把代码全部整合成一起给我

当然可以。这里是整个代码，包含了图像读取、灰度转换、DCT压缩、小波分解与融合，以及图像的显示和保存（如果你想要的话）。请注意，由于你提到的“脸”系列图片(`face1~face8`)的具体路径需要你自己提供，这里假设它们都存在当前工作目录下。另外，关于小波融合的部分，这里简单地使用了系数相加作为例子，你可以根据需要选择其他融合方法。

import cv2
import numpy as np
import matplotlib.pyplot as plt
from scipy.fft import dct, idct
from pywt import wavedec2, waverec2

# 读取图像列表
image_files = ['face1.jpg', 'face2.jpg']  # 替换成你实际的脸图片文件名
images = [cv2.imread(file, cv2.IMREAD_GRAYSCALE) for file in image_files]

# 小波分解函数
def perform_wavelet_decomposition(image, wavelet_name='haar', level=2):
    return wavedec2(image, wavelet_name, level=level)

# 融合小波系数函数，这里简单采用平均
def fuse_coefficients(coeffs_list):
    fused_coeffs = np.mean([coeffs[0] for coeffs in coeffs_list], axis=0)
    return fused_coeffs

# 用于组合DCT和小波的处理流程
def process_images(images, dct_threshold=60, wavelet_name='haar', level=2):
    processed_images = []
    
    for img in images:
        # DCT处理
        gray_in = img.copy()
        h, w = gray_in.shape
        DCT_I = dct(dct(gray_in.T, norm='ortho').T, norm='ortho')
        
        # 压缩 DCT
        FDCT_I = np.zeros((h, w))
        FDCT_I[:dct_threshold, :dct_threshold] = DCT_I[:dct_threshold, :dct_threshold]
        
        # 逆DCT
        gray_out_dct = np.abs(idct(idct(FDCT_I.T, norm='ortho').T, norm='ortho'))
        
        # 小波处理
        coeffs = perform_wavelet_decomposition(gray_out_dct, wavelet_name, level)
        
        # 融合系数
        fused_coeffs = fuse_coefficients([coeffs])
        
        # 重构图像
        reconstructed_img = waverec2(fused_coeffs, wavelet_name)
        
        processed_images.append(reconstructed_img)
    
    return processed_images

# 执行处理
processed_images = process_images(images, dct_threshold=60, wavelet_name='haar', level=2)

# 显示原图和处理后的图像
fig, axs = plt.subplots(nrows=len(images), ncols=2, figsize=(10, len(images)*5))
for i, (img, proc_img) in enumerate(zip(images, processed_images)):
    axs[i, 0].imshow(img, cmap='gray')
    axs[i, 0].set_title(f"原图 {i+1}")
    axs[i, 0].axis('off')

    axs[i, 1].imshow(proc_img, cmap='gray')
    axs[i, 1].set_title(f"DCT & 小波融合后的图 {i+1}")
    axs[i, 1].axis('off')

plt.tight_layout()
plt.show()

# 保存结果
if save_images:
    for i, proc_img in enumerate(processed_images):
        cv2.imwrite(f'reconstructed_{i+1}.jpg', proc_img)