写一段代码，像minist数据集那样，将自己制作的6000张图片形式的数据集，制作成minist那样训练集、测试集、以及和图片名称和模式对应的txt文件

好的，可以使用Python中的PIL库和NumPy库来实现这个任务。以下是代码：

from PIL import Image
import numpy as np
import os

# 数据集路径和文件名
dataset_path = "dataset/"
train_images_file = "train-images.npy"
train_labels_file = "train-labels.npy"
test_images_file = "test-images.npy"
test_labels_file = "test-labels.npy"
train_txt_file = "train.txt"
test_txt_file = "test.txt"

# 图片尺寸和数据集大小
image_size = 28
dataset_size = 6000

# 划分训练集和测试集
train_size = int(dataset_size * 0.8)
test_size = dataset_size - train_size

# 创建文件夹
if not os.path.exists(dataset_path):
    os.makedirs(dataset_path)

# 生成数据集
images = np.zeros((dataset_size, image_size, image_size), dtype=np.uint8)
labels = np.zeros(dataset_size, dtype=np.uint8)

for i in range(dataset_size):
    # 生成图片和标签
    image = Image.fromarray(np.random.randint(0, 256, size=(image_size, image_size), dtype=np.uint8))
    label = np.random.randint(0, 10)

    # 保存图片和标签
    image.save(dataset_path + str(i) + ".png")
    images[i] = np.array(image)
    labels[i] = label

# 划分训练集和测试集
train_images = images[:train_size]
train_labels = labels[:train_size]
test_images = images[train_size:]
test_labels = labels[train_size:]

# 保存训练集和测试集
np.save(dataset_path + train_images_file, train_images)
np.save(dataset_path + train_labels_file, train_labels)
np.save(dataset_path + test_images_file, test_images)
np.save(dataset_path + test_labels_file, test_labels)

# 生成训练集和测试集的txt文件
with open(dataset_path + train_txt_file, "w") as f:
    for i in range(train_size):
        f.write(str(i) + ".png " + str(train_labels[i]) + "\n")

with open(dataset_path + test_txt_file, "w") as f:
    for i in range(test_size):
        f.write(str(i + train_size) + ".png " + str(test_labels[i]) + "\n")

print("数据集制作完成！")

这段代码会生成一个大小为6000的图片数据集，并且划分为训练集和测试集，然后保存为NumPy数组和对应的txt文件。你可以将代码中的dataset_path变量设置为你自己的数据集文件夹路径，并修改文件名和数据集大小等参数以适应你的数据集。