1、环境

anaconda环境安装配置

2、工具

安装labelme工具

3、安装软件

3.1、打开anaconda控制台

在这里插入图片描述

3.2、创建虚拟环境

conda create -n labelme python=3.7

3.3、激活环境

conda activate labelme

3.4、下载labelme

pip install labelme

3.5、输入labelme打开软件

以后打开跳过3.2和3.4打开即可

labelme

在这里插入图片描述

4、制作labelme数据集

4.1、打开文件夹

存有多张图片的文件夹
图片为统一格式（比如都为.png或者.jpg）

在这里插入图片描述

4.2、创建矩形框

在这里插入图片描述

4.3、label名称

为框选住的类别起一个名字

在这里插入图片描述
再次框选的时候会保存已经存在的label

在这里插入图片描述

4.4、保存

在这里插入图片描述

保存名字和图片在同一个路径，同样的名字

在这里插入图片描述

4.5、结果

点击下一个继续标注label
在这里插入图片描述

把需要的图片全部做标签保存
在这里插入图片描述

5、转换coco数据

5.1、创建目录

dataset中放第4步制作好的数据集

├─data-labelme
│  ├─coco
│  │  ├─annotations
│  │  ├─train2017
│  │  └─val2017
│  ├─dataset
├─json2coco.py

5.2、运行文件

然后执行json2coco.py文件
将代码中标有修改的注释下面代码进行替换

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

np.random.seed(41)

# 修改1->改成自己的类别
classname_to_id = {
    "green": 0,  
    "purple": 1,
    "yellow": 2
}


class Lableme2CoCo:

    def __init__(self):
        self.images = []
        self.annotations = []
        self.categories = []
        self.img_id = 0
        self.ann_id = 0

    def save_coco_json(self, instance, save_path):
        json.dump(instance, open(save_path, 'w', encoding='utf-8'), ensure_ascii=False, indent=1)  # indent=2 更加美观显示

    # 由json文件构建COCO
    def to_coco(self, json_path_list):
        self._init_categories()
        for json_path in json_path_list:
            obj = self.read_jsonfile(json_path)
            self.images.append(self._image(obj, json_path))
            shapes = obj['shapes']
            for shape in shapes:
                annotation = self._annotation(shape)
                self.annotations.append(annotation)
                self.ann_id += 1
            self.img_id += 1
        instance = {}
        instance['info'] = 'spytensor created'
        instance['license'] = ['license']
        instance['images'] = self.images
        instance['annotations'] = self.annotations
        instance['categories'] = self.categories
        return instance

    # 构建类别
    def _init_categories(self):
        for k, v in classname_to_id.items():
            category = {}
            category['id'] = v
            category['name'] = k
            self.categories.append(category)

    # 构建COCO的image字段
    def _image(self, obj, path):
        image = {}
        from labelme import utils
        img_x = utils.img_b64_to_arr(obj['imageData'])
        h, w = img_x.shape[:-1]
        image['height'] = h
        image['width'] = w
        image['id'] = self.img_id
        image['file_name'] = os.path.basename(path).replace(".json", ".jpg")
        return image

    # 构建COCO的annotation字段
    def _annotation(self, shape):
        # print('shape', shape)
        label = shape['label']
        points = shape['points']
        annotation = {}
        annotation['id'] = self.ann_id
        annotation['image_id'] = self.img_id
        annotation['category_id'] = int(classname_to_id[label])
        annotation['segmentation'] = [np.asarray(points).flatten().tolist()]
        annotation['bbox'] = self._get_box(points)
        annotation['iscrowd'] = 0
        annotation['area'] = 1.0
        return annotation

    # 读取json文件，返回一个json对象
    def read_jsonfile(self, path):
        with open(path, "r", encoding='utf-8') as f:
            return json.load(f)

    # COCO的格式： [x1,y1,w,h] 对应COCO的bbox格式
    def _get_box(self, points):
        min_x = min_y = np.inf
        max_x = max_y = 0
        for x, y in points:
            min_x = min(min_x, x)
            min_y = min(min_y, y)
            max_x = max(max_x, x)
            max_y = max(max_y, y)
        return [min_x, min_y, max_x - min_x, max_y - min_y]

# 训练过程中，如果遇到Index put requires the source and destination dtypes match, got Long for the destination and Int for the source
# 参考：https://github.com/open-mmlab/mmdetection/issues/6706


if __name__ == '__main__':
    labelme_path = "./data-labelme/dataset"
    saved_coco_path = "./data-labelme/"
    print('reading...')
    # 创建文件
    if not os.path.exists("%scoco/annotations/" % saved_coco_path):
        os.makedirs("%scoco/annotations/" % saved_coco_path)
    if not os.path.exists("%scoco/train2017/" % saved_coco_path):
        os.makedirs("%scoco/train2017" % saved_coco_path)
    if not os.path.exists("%scoco/val2017/" % saved_coco_path):
        os.makedirs("%scoco/val2017" % saved_coco_path)
    # 获取images目录下所有的joson文件列表
    print(labelme_path + "/*.json")
    json_list_path = glob.glob(labelme_path + "/*.json")
    print('json_list_path: ', len(json_list_path))
    # 修改2训练集和测试集的比例
    # 数据划分,这里没有区分val2017和tran2017目录，所有图片都放在images目录下
    train_path, val_path = train_test_split(json_list_path, test_size=0.1, train_size=0.9)
    print("train_n:", len(train_path), 'val_n:', len(val_path))
    # 把训练集转化为COCO的json格式
    l2c_train = Lableme2CoCo()
    train_instance = l2c_train.to_coco(train_path)
    l2c_train.save_coco_json(train_instance, '%scoco/annotations/instances_train2017.json' % saved_coco_path)
    for file in train_path:
        # 修改3 换成自己图片的后缀名
        img_name = file.replace('json', 'jpg')
        temp_img = cv2.imread(img_name)
        try:
            cv2.imwrite("{}coco/train2017/{}".format(saved_coco_path, img_name.split('\\')[-1]), temp_img)
        except Exception as e:
            print(e)
            print('Wrong Image:', img_name )
            continue
        print(img_name + '-->', img_name)

    for file in val_path:
        # 修改4 换成自己图片的后缀名
        img_name = file.replace('json', 'jpg')
        temp_img = cv2.imread(img_name)
        try:
            cv2.imwrite("{}coco/val2017/{}".format(saved_coco_path, img_name.split('\\')[-1]), temp_img)
        except Exception as e:
            print(e)
            print('Wrong Image:', img_name)
            continue
        print(img_name + '-->', img_name)

    # 把验证集转化为COCO的json格式
    l2c_val = Lableme2CoCo()
    val_instance = l2c_val.to_coco(val_path)
    l2c_val.save_coco_json(val_instance, '%scoco/annotations/instances_val2017.json' % saved_coco_path)