classification-main/tool/generate_txt.py

import os
from torchvision import datasets

'''
    为数据集生成对应的txt文件
    数据集排列格式：数据集名称->train/test->分类名称->图片
'''


def gen_txt(txt_path, img_dir):
    f = open(txt_path, 'w')
    classes = []  # 列表存储所有类名
    for root, s_dirs, _ in os.walk(img_dir, topdown=True):  # 获取 train文件下各文件夹名称
        j = 0
        for sub_dir in s_dirs:
            classes.append(sub_dir)  # 将类名添加到列表
            i_dir = os.path.join(root, sub_dir)             # 获取各类的文件夹 绝对路径
            print(i_dir)
            img_list = os.listdir(i_dir)                    # 获取类别文件夹下所有png图片的路径
            for i in range(len(img_list)):
                if not img_list[i].endswith('jpg'):         # 若不是png文件，跳过
                    continue
                label = str(j)
                img_path = os.path.join(i_dir, img_list[i])
                line = img_path + ' ' + label + '\n'
                f.write(line)
            j+=1
    f.close()
    return classes

def write_class_list(classes, class_txt_path):
    with open(class_txt_path, 'w') as f:
        for cls in sorted(classes):
            f.write(cls + '\n')


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='')
    parser.add_argument('--txt_path', default='./dataset/New_dataset', type=str, help='path to new datasets')
    parser.add_argument('--specific_data', default='testtest', type=str, help='process the file_name')
    parser.add_argument('--txt_name', default='train', type=str, help='process the file_name')
    # parser.add_argument('--class_txt_path', default='./dataset/New_dataset', type=str, help='class.txt')
    args = parser.parse_args()

    train_txt_path = os.path.join(args.txt_path, f"{args.txt_name}.txt")

    train_dir = os.path.join(args.txt_path, args.specific_data)

    # valid_txt_path = os.path.join('/home/yhsun/classification-main/dataset/CIFAR-10', "test_png.txt")
    # valid_dir = os.path.join('/home/yhsun/classification-main/dataset/CIFAR-10', 'test_cifar10_PNG')
    class_txt_path = os.path.join(args.txt_path, "class.txt")

    classes = gen_txt(train_txt_path, train_dir)
    # gen_txt(valid_txt_path, valid_dir)
    write_class_list(classes, class_txt_path)