Yolov5旋转目标检测数据集labelme生成的json转txt

用labelme polygons标出四个点生成的json文件和原图一起放到data文件夹中，同级目录下运行下面的python文件生成txt

 

import os

import numpy as np

import json

from glob import glob

import cv2

import math

from sklearn.model_selection import train_test_split

from os import getcwd


classes = ["0", "1"]#写自己数据集的标签


labelme_path = "data/" #数据集路径


isUseTest = True # 是否创建test集


files = glob(labelme_path + "*.json")

files = [i.replace("\\", "/").split("/")[-1].split(".json")[0] for i in files]

print(files)

if isUseTest:

trainval_files, test_files = train_test_split(files, test_size=0.1, random_state=55)

else:

trainval_files = files


train_files, val_files = train_test_split(trainval_files, test_size=0.1, random_state=55)

contours=[]

rects=[]


def convert(size, box):

dw = 1. / (size[0])

dh = 1. / (size[1])

x1 = ((box[0] + box[1]) / 2.0 - 1)*dw#归一化

y1 = ((box[2] + box[3]) / 2.0 - 1)*dh

for rect in rects:

x, y = rect[0]

width,height=rect[1]

angle=rect[2]

if width 0 else 0

xmax = max(points[:, 0]) if max(points[:, 0]) > 0 else 0

ymin = min(points[:, 1]) if min(points[:, 1]) > 0 else 0

ymax = max(points[:, 1]) if max(points[:, 1]) > 0 else 0

label = multi["label"]


cls_id = classes.index(label)

b = (float(xmin), float(xmax), float(ymin), float(ymax))

bb = convert((width, height), b)

out_file.write( str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')


ChangeToYolo5(train_files, "train")

ChangeToYolo5(val_files, "val")

ChangeToYolo5(test_files, "test")

生成txt格式：x，y，w，h，angle

参考文章链接：Win10 Labelme标注数据转为YOLOV5 训练的数据集_AI浩-CSDN博客_labelme转yolo