Win10 Labelme标注数据转为YOLOV5 训练的数据集

• 将Labelme标注的数据复制到工程的根目录，并将其命名为LabelmeData。我的工程根目录是yolov5-master，如下图：

• 打开工程，在根目录新建LabelmeToYolov5.py。写入下面的代码

import os
import numpy as np
import json
from glob import glob
import cv2
from sklearn.model_selection import train_test_split
from os import getcwd

classes = ["aircraft", "oiltank"]
# 1.标签路径
labelme_path = "LabelmeData/"
isUseTest = True  # 是否创建test集
# 3.获取待处理文件
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
# split
train_files, val_files = train_test_split(trainval_files, test_size=0.1, random_state=55)


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)


wd = getcwd()
print(wd)


def ChangeToYolo5(files, txt_Name):
    if not os.path.exists('tmp/'):
        os.makedirs('tmp/')
    list_file = open('tmp/%s.txt' % (txt_Name), 'w')
    for json_file_ in files:
        json_filename = labelme_path + json_file_ + ".json"
        imagePath = labelme_path + json_file_ + ".jpg"
        list_file.write('%s/%s\n' % (wd, imagePath))
        out_file = open('%s/%s.txt' % (labelme_path, json_file_), 'w')
        json_file = json.load(open(json_filename, "r", encoding="utf-8"))
        height, width, channels = cv2.imread(labelme_path + json_file_ + ".jpg").shape
        for multi in json_file["shapes"]:
            points = np.array(multi["points"])
            xmin = min(points[:, 0]) if min(points[:, 0]) > 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"]
            if xmax <= xmin:
                pass
            elif ymax <= ymin:
                pass
            else:
                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')
                print(json_filename, xmin, ymin, xmax, ymax, cls_id)


ChangeToYolo5(train_files, "train")
ChangeToYolo5(val_files, "val")
ChangeToYolo5(test_files, "test")
'''
file1 = open("tmp/train.txt", "r")
file2 = open("tmp/val.txt", "r")
file_list1 = file1.readlines()  # 将所有变量读入列表file_list1

file_list2 = file2.readlines()  # 将所有变量读入列表file_list2
file3 = open("tmp/trainval.txt", "w")
for line in file_list1:
    print(line)
    file3.write(line)
for line in file_list2:
    print(line)
    file3.write(line)
''' 

这段代码执行完成会在LabelmeData生成每个图片的txt标注数据，同时在tmp文件夹下面生成训练集、验证集和测试集的txt，txt记录的是图片的路径，为下一步生成YoloV5训练和测试用的数据集做准备。

• 在tmp文件夹新建makedata.py。执行完成后会在工程的根目录生成VOC数据集。

import shutil
import os

file_List = ["train", "val", "test"]
for file in file_List:
    if not os.path.exists('../VOC/images/%s' % file):
        os.makedirs('../VOC/images/%s' % file)
    if not os.path.exists('../VOC/labels/%s' % file):
        os.makedirs('../VOC/labels/%s' % file)
    print(os.path.exists('../tmp/%s.txt' % file))
    f = open('../tmp/%s.txt' % file, 'r')
    lines = f.readlines()
    for line in lines:
        print(line)
        line = "/".join(line.split('/')[-5:]).strip()
        shutil.copy(line, "../VOC/images/%s" % file)
        line = line.replace('jpg', 'txt')
        shutil.copy(line, "../VOC/labels/%s/" % file) 

运行结果如下：

本文地址：https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/108865894