深度学习—自有图像数据集划分

  要对自有图像数据集进行图像分类，首选需要将自有图像数据集划分为train和val(或者test）数据集。

       当然 前提是将自有图像数据集已经按照分类进行了预处理，每个分类的图像作为一个单独的目录。然后划分train和val的代码如下所示：

import os
import random
import shutil
from shutil import copy2
 
def data_set_split(src_data_folder, target_data_folder, train_scale=0.8, val_scale=0.1, test_scale=0.1):
        #读取源数据文件夹，生成划分好的文件夹，分为trian、val、test三个文件夹
    print("开始数据集划分")
    class_names = os.listdir(src_data_folder)
    split_names = ['train', 'val', 'test']
    for split_name in split_names:
        split_path = os.path.join(target_data_folder, split_name)
        if os.path.isdir(split_path):
            pass
        else:
            os.mkdir(split_path)
        for class_name in class_names:
            class_split_path = os.path.join(split_path, class_name)
            if os.path.isdir(class_split_path):
                pass
            else:
                os.mkdir(class_split_path)
 
    for class_name in class_names:
        current_class_data_path = os.path.join(src_data_folder, class_name)
        current_all_data = os.listdir(current_class_data_path)
        current_data_length = len(current_all_data)
        current_data_index_list = list(range(current_data_length))
        random.shuffle(current_data_index_list)
 
        train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)
        val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name)
        test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)
        train_stop_flag = current_data_length * train_scale
        val_stop_flag = current_data_length * (train_scale + val_scale)
        current_idx = 0
        train_num = 0
        val_num = 0
        test_num = 0
        for i in current_data_index_list:
            src_img_path = os.path.join(current_class_data_path, current_all_data[i])
            if current_idx <= train_stop_flag:
                copy2(src_img_path, train_folder)
                train_num = train_num + 1
            elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag):
                copy2(src_img_path, val_folder)
                val_num = val_num + 1
            else:
                copy2(src_img_path, test_folder)
                test_num = test_num + 1
 
            current_idx = current_idx + 1
 
        print("*********************************{}*************************************".format(class_name))
        print("{}类按照{}：{}：{}的比例划分完成，一共{}张图片".format(class_name, train_scale, val_scale, test_scale, current_data_length))
        print("训练集{}：{}张".format(train_folder, train_num))
        print("验证集{}：{}张".format(val_folder, val_num))
        print("测试集{}：{}张".format(test_folder, test_num))
 
 
src_data_folder = "./origin"
target_data_folder = "./demo"
data_set_split(src_data_folder, target_data_folder)

在执行了上述代码之后，实现了自有图像数据集的划分，然后就可以利用该数据集进行模型训练了。