yolo数据集 标签格式转换以及数据集划分
1 xml格式转txt格式
import xml.etree.ElementTree as ET
import pickle
import os
from os import listdir, getcwd
from os.path import join
import glob
classes = ["", "", "", ""] #这里是需要改的第一个地方，写入自己的标签类型，不可多写，不可少写
def convert(size, box):
dw = 1.0 / size[0]
dh = 1.0 / 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)
def convert_annotation(image_name):
in_file = open("D:\\data set\\Pepper_3.0\\test\\xmllabels\\" + image_name[:-3] + 'xml') # 这里是需要改的第二个地方，输入xml文件的路径
out_file = open("D:\\data set\\Pepper_3.0\\test\\labels\\" + image_name[:-3] + 'txt', 'w') # 这里是需要改的第三个地方，输入用于存放转换后的txt文件的路径
f = open("D:\\data set\\Pepper_3.0\\test\\xmllabels\\"+ image_name[:-3] + 'xml')
xml_text = f.read()
root = ET.fromstring(xml_text)
f.close()
size = root.find('size')
w = int(size.find('width').text)
h = int(size.find('height').text)
for obj in root.iter('object'):
cls = obj.find('name').text
if cls not in classes:
print(cls)
continue
cls_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')
wd = getcwd()
if __name__ == '__main__':
for image_path in glob.glob("D:\\data set\\Pepper_3.0\\test\\images\\*.jpg"): # 这里是需要改的最后一个地方，输入图片的路径，每一张图图片都要有一个xml文件对应，没有对应xml文件的图片要删除，当然，这里图片的命名要和对应xml文件的命名一致，之后就可以输出对应命名的txt文件
image_name = image_path.split('\\')[-1]
convert_annotation(image_name)
2 txt格式转xml格式
from xml.dom.minidom import Document
import os
import cv2
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
在自己的标注图片文件夹下建三个子文件夹，分别命名为picture、txt、xml
"""
# 创建字典用来对类型进行转换，要与classes.txt文件中的类对应，且顺序要一致
dic = {'0': "Capsicum anthracnose", '1': "Viral diseases", '2': "Bacterial diseases", '3': "Umbilical rot"}
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") # 注意这里的图片后缀，.jpg/.png
Pheight, Pwidth, Pdepth = img.shape
folder = xmlBuilder.createElement("folder") # folder标签
foldercontent = xmlBuilder.createTextNode("datasetRGB")
folder.appendChild(foldercontent)
annotation.appendChild(folder)
filename = xmlBuilder.createElement("filename") # filename标签
filenamecontent = xmlBuilder.createTextNode(name[0:-4] + ".jpg")
filename.appendChild(filenamecontent)
annotation.appendChild(filename)
size = xmlBuilder.createElement("size") # size标签
width = xmlBuilder.createElement("width") # size子标签width
widthcontent = xmlBuilder.createTextNode(str(Pwidth))
width.appendChild(widthcontent)
size.appendChild(width)
height = xmlBuilder.createElement("height") # size子标签height
heightcontent = xmlBuilder.createTextNode(str(Pheight))
height.appendChild(heightcontent)
size.appendChild(height)
depth = xmlBuilder.createElement("depth") # size子标签depth
depthcontent = xmlBuilder.createTextNode(str(Pdepth))
depth.appendChild(depthcontent)
size.appendChild(depth)
annotation.appendChild(size)
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)
pose = xmlBuilder.createElement("pose") # pose标签
posecontent = xmlBuilder.createTextNode("Unspecified")
pose.appendChild(posecontent)
object.appendChild(pose)
truncated = xmlBuilder.createElement("truncated") # truncated标签
truncatedContent = xmlBuilder.createTextNode("0")
truncated.appendChild(truncatedContent)
object.appendChild(truncated)
difficult = xmlBuilder.createElement("difficult") # difficult标签
difficultcontent = xmlBuilder.createTextNode("0")
difficult.appendChild(difficultcontent)
object.appendChild(difficult)
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)
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)
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)
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)
object.appendChild(bndbox) # bndbox标签结束
annotation.appendChild(object)
f = open(xmlPath + name[0:-4] + ".xml", 'w')
xmlBuilder.writexml(f, indent='\t', newl='\n', addindent='\t', encoding='utf-8')
f.close()
if __name__ == "__main__":
picPath = "D:\\data set\\d1_2.0\\d1_2.0\\" # 图片所在文件夹路径，后面的\\一定要带上
txtPath = "D:\\data set\\d1_2.0\\d1_2.0txt\\" # txt所在文件夹路径，后面的\\一定要带上
xmlPath = "D:\\data set\\d1_2.0\\d1_2.0xml\\" # xml文件保存路径，后面的\\一定要带上
makexml(picPath, txtPath, xmlPath)
3 数据集划分训练集验证集及测试集
import os
import shutil
import random
random.seed(0)
def split_data(file_path,xml_path, new_file_path, train_rate, val_rate, test_rate):
each_class_image = []
each_class_label = []
for image in os.listdir(file_path):
each_class_image.append(image)
for label in os.listdir(xml_path):
each_class_label.append(label)
data=list(zip(each_class_image,each_class_label))
total = len(each_class_image)
random.shuffle(data)
each_class_image,each_class_label=zip(*data)
train_images = each_class_image[0:int(train_rate * total)]
val_images = each_class_image[int(train_rate * total):int((train_rate + val_rate) * total)]
test_images = each_class_image[int((train_rate + val_rate) * total):]
train_labels = each_class_label[0:int(train_rate * total)]
val_labels = each_class_label[int(train_rate * total):int((train_rate + val_rate) * total)]
test_labels = each_class_label[int((train_rate + val_rate) * total):]
for image in train_images:
print(image)
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'train' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in train_labels:
print(label)
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'train' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in val_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'val' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in val_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'val' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
for image in test_images:
old_path = file_path + '/' + image
new_path1 = new_file_path + '/' + 'test' + '/' + 'images'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + image
shutil.copy(old_path, new_path)
for label in test_labels:
old_path = xml_path + '/' + label
new_path1 = new_file_path + '/' + 'test' + '/' + 'labels'
if not os.path.exists(new_path1):
os.makedirs(new_path1)
new_path = new_path1 + '/' + label
shutil.copy(old_path, new_path)
if __name__ == '__main__':
file_path = "D:/data set/Pepper_data/image"
xml_path = "D:/data set/Pepper_data/label"
new_file_path = "D:/data set/Pepper_3.0"
split_data(file_path, xml_path, new_file_path, train_rate=0.6, val_rate=0.2, test_rate=0.2)