深度学习分类数据集小工具

import os
dir = r"./data/train"
dirs = os.listdir(dir)
for i in range(len(dirs)):
    try:
        imgs = os.listdir(os.path.join(dir, dirs[i]))
        print(dirs[i]+":"+str(len(imgs)))
    except:
        pass

import os
import random
import cv2
import shutil
def get_txt(data_path, save_path, trainval_percent, classname, type):
    src_dir = os.path.join(data_path, classname)
    des_dir = os.path.join(save_path, classname)
    if not os.path.exists(des_dir):
        os.makedirs(des_dir)
    if type == 0:
        total_img = os.listdir(src_dir)
        num = len(total_img)
        list1 = range(num)
        tv = int(num * trainval_percent)
        trainval = random.sample(list1, tv)
        for i in range(num):
            name = total_img[i][:-4]
            if i in trainval:
                pass
            else:
                src_path = os.path.join(src_dir, name+".jpg")
                des_path = os.path.join(des_dir, name+".jpg")
                img = cv2.imread(src_path)
                print(name)
                cv2.imshow("img", img)
                cv2.waitKey(1)
                shutil.move(src_path, des_path)
    elif type == 1:
        total_img = os.listdir(des_dir)
        for i in range(len(total_img)):
            name = total_img[i][:-4]
            src_path = os.path.join(src_dir, name + ".jpg")
            des_path = os.path.join(des_dir, name + ".jpg")
            img = cv2.imread(des_path)
            cv2.imshow("img", img)
            cv2.waitKey(1)
            shutil.move(des_path, src_path)
if __name__ == '__main__':
    data_dir = r"./classify_data"
    data_path = os.path.join(data_dir, "train")
    save_path = os.path.join(data_dir, "test")
    names = os.listdir(data_path)
    className = []
    for i in range(len(names)):
        className.append(names[i])
    trainval_percent = 0.8
    for i in range(len(className)):
        get_txt(data_path, save_path, trainval_percent, className[i], 0)