制作自定义语义分割数据集

自定义语义分割数据集（划分训练集与验证集）、并且将一个文件夹下的所有图片的名字存到txt文件
我们可以借助Pytorch从文件夹中读取数据集，十分方便，但是Pytorch中没有提供数据集划分的操作，需要手动将原始的数据集划分为训练集、验证集和测试集，废话不多说， 这里写了一个工具类，帮助大家将数据集自动划分为训练集、验证集和测试集，还可以指定比例，代码如下。

1.划分训练集、验证集与测试集

工具类

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三个文件夹进行
    :param src_data_folder: 源文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/src_data
    :param target_data_folder: 目标文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/target_data
    :param train_scale: 训练集比例
    :param val_scale: 验证集比例
    :param test_scale: 测试集比例
    :return:
    '''
    print("开始数据集划分")
    class_names = os.listdir(src_data_folder)
    # 在目标目录下创建文件夹
    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)
        # 然后在split_path的目录下创建类别文件夹
        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)

    # 按照比例划分数据集，并进行数据图片的复制
    # 首先进行分类遍历
    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)
                # print("{}复制到了{}".format(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)
                # print("{}复制到了{}".format(src_img_path, val_folder))
                val_num = val_num + 1
            else:
                copy2(src_img_path, test_folder)
                # print("{}复制到了{}".format(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))


if __name__ == '__main__':
    src_data_folder = "/home/ubuntu/PycharmProjects/DataSet/workshop"
    target_data_folder = "/home/ubuntu/PycharmProjects/DataSet/workshop"
    data_set_split(src_data_folder, target_data_folder)

2.文件名称保存为txt

import os

img_path = '/home/ubuntu/PycharmProjects/DataSet/workshop/train/'
img_list = os.listdir(img_path)
print(img_list)
f = open('/home/ubuntu/PycharmProjects/DataSet/workshop/workshop_train_list.txt', 'w')
for img in img_list:
    img = img[:-4] + '.jpg'
    imgstr = 'train/'+img+' '+'trainannot/'+img[:-4]+'.png'+'\n'
    print(imgstr)
    f.write(imgstr)
f.close()

3.文件移动
第一张图是训练集(train)，第二张图是未划分之前的原标签(mask)数据集，现在需要根据训练集中的图片，将与之对应的标签(mask)图片移动到另一个文件夹(trainannot)中

import os, random, shutil

def moveImage(fileImageDir):
    pathDir = os.listdir(fileImageDir)
    filenumber = len(pathDir)
    rate = 0.1
    picknumber = int(filenumber * rate)  # 按照设定比例从文件夹中取一定数量图片
    sample = random.sample(pathDir, picknumber)
    print(sample)
    for name in sample:
        shutil.move(fileImageDir + name, tarImageDir + name)
    return


def extract_name(Image_dir, write_file_name):
    file_list = []
    # 读取文件，并将地址、图片名和标签写到txt文件中
    write_file = open(write_file_name, "w")  # 打开write_file_name文件
    for file in os.listdir(Image_dir):
        if file.endswith(".jpg"):
            name = file.split('.')[0]  # 分割图像名称和后缀名
            write_name = name
            file_list.append(write_name)
    sorted(file_list)  # 将列表中所有元素随机排列
    number_of_lines = len(file_list)
    for current_line in range(number_of_lines):
        write_file.write(file_list[current_line] + '\n')
    write_file.close()


def moveLabel(fileLabelDir, write_file_name):
    pathDir = os.listdir(fileLabelDir)
    f = open(write_file_name, 'r')
    lines = f.readlines()
    for line in lines:
        line = line.strip('\n')  # 去除文本的换行符，否则报错
        shutil.move(fileLabelDir + str(line) + '.jpg', tarLabelDir + str(line) + '.jpg')


if __name__ == '__main__':
    fileImageDir = '/home/ubuntu/PycharmProjects/DataSet/workshop/img/'  # 训练集图像地址
    tarImageDir = '/home/ubuntu/PycharmProjects/DataSet/workshop/test/'  # 测试集图像地址
    Image_dir = tarImageDir
    write_file_name = '/home/ubuntu/PycharmProjects/DataSet/workshop/img.txt'  # 提取测试集文件名称文件存放地址
    fileLabelDir = '/home/ubuntu/PycharmProjects/DataSet/workshop/mask/'  # 训练集标签地址
    tarLabelDir = '/home/ubuntu/PycharmProjects/DataSet/workshop/testannot/'  # 测试集标签地址
    moveImage(fileImageDir)
    extract_name(Image_dir, write_file_name)
    moveLabel(fileLabelDir, write_file_name)

4.将数据集保存为.pkl格式以及读取.pkl格式文件

import pickle  

dict_data = {"name":["张三", "李四"]}

with open("dict_data.pkl", 'wb') as fo:     # 将数据写入pkl文件
    pickle.dump(dict_data, fo)
    
with open("dict_data.pkl", 'rb') as fo:     # 读取pkl文件数据
    dict_data = pickle.load(fo, encoding='bytes')
  
print(dict_data.keys())    # 测试我们读取的文件
print(dict_data)
print(dict_data["name"])
==============================
结果如下：
dict_keys(['name'])
{'name': ['张三', '李四']}
['张三', '李四']