# 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__':
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)