Please download the dataset from ‘./dataset/images’, which contains RGB images sampled from the Anime face dataset. According to each color channel, the RGB images in this dataset can be loaded into three single-channel matrices, i.e., , and , each of them contains rows (pixels) and columns (faces). Please combine the three single-channel matrices into the matrix , where . (Please provide the code, no need to save the matrices into files)要首先将不同大小的图片先转换成相同大小（64*64）后进行计算，将题目要求翻译成中文，并用python实现，同时给出中文注释.最终的矩阵的行数等于图像数，矩阵的列等于图像的长宽通道数的乘积

以下是将不同大小的RGB图片转换为64x64大小后，将三个单通道矩阵合并为一个三通道矩阵的Python代码实现，中文注释已经添加：

import cv2
import numpy as np
import os

# 定义需要转换的图片大小
IMG_SIZE = (64, 64)

# 读取图片并转换为指定大小
def read_image(file_path):
    img = cv2.imread(file_path)
    img = cv2.resize(img, IMG_SIZE)
    return img

# 读取指定路径下所有图片并转换为三个单通道矩阵
def load_images(path):
    # 获取路径下所有文件名
    img_names = os.listdir(path)
    # 初始化三个单通道矩阵
    img_r = np.zeros((len(img_names), IMG_SIZE[0], IMG_SIZE[1]), dtype=np.uint8)
    img_g = np.zeros((len(img_names), IMG_SIZE[0], IMG_SIZE[1]), dtype=np.uint8)
    img_b = np.zeros((len(img_names), IMG_SIZE[0], IMG_SIZE[1]), dtype=np.uint8)
    # 遍历所有图片并转换为单通道矩阵
    for i, name in enumerate(img_names):
        file_path = os.path.join(path, name)
        img = read_image(file_path)
        img_r[i] = img[:, :, 0]  # 取红色通道
        img_g[i] = img[:, :, 1]  # 取绿色通道
        img_b[i] = img[:, :, 2]  # 取蓝色通道
    return img_r, img_g, img_b

# 合并三个单通道矩阵为一个三通道矩阵
def merge_channels(img_r, img_g, img_b):
    # 获取图像的数量、高度和宽度
    n_imgs = img_r.shape[0]
    height = img_r.shape[1]
    width = img_r.shape[2]
    # 初始化三通道矩阵
    img_rgb = np.zeros((n_imgs, height, width, 3), dtype=np.uint8)
    # 将三个单通道矩阵合并为一个三通道矩阵
    img_rgb[:, :, :, 0] = img_r
    img_rgb[:, :, :, 1] = img_g
    img_rgb[:, :, :, 2] = img_b
    return img_rgb

# 加载图片并转换为三通道矩阵
img_r, img_g, img_b = load_images('./dataset/images')
img_rgb = merge_channels(img_r, img_g, img_b)

# 打印矩阵的形状，确保符合要求
print(img_rgb.shape)

根据题目要求，最终的矩阵的行数等于图像数，矩阵的列等于图像的长宽通道数的乘积。