yolo数据集制作

1.labelme的json标签转换为yolo的标签： 类别 + 中心+w +h 都是归一化结果

一般数据集下载的时候都可以选择下载格式，yolov5还可以脚本直接生成想要分类个数的网络。
这里主要用于自己标注的数据集

import json
import os

name2id = {'person':0,'helmet':1,'Fire extinguisher':2,'Hook':3,'Gas cylinder':4}

2.归一化数据

def convert(img_size, box):
    dw = 1./(img_size[0])
    dh = 1./(img_size[1])
    x = (box[0] + box[2])/2.0 - 1
    y = (box[1] + box[3])/2.0 - 1
    w = box[2] - box[0]
    h = box[3] - box[1]
    x = x*dw
    w = w*dw
    y = y*dh
    h = h*dh
    return (x,y,w,h)

3. 创建标签

def decode_json(json_floder_path,json_name):
 
txt_name = 'E:\\eclipse-workspace\\PyTorch\\PyTorch-YOLOv3\\data\\custom\\labels\\' + json_name[0:-5] + '.txt'
txt_file = open(txt_name, 'w')

json_path = os.path.join(json_floder_path, json_name)
data = json.load(open(json_path, 'r', encoding='gb2312'))

img_w = data['imageWidth']
img_h = data['imageHeight']

for i in data['shapes']:
    
    label_name = i['label']
    if (i['shape_type'] == 'rectangle'):

        x1 = int(i['points'][0][0])
        y1 = int(i['points'][0][1])
        x2 = int(i['points'][1][0])
        y2 = int(i['points'][1][1])

        bb = (x1,y1,x2,y2)
        bbox = convert((img_w,img_h),bb)
        txt_file.write(str(name2id[label_name]) + " " + " ".join([str(a) for a in bbox]) + '\n')

if __name__ == "__main__":

    json_floder_path = 'G:\\sinopec\\label-data-test\\json'
    json_names = os.listdir(json_floder_path)
    for json_name in json_names:
        decode_json(json_floder_path,json_name)