Contents

• 1. LabelImg Install
• • 1) dataset file
  • 2) Implement
  • 3) labelimg Introduction
  • 4) Configuration
  • 5) Hot key
• 2. Format Conversion Code
• 3. Split Data Code
• Conclusion

1. LabelImg Install

打开Anaconda终端，输入即可安装完成：

pip install labelimg -i https://pypi.tuna.tsinghua.edu.cn/simple

1) dataset file

准备一个** data **的根目录文件夹，文件夹格式如下：

data-
--images #装有你待标注图像的子文件夹
--labels #等会需要保存标注txt文件的子文件夹
--class.txt #里面保存有你的标注类别

class.txt格式如下,这是代表有四类文件：

red
gray
blue
other

2) Implement

使用anaconda终端 ，依次输入:

cd  [data文件夹所在路径]

labelimg images class.txt #即labelimg  [待标注图像路径]  [类别txt文件]

即可用labelimg标注工具打开待标注图像

3) labelimg Introduction

Open Dir：待标注图片数据的路径文件夹，即选择images文件夹
Change Save Dir：保存类别标签的路径文件夹，即选择labels文件夹
YOLO：标注的标签保存成YOLO格式，在鼠标点一下就变成PascalVOC，即此时就会把标注的标签变成VOC格式

4) Configuration

如下图，选中这个选项即可：
Auto Save mode：当你切换到下一张图片时，就会自动把上一张标注的图片标签自动保存下来，这样就不用每标注一样图片都按Ctrl+S保存一下了
Display Labels：标注好图片之后，会把框和标签都显示出来
Advanced Mode：这样标注的十字架就会一直悬浮在窗口，不用每次标完一个目标，再按一次W快捷键，调出标注的十字架。

5) Hot key

W：调出标注的十字架，开始标注
A：切换到上一张图片
D：切换到下一张图片
Ctrl+S：保存标注好的标签
del：删除标注的矩形框
Ctrl+鼠标滚轮：按住Ctrl，然后滚动鼠标滚轮，可以调整标注图片的显示大小
Ctrl+u：选择要标注图片的文件夹
Ctrl+r：选择标注好的label标签存放的文件夹
↑→↓←：移动标注的矩形框的位置

代码如下（示例）：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import  ssl
ssl._create_default_https_context = ssl._create_unverified_context

2. Format Conversion Code

YOLO转化为VOC格式完整代码如下，只需要在终端改成自己的路径即可：

import os
import cv2
import json
from tqdm import tqdm
from sklearn.model_selection import train_test_split
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', default='./data', type=str,
                    help="root path of images and labels, include ./images and ./labels and classes.txt")
parser.add_argument('--save_path', type=str, default='./test.json',
                    help="if not split the dataset, give a path to a json file")
parser.add_argument('--random_split', action='store_true', help="random split the dataset, default ratio is 8:1:1")
parser.add_argument('--split_by_file', action='store_true',
                    help="define how to split the dataset, include ./train.txt ./val.txt ./test.txt ")

arg = parser.parse_args()


def train_test_val_split_random(img_paths, ratio_train=0.8, ratio_test=0.1, ratio_val=0.1):
    # 这里可以修改数据集划分的比例。
    assert int(ratio_train + ratio_test + ratio_val) == 1
    train_img, middle_img = train_test_split(img_paths, test_size=1 - ratio_train, random_state=233)
    ratio = ratio_val / (1 - ratio_train)
    val_img, test_img = train_test_split(middle_img, test_size=ratio, random_state=233)
    print("NUMS of train:val:test = {}:{}:{}".format(len(train_img), len(val_img), len(test_img)))
    return train_img, val_img, test_img


def train_test_val_split_by_files(img_paths, root_dir):
    # 根据文件 train.txt, val.txt, test.txt（里面写的都是对应集合的图片名字） 来定义训练集、验证集和测试集
    phases = ['train', 'val', 'test']
    img_split = []
    for p in phases:
        define_path = os.path.join(root_dir, f'{p}.txt')
        print(f'Read {p} dataset definition from {define_path}')
        assert os.path.exists(define_path)
        with open(define_path, 'r') as f:
            img_paths = f.readlines()
            # img_paths = [os.path.split(img_path.strip())[1] for img_path in img_paths]  # NOTE 取消这句备注可以读取绝对地址。
            img_split.append(img_paths)
    return img_split[0], img_split[1], img_split[2]


def yolo2coco(arg):
    root_path = arg.root_dir
    print("Loading data from ", root_path)

    assert os.path.exists(root_path)
    originLabelsDir = os.path.join(root_path, 'labels')
    originImagesDir = os.path.join(root_path, 'images')
    with open(os.path.join(root_path, 'classes.txt')) as f:
        classes = f.read().strip().split()
    # images dir name
    indexes = os.listdir(originImagesDir)

    if arg.random_split or arg.split_by_file:
        # 用于保存所有数据的图片信息和标注信息
        train_dataset = {'categories': [], 'annotations': [], 'images': []}
        val_dataset = {'categories': [], 'annotations': [], 'images': []}
        test_dataset = {'categories': [], 'annotations': [], 'images': []}

        # 建立类别标签和数字id的对应关系, 类别id从0开始。
        for i, cls in enumerate(classes, 0):
            train_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})
            val_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})
            test_dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})

        if arg.random_split:
            print("spliting mode: random split")
            train_img, val_img, test_img = train_test_val_split_random(indexes, 0.8, 0.1, 0.1)
        elif arg.split_by_file:
            print("spliting mode: split by files")
            train_img, val_img, test_img = train_test_val_split_by_files(indexes, root_path)
    else:
        dataset = {'categories': [], 'annotations': [], 'images': []}
        for i, cls in enumerate(classes, 0):
            dataset['categories'].append({'id': i, 'name': cls, 'supercategory': 'mark'})

    # 标注的id
    ann_id_cnt = 0
    for k, index in enumerate(tqdm(indexes)):
        # 支持 png jpg 格式的图片。
        txtFile = index.replace('images', 'txt').replace('.jpg', '.txt').replace('.png', '.txt')
        # 读取图像的宽和高
        im = cv2.imread(os.path.join(root_path, 'images/') + index)
        height, width, _ = im.shape
        if arg.random_split or arg.split_by_file:
            # 切换dataset的引用对象，从而划分数据集
            if index in train_img:
                dataset = train_dataset
            elif index in val_img:
                dataset = val_dataset
            elif index in test_img:
                dataset = test_dataset
        # 添加图像的信息
        dataset['images'].append({'file_name': index,
                                  'id': k,
                                  'width': width,
                                  'height': height})
        if not os.path.exists(os.path.join(originLabelsDir, txtFile)):
            # 如没标签，跳过，只保留图片信息。
            continue
        with open(os.path.join(originLabelsDir, txtFile), 'r') as fr:
            labelList = fr.readlines()
            for label in labelList:
                label = label.strip().split()
                x = float(label[1])
                y = float(label[2])
                w = float(label[3])
                h = float(label[4])

                # convert x,y,w,h to x1,y1,x2,y2
                H, W, _ = im.shape
                x1 = (x - w / 2) * W
                y1 = (y - h / 2) * H
                x2 = (x + w / 2) * W
                y2 = (y + h / 2) * H
                # 标签序号从0开始计算, coco2017数据集标号混乱，不管它了。
                cls_id = int(label[0])
                width = max(0, x2 - x1)
                height = max(0, y2 - y1)
                dataset['annotations'].append({
                    'area': width * height,
                    'bbox': [x1, y1, width, height],
                    'category_id': cls_id,
                    'id': ann_id_cnt,
                    'image_id': k,
                    'iscrowd': 0,
                    # mask, 矩形是从左上角点按顺时针的四个顶点
                    'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]]
                })
                ann_id_cnt += 1

    # 保存结果
    folder = os.path.join(root_path, 'annotations')
    if not os.path.exists(folder):
        os.makedirs(folder)
    if arg.random_split or arg.split_by_file:
        for phase in ['train', 'val', 'test']:
            json_name = os.path.join(root_path, 'annotations/{}.json'.format(phase))
            with open(json_name, 'w') as f:
                if phase == 'train':
                    json.dump(train_dataset, f)
                elif phase == 'val':
                    json.dump(val_dataset, f)
                elif phase == 'test':
                    json.dump(test_dataset, f)
            print('Save annotation to {}'.format(json_name))
    else:
        json_name = os.path.join(root_path, 'annotations/{}'.format(arg.save_path))
        with open(json_name, 'w') as f:
            json.dump(dataset, f)
            print('Save annotation to {}'.format(json_name))


if __name__ == "__main__":
    yolo2coco(arg)

3. Split Data Code

路径修改：需要把该代码的python文件和上面的data文件夹放在一起

将下方代码中label_txt_path 的data改成自己的data文件夹名字，即跟随我设置的data标注文件夹名，或者你自己设置的文件夹名。
str 是linux系统设置’/’，windows系统设置为’\\’
其它的自行看代码

str = '/'
#图像路径
label_txt_path = str + "data3"：

import os
import random

def data_split(full_list, ratio, shuffle=False):
    """
    数据集拆分: 将列表full_list按比例ratio（随机）划分为2个子列表sublist_1与sublist_2
    """
    n_total = len(full_list)
    offset = int(n_total * ratio)
    if n_total == 0 or offset < 1:
        return [], full_list
    if shuffle:
        random.shuffle(full_list)
    train = full_list[:offset]
    test = full_list[offset:]
    return train, test
def get_path(path,path_ImageSets,str):
    f_train=open(path + str +"train.txt",'w+')
    f_val=open(path + str+"val.txt",'w+')
    f_test=open(path + str+"test.txt",'w+')

    f_train_ImageSets=open(path_ImageSets + str +"train.txt",'w+')
    f_val_ImageSets=open(path_ImageSets + str +"val.txt",'w+')
    f_test_ImageSets=open(path_ImageSets + str +"test.txt",'w+')

    for i in train:
        filepath = img_filedir + str +i +  b
        dir = filepath + '\n'
        #print(dir)
        dir_ImageSets = i + '\n'
        #print(dir_ImageSets)
        # 把dir写到.txt文本里面
        f_train.writelines(dir)
        f_train_ImageSets.writelines(dir_ImageSets)
        
    for j in test:
        filepath = img_filedir + str + j +  b
        dir = filepath + '\n'
        #print(dir)
        # 把dir写到.txt文本里面
        dir_ImageSets = j + '\n'
        #print(dir_ImageSets)
        f_val.writelines(dir)
        f_val_ImageSets.writelines(dir_ImageSets)
        f_test.writelines(dir)
        f_test_ImageSets.writelines(dir_ImageSets)
    f_train.close()
    f_train_ImageSets.close()
    f_val.close()
    f_val_ImageSets.close()
    f_test.close()
    f_test_ImageSets.close()

str = '/'
#图像路径
label_txt_path = str + "data3"
images_path = label_txt_path + str+ "images"
images_name_path = label_txt_path + str + "ImageSets"
#train_test 划分比例
ratio = 0.1
#train_test 是否随机划分
shuffle = False

#用getcwd()获取当前目录
filedir = os.getcwd()
print(filedir)
img_filedir = filedir + images_path

#获取目录列表
filenames=os.listdir(img_filedir)

#for循环
list = []
for filename in filenames:
    a,b = os.path.splitext(filename)
    #a = int(a)
    list.append(a)
list.sort()
#print(list)

train,test = data_split(list,ratio= ratio,shuffle = shuffle)
#print('train=',train)
#print('test=',test)
#以写的方式打开一个文本
path = filedir + label_txt_path
path_ImageSets = filedir + images_name_path

get_path(path,path_ImageSets,str)

Conclusion

暂时没有

标签：img,voc,yolo,train,split,test,path,txt,标注
来源： https://blog.csdn.net/m0_47646169/article/details/121587131