YOLOv8实例分割制作自己的数据集代码

# 将图片和标注数据按比例切分为 训练集和测试集 import shutil import random import os import 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)