深度学习---自有图像数据集划分

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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。