1、YOLO格式转JSON格式
2、 VOC格式转YOLO格式
3、YOLO格式转VOC格式
4、JSON格式转YOLO格式
1、YOLO格式转JSON格式
"""
YOLO转JSON格式
"""
import os
import cv2
import json
import argparse
from tqdm import tqdm
import xml.etree.ElementTree as ET
COCO_DICT=['images','annotations','categories']
IMAGES_DICT=['file_name','height','width','id']
ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id']
CATEGORIES_DICT=['id','name']
#自定义YOLO数据集类别
YOLO_CATEGORIES=['Cahua', 'Crazing']
parser=argparse.ArgumentParser(description='2COCO')
#需修改：图片所在路径
parser.add_argument('--image_path',type=str,default=r'D:\desk\defect_dataset\images\test/',help='config file')
#需修改：标签所在路径
parser.add_argument('--annotation_path',type=str,default=r'D:\desk\defect_dataset\labels\test/',help='config file')
parser.add_argument('--dataset',type=str,default='YOLO',help='config file')
#需修改：JSON文件保存位置
parser.add_argument('--save',type=str,default=r'D:\desk\defect_dataset\JASON/instances_test2017.json',help='config file')
args=parser.parse_args()
def load_json(path):
with open(path,'r') as f:
json_dict=json.load(f)
for i in json_dict:
print(i)
print(json_dict['annotations'])
def save_json(dict,path):
print('SAVE_JSON...')
with open(path,'w') as f:
json.dump(dict,f)
print('SUCCESSFUL_SAVE_JSON:',path)
def load_image(path):
img=cv2.imread(path)
return img.shape[0],img.shape[1]
def generate_categories_dict(category): #ANNOTATIONS_DICT=['image_id','iscrowd','area','bbox','category_id','id']
print('GENERATE_CATEGORIES_DICT...')
return [{CATEGORIES_DICT[0]:category.index(x)+1,CATEGORIES_DICT[1]:x} for x in category] #CATEGORIES_DICT=['id','name']
def generate_images_dict(imagelist,image_path,start_image_id=11725): #IMAGES_DICT=['file_name','height','width','id']
print('GENERATE_IMAGES_DICT...')
images_dict=[]
with tqdm(total=len(imagelist)) as load_bar:
for x in imagelist: #x就是图片的名称
#print(start_image_id)
dict={IMAGES_DICT[0]:x,IMAGES_DICT[1]:load_image(image_path+x)[0],\
IMAGES_DICT[2]:load_image(image_path+x)[1],IMAGES_DICT[3]:imagelist.index(x)+start_image_id}
load_bar.update(1)
images_dict.append(dict)
return images_dict
def YOLO_Dataset(image_path,annotation_path,start_image_id=0,start_id=0):
categories_dict=generate_categories_dict(YOLO_CATEGORIES)
imgname=os.listdir(image_path)
images_dict=generate_images_dict(imgname,image_path)
print('GENERATE_ANNOTATIONS_DICT...')
annotations_dict=[]
id=start_id
for i in images_dict:
image_id=i['id']
image_name=i['file_name']
W,H=i['width'],i['height']
annotation_txt=annotation_path+image_name.split('.')[0]+'.txt'
txt=open(annotation_txt,'r')
lines=txt.readlines()
for j in lines:
category_id=int(j.split(' ')[0])+1
category=YOLO_CATEGORIES
x=float(j.split(' ')[1])
y=float(j.split(' ')[2])
w=float(j.split(' ')[3])
h=float(j.split(' ')[4])
x_min=(x-w/2)*W
y_min=(y-h/2)*H
w=w*W
h=h*H
area=w*h
bbox=[x_min,y_min,w,h]
dict={'image_id':image_id,'iscrowd':0,'area':area,'bbox':bbox,'category_id':category_id,'id':id}
annotations_dict.append(dict)
id=id+1
print('SUCCESSFUL_GENERATE_YOLO_JSON')
return {COCO_DICT[0]:images_dict,COCO_DICT[1]:annotations_dict,COCO_DICT[2]:categories_dict}
if __name__=='__main__':
dataset=args.dataset #数据集名字
save=args.save #json的保存路径
image_path=args.image_path #对于coco是图片的路径
annotation_path=args.annotation_path #coco的annotation路径
if dataset=='YOLO':
json_dict=YOLO_Dataset(image_path,annotation_path,0)
save_json(json_dict,save)
上述代码中仅需要修改以下部分，再运行即可完成YOLO格式转换为JSON格式。
23行 #自定义YOLO数据集类别 YOLO_CATEGORIES=['Cahua', 'Crazing']27行 #需修改：图片所在路径 parser.add_argument('--image_path',type=str,default=r'D:\desk\defect_dataset\images\test/',help='config file')30行 #需修改：标签所在路径 parser.add_argument('--annotation_path',type=str,default=r'D:\desk\defect_dataset\labels\test/',help='config file')35行 parser.add_argument('--save',type=str,default=r'D:\desk\defect_dataset\JASON/instances_test2017.json',help='config file')
2、 VOC格式转YOLO格式
"""
VOC转YOLO格式
"""
import xml.etree.ElementTree as ET
import os, cv2
import numpy as np
from os import listdir
from os.path import join
#需修改，添加数据集的类别
classes = []
def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = (box[0] + box[1]) / 2.0 - 1
y = (box[2] + box[3]) / 2.0 - 1
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(xmlpath, xmlname):
with open(xmlpath, "r", encoding='utf-8') as in_file:
txtname = xmlname[:-4] + '.txt'
txtfile = os.path.join(txtpath, txtname)
tree = ET.parse(in_file)
root = tree.getroot()
filename = root.find('filename')
img = cv2.imdecode(np.fromfile('{}/{}.{}'.format(imgpath, xmlname[:-4], postfix), np.uint8), cv2.IMREAD_COLOR)
h, w = img.shape[:2]
res = []
for obj in root.iter('object'):
cls = obj.find('name').text
if cls not in classes:
classes.append(cls)
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)
res.append(str(cls_id) + " " + " ".join([str(a) for a in bb]))
if len(res) != 0:
with open(txtfile, 'w+') as f:
f.write('\n'.join(res))
if __name__ == "__main__":
#确保图片后缀为统一的jpg格式
postfix = 'jpg'
#需修改，改为自己的图片所在位置
imgpath = r'D:\desk\defect_dataset\IMG'
#需修改，改为自己的VOC标签格式所在位置
xmlpath = r'D:\desk\defect_dataset\VOC'
#需修改，改为自己的想保存YOLO标签格式所在位置
txtpath = r'D:\desk\defect_dataset\TXT'
if not os.path.exists(txtpath):
os.makedirs(txtpath, exist_ok=True)
list = os.listdir(xmlpath)
error_file_list = []
for i in range(0, len(list)):
try:
path = os.path.join(xmlpath, list[i])
if ('.xml' in path) or ('.XML' in path):
convert_annotation(path, list[i])
print(f'file {list[i]} convert success.')
else:
print(f'file {list[i]} is not xml format.')
except Exception as e:
print(f'file {list[i]} convert error.')
print(f'error message:\n{e}')
error_file_list.append(list[i])
print(f'this file convert failure\n{error_file_list}')
print(f'Dataset Classes:{classes}')上述代码中仅需要修改以下部分，再运行即可完成VOC格式转换为YOLO格式
7行 # 需修改，添加数据集的类别 classes = []54行 # 需修改，改为自己的图片所在位置 imgpath = r'D:\desk\defect_dataset\IMG' 57行 # 需修改，改为自己的VOC标签格式所在位置 xmlpath = r'D:\desk\defect_dataset\VOC' 60行 # 需修改，改为自己的想保存YOLO标签格式所在位置 txtpath = r'D:\desk\defect_dataset\TXT'
3、YOLO格式转VOC格式
"""
YOLO转VOC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
#需修改，数据集类别，注意一一对应
dic = {'0': "crazing",
'1': "patches",
'2' :'inclusion',
'3': "pitted_surface",
'4': "rolled-in_scale",
'5': "scratches",
}
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.shape
folder = 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width
widthcontent = 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height
heightcontent = 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depth
depthcontent = 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', encoding='utf-8')
f.close()
if __name__ == "__main__":
# 需修改，图片所在文件夹路径
picPath = r"D:\desk\defect_dataset\IMG/"
# 需修改，YOLO标签所在文件夹路径
txtPath = r"D:\desk\defect_dataset\TXT/"
# 需修改，VOC文件保存路径
xmlPath = r"D:\desk\defect_dataset\VOC/"
makexml(picPath, txtPath, xmlPath)上述代码中仅需要修改以下部分，再运行即可完成YOLO格式转换为VOC格式
11行 #需修改，数据集类别，注意一一对应 dic = {'0': "crazing", '1': "patches", '2' :'inclusion', '3': "pitted_surface", '4': "rolled-in_scale", '5': "scratches", }117行 # 需修改，图片所在文件夹路径 picPath = r"D:\desk\defect_dataset\IMG/" 120行 # 需修改，YOLO标签所在文件夹路径 txtPath = r"D:\desk\defect_dataset\TXT/" 123行 # 需修改，VOC文件保存路径 xmlPath = r"D:\desk\defect_dataset\VOC/"
4、JSON格式转YOLO格式
# COCO 格式的数据集转化为 YOLO 格式的数据集
# --json_path 输入的json文件路径
# --save_path 保存的文件夹名字，默认为当前目录下的labels。
import os
import json
from tqdm import tqdm
import argparse
parser = argparse.ArgumentParser()
# 需修改，这里替换自己的json文件位置
parser.add_argument('--json_path',
default=r'D:\desk\asssssssssssssss\try\json\instances_val2017.json', type=str,
help="input: coco format(json)")
# 需修改，这里输出的txt文件保存位置
parser.add_argument('--save_path', default=r'D:\desk\asssssssssssssss\try\txt', type=str,
help="specify where to save the output dir of labels")
# 需修改，这里设置json标签对应的图片文件位置
parser.add_argument('--img_path', default=r'D:\desk\asssssssssssssss\try\img', type=str,
help="all img path")
arg = parser.parse_args()
def convert(size, box):
dw = 1. / (size[0])
dh = 1. / (size[1])
x = box[0] + box[2] / 2.0
y = box[1] + box[3] / 2.0
w = box[2]
h = box[3]
# round函数确定(xmin, ymin, xmax, ymax)的小数位数
x = round(x * dw, 6)
w = round(w * dw, 6)
y = round(y * dh, 6)
h = round(h * dh, 6)
return (x, y, w, h)
if __name__ == '__main__':
json_file = arg.json_path # COCO Object Instance 类型的标注
ana_txt_save_path = arg.save_path # 保存的路径
data = json.load(open(json_file, 'r'))
if not os.path.exists(ana_txt_save_path):
os.makedirs(ana_txt_save_path)
id_map = {} # coco数据集的id不连续！重新映射一下再输出！
with open(os.path.join(ana_txt_save_path, 'classes.txt'), 'w') as f:
# 写入classes.txt
for i, category in enumerate(data['categories']):
f.write(f"{category['name']}\n")
id_map[category['id']] = i
# print(id_map)
# 这里需要根据自己的需要，更改写入图像相对路径的文件位置。
list_file = open(os.path.join(ana_txt_save_path, 'train2017.txt'), 'w')
for img in tqdm(data['images']):
filename = img["file_name"]
img_width = img["width"]
img_height = img["height"]
img_id = img["id"]
head, tail = os.path.splitext(filename)
ana_txt_name = head + ".txt" # 对应的txt名字，与jpg一致
f_txt = open(os.path.join(ana_txt_save_path, ana_txt_name), 'w')
for ann in data['annotations']:
if ann['image_id'] == img_id:
box = convert((img_width, img_height), ann["bbox"])
f_txt.write("%s %s %s %s %s\n" % (id_map[ann["category_id"]], box[0], box[1], box[2], box[3]))
f_txt.close()
list_file.write(os.path.join(arg.img_path,r'%s.jpg\n') % (head))#修改
list_file.close()上述代码中仅需要修改以下部分，再运行即可完成JSON格式转换为YOLO格式
11行
# 需修改，这里替换自己的json文件位置
parser.add_argument('--json_path',
default=r'D:\desk\asssssssssssssss\try\json\instances_val2017.json', type=str,
help="input: coco format(json)")
16行
# 需修改，这里输出的txt文件保存位置
parser.add_argument('--save_path', default=r'D:\desk\asssssssssssssss\try\txt', type=str,
help="specify where to save the output dir of labels")
20行
# 需修改，这里设置json标签对应的图片文件位置
parser.add_argument('--img_path', default=r'D:\desk\asssssssssssssss\try\img', type=str,
help="all img path")
代码仅用于本人学习使用
