我现在有灰度图的bmp图像和二值图的bmp图像，其中二值图我是直接拿来当成label图，我想把这两个文件直接划分成训练集、验证集和测试集，可以给出相应代码吗

当您有灰度图的BMP图像和对应的二值图作为标签时，您可以使用Python中的一些库来划分数据集。下面是一个示例代码，使用scikit-learn库中的train_test_split函数来进行数据集划分：

import os
import random
from sklearn.model_selection import train_test_split

# 设置图像和标签的文件夹路径
image_folder = "path_to_gray_images_folder"
label_folder = "path_to_binary_images_folder"

# 获取图像和标签文件列表
image_files = os.listdir(image_folder)
label_files = os.listdir(label_folder)

# 确保图像和标签文件数量相同
assert len(image_files) == len(label_files), "Number of images and labels do not match."

# 打乱图像和标签文件顺序
random.shuffle(image_files)
random.shuffle(label_files)

# 划分数据集
train_ratio = 0.7  # 训练集比例
val_ratio = 0.15  # 验证集比例
test_ratio = 0.15  # 测试集比例

# 根据比例计算划分数据集的数量
train_count = int(len(image_files) * train_ratio)
val_count = int(len(image_files) * val_ratio)
test_count = len(image_files) - train_count - val_count

# 划分图像和标签文件列表为训练集、验证集和测试集
train_images, val_images, train_labels, val_labels = train_test_split(image_files[:train_count], label_files[:train_count], test_size=val_count)
test_images, test_labels = image_files[train_count+val_count:], label_files[train_count+val_count:]

# 打印数据集信息
print("Train set: {} images".format(len(train_images)))
print("Validation set: {} images".format(len(val_images)))
print("Test set: {} images".format(len(test_images)))

# 在每个数据集中移动图像和标签文件到相应的文件夹
def move_files(files, src_folder, dest_folder):
    for file in files:
        src_path = os.path.join(src_folder, file)
        dest_path = os.path.join(dest_folder, file)
        os.rename(src_path, dest_path)

# 移动图像和标签文件到训练集文件夹
move_files(train_images, image_folder, "path_to_train_images_folder")
move_files(train_labels, label_folder, "path_to_train_labels_folder")

# 移动图像和标签文件到验证集文件夹
move_files(val_images, image_folder, "path_to_val_images_folder")
move_files(val_labels, label_folder, "path_to_val_labels_folder")

# 移动图像和标签文件到测试集文件夹
move_files(test_images, image_folder, "path_to_test_images_folder")
move_files(test_labels, label_folder, "path_to_test_labels_folder")

请根据您的实际情况修改代码中的文件夹路径，并确保文件夹和文件命名的正确性。这段代码将根据指定的比例划分图像和标签文件为训练集、验证集和测试集，并将它们移动到相应的文件夹中。