.exists(annotation_path): test_file.write(image_path ‘\n’) convert_annotation(nameWithoutExtention) # convert label copyfile(image_path, yolov5_images_test_dir voc_path) copyfile(label_path, yolov5_labels_test_dir label_name) train_file.close() test_file.close()
