1.利用labelme进行数据标注
1.1Labelme 安装方法
首先安装 Anaconda，然后运行下列命令：
#################### for Python 2 ####################conda create --name=labelme python=2.7source activate labelme# conda install -c conda-forge pyside2conda install pyqtpip install labelme# 如果想安装最新版本，请使用下列命令安装：# pip install git+https://github.com/wkentaro/labelme.git#################### for Python 3 ####################conda create --name=labelme python=3.6source activate labelme# conda install -c conda-forge pyside2# conda install pyqtpip install pyqt5 # pyqt5 can be installed via pip on python3pip install labelme输入以下指令打开labelme1.2Labelme 使用教程
使用 labelme 进行场景分割标注的教程详见：labelme
2.转换划分数据集
对数据集进行转换和划分。注意：在数据标注的时候将图片和json文件放在不同的文件夹里。如下图所示，另外新建两个文件夹txt 和split。
2.1将json格式文件转换为txt格式
新建json2txt.py文件，修改文件路径为自己的路径
# -*- coding: utf-8 -*-import jsonimport osimport argparsefrom tqdm import tqdmdef convert_label_json(json_dir, save_dir, classes): json_paths = os.listdir(json_dir) classes = classes.split(',') for json_path in tqdm(json_paths): # for json_path in json_paths: path = os.path.join(json_dir, json_path) with open(path, 'r') as load_f: json_dict = json.load(load_f) h, w = json_dict['imageHeight'], json_dict['imageWidth'] # save txt path txt_path = os.path.join(save_dir, json_path.replace('json', 'txt')) txt_file = open(txt_path, 'w') for shape_dict in json_dict['shapes']: label = shape_dict['label'] label_index = classes.index(label) points = shape_dict['points'] points_nor_list = [] for point in points: points_nor_list.append(point[0] / w) points_nor_list.append(point[1] / h) points_nor_list = list(map(lambda x: str(x), points_nor_list)) points_nor_str = ' '.join(points_nor_list) label_str = str(label_index) + ' ' + points_nor_str + '\n' txt_file.writelines(label_str)if __name__ == "__main__": """ python json2txt_nomalize.py --json-dir my_datasets/color_rings/jsons --save-dir my_datasets/color_rings/txts --classes "cat,dogs" """ parser = argparse.ArgumentParser(description='json convert to txt params') parser.add_argument('--json-dir', type=str,default='D:/ultralytics-main/data/json', help='json path dir') parser.add_argument('--save-dir', type=str,default='D:/ultralytics-main/data/txt' ,help='txt save dir') parser.add_argument('--classes', type=str, default='ccc,ccc1',help='classes') args = parser.parse_args() json_dir = args.json_dir save_dir = args.save_dir classes = args.classes convert_label_json(json_dir, save_dir, classes)2.2划分数据集
新建split.py,修改文件路径为自己的路径
# 将图片和标注数据按比例切分为 训练集和测试集import shutilimport randomimport osimport argparse# 检查文件夹是否存在def mkdir(path): if not os.path.exists(path): os.makedirs(path)def main(image_dir, txt_dir, save_dir): # 创建文件夹 mkdir(save_dir) images_dir = os.path.join(save_dir, 'images') labels_dir = os.path.join(save_dir, 'labels') img_train_path = os.path.join(images_dir, 'train') img_test_path = os.path.join(images_dir, 'test') img_val_path = os.path.join(images_dir, 'val') label_train_path = os.path.join(labels_dir, 'train') label_test_path = os.path.join(labels_dir, 'test') label_val_path = os.path.join(labels_dir, 'val') mkdir(images_dir); mkdir(labels_dir); mkdir(img_train_path); mkdir(img_test_path); mkdir(img_val_path); mkdir(label_train_path); mkdir(label_test_path); mkdir(label_val_path); # 数据集划分比例，训练集75%，验证集15%，测试集15%，按需修改 train_percent = 0.8 val_percent = 0.1 test_percent = 0.1 total_txt = os.listdir(txt_dir) num_txt = len(total_txt) list_all_txt = range(num_txt) # 范围 range(0, num) num_train = int(num_txt * train_percent) num_val = int(num_txt * val_percent) num_test = num_txt - num_train - num_val train = random.sample(list_all_txt, num_train) # 在全部数据集中取出train val_test = [i for i in list_all_txt if not i in train] # 再从val_test取出num_val个元素，val_test剩下的元素就是test val = random.sample(val_test, num_val) print("训练集数目：{}, 验证集数目：{},测试集数目：{}".format(len(train), len(val), len(val_test) - len(val))) for i in list_all_txt: name = total_txt[i][:-4] srcImage = os.path.join(image_dir, name + '.jpg') srcLabel = os.path.join(txt_dir, name + '.txt') if i in train: dst_train_Image = os.path.join(img_train_path, name + '.jpg') dst_train_Label = os.path.join(label_train_path, name + '.txt') shutil.copyfile(srcImage, dst_train_Image) shutil.copyfile(srcLabel, dst_train_Label) elif i in val: dst_val_Image = os.path.join(img_val_path, name + '.jpg') dst_val_Label = os.path.join(label_val_path, name + '.txt') shutil.copyfile(srcImage, dst_val_Image) shutil.copyfile(srcLabel, dst_val_Label) else: dst_test_Image = os.path.join(img_test_path, name + '.jpg') dst_test_Label = os.path.join(label_test_path, name + '.txt') shutil.copyfile(srcImage, dst_test_Image) shutil.copyfile(srcLabel, dst_test_Label)if __name__ == '__main__': """ python split_datasets.py --image-dir my_datasets/color_rings/imgs --txt-dir my_datasets/color_rings/txts --save-dir my_datasets/color_rings/train_data """ parser = argparse.ArgumentParser(description='split datasets to train,val,test params') parser.add_argument('--image-dir', type=str,default='D:/ultralytics-main/data', help='image path dir') parser.add_argument('--txt-dir', type=str,default='D:/ultralytics-main/data/txt' , help='txt path dir') parser.add_argument('--save-dir', default='D:/ultralytics-main/data/split',type=str, help='save dir') args = parser.parse_args() image_dir = args.image_dir txt_dir = args.txt_dir save_dir = args.save_dir main(image_dir, txt_dir, save_dir)运行完后得到如下文件
3.训练设置
3.1新建seg.yaml文件 ，按照下列格式创建 我一般写成绝对路径，方便一点。
train: D:\ultralytics-main\data\split\images\train # train images (relative to 'path') 128 imagesval: D:\ultralytics-main\data\split\images\train # val images (relative to 'path') 128 imagestest: D:\ultralytics-main\data\split\images\train # test images (optional)# Classesnames: 0: ccc 1: ccc13.2训练参数设置
task: segment # YOLO task, i.e. detect, segment, classify, posemode: train # YOLO mode, i.e. train, val, predict, export, track, benchmark# Train settings -------------------------------------------------------------------------------------------------------model: yolov8s-seg.yaml # path to model file, i.e. yolov8n.pt, yolov8n.yaml#model:runs/detect/yolov8s/weights/best.ptdata: seg.yaml # path to data file, i.e. coco128.yamlepochs: 10 # number of epochs to train forpatience: 50 # epochs to wait for no observable improvement for early stopping of trainingbatch: 16 # number of images per batch (-1 for AutoBatch)然后开始训练即可。
参考：
(52条消息) 数据标注软件labelme详解_黑暗星球的博客-CSDN博客
(52条消息) YOLOv5-7.0实例分割训练自己的数据，切分mask图并摆正_yolo 图像分割_jin__9981的博客-CSDN博客