1.文件放置
JPEGImages:存放数据集照片
labels:存放yolo格式的txt文件
DataSet:用来存放我们划分后的图片和txt文件
这几个
2.代码
代码参考如下链接,但是在linux中代码中的 '\\' 需要修改成 '/', 将代码放在与上诉文件夹相同的位置运行,运行成功DataSet中就会生成所需文件夹。代码中可以按照自己想要的比列分。(1条消息) YOLOv5数据集划分脚本(train、val、test)_yolov5 val_叱咤风云灬龙的博客-CSDN博客
import os, shutil, random
from tqdm import tqdm
def split_img(img_path, label_path, split_list):
try :
Data = 'DataSet'
# Data是你要将要创建的文件夹路径(路径一定是相对于你当前的这个脚本而言的)
os.mkdir(Data)
train_img_dir = Data + '/images/train'
val_img_dir = Data + '/images/val'
test_img_dir = Data + '/images/test'
train_label_dir = Data + '/labels/train'
val_label_dir = Data + '/labels/val'
test_label_dir = Data + '/labels/test'
# 创建文件夹
os.makedirs(train_img_dir)
os.makedirs(train_label_dir)
os.makedirs(val_img_dir)
os.makedirs(val_label_dir)
os.makedirs(test_img_dir)
os.makedirs(test_label_dir)
except:
print('文件目录已存在')
train, val, test = split_list
all_img = os.listdir(img_path)
all_img_path = [os.path.join(img_path, img) for img in all_img]
# all_label = os.listdir(label_path)
# all_label_path = [os.path.join(label_path, label) for label in all_label]
train_img = random.sample(all_img_path, int(train * len(all_img_path)))
train_img_copy = [os.path.join(train_img_dir, img.split('/')[-1]) for img in train_img]
train_label = [toLabelPath(img, label_path) for img in train_img]
train_label_copy = [os.path.join(train_label_dir, label.split('/')[-1]) for label in train_label]
for i in tqdm(range(len(train_img)), desc='train ', ncols=80, unit='img'):
_copy(train_img[i], train_img_dir)
_copy(train_label[i], train_label_dir)
all_img_path.remove(train_img[i])
val_img = random.sample(all_img_path, int(val / (val + test) * len(all_img_path)))
val_label = [toLabelPath(img, label_path) for img in val_img]
for i in tqdm(range(len(val_img)), desc='val ', ncols=80, unit='img'):
_copy(val_img[i], val_img_dir)
_copy(val_label[i], val_label_dir)
all_img_path.remove(val_img[i])
test_img = all_img_path
test_label = [toLabelPath(img, label_path) for img in test_img]
for i in tqdm(range(len(test_img)), desc='test ', ncols=80, unit='img'):
_copy(test_img[i], test_img_dir)
_copy(test_label[i], test_label_dir)
def _copy(from_path, to_path):
shutil.copy(from_path, to_path)
def toLabelPath(img_path, label_path):
img = img_path.split('/')[-1]
label = img.split('.jpg')[0] + '.txt'
return os.path.join(label_path, label)
def main():
img_path = 'JPEGImages'
label_path = 'labels'
split_list = [0.8, 0.1, 0.1] # 数据集划分比例[train:val:test]
split_img(img_path, label_path, split_list)
if __name__ == '__main__':
main()
3.DataSet文件夹
DataSet
---images # 划分后的图片
-----train
-----test
-----val
---labels # 划分后的txt
-----train
-----test
-----val
4.数据yaml文件
数据yaml文件的路径填写的为我们分割过后的图片的相对路径(相对于train.py)
nc:自己数据集的类别数
names:自己数据集的类别名