# 读取图像 img = cv2.imread('tupianji/peizhuntu.jpg', cv2.IMREAD_GRAYSCALE) # 定义小波变换类型和层数 wavelet_type = 'db4' level = 3 # 进行小波变换 coeffs = pywt.wavedec2(img, wavelet_type, level=level) # 高频子带融合方法：取两个图像的高频子带系数的平均值 def high_frequency_fusion(coeffs1, coeffs2): fused_coeffs = [] for i in range(1, len(coeffs1)): if isinstance(coeffs1[i], tuple): cH1, cV1, cD1 = coeffs1[i] cH2, cV2, cD2 = coeffs2[i] cH = (cH1 + cH2) / 2 cV = (cV1 + cV2) / 2 cD = (cD1 + cD2) / 2 fused_coeffs.append((cH, cV, cD)) else: cA1, cA2 = coeffs1[i], coeffs2[i] cA = (cA1 + cA2) / 2 fused_coeffs.append(cA) return tuple(fused_coeffs) # 低频子带融合方法：取两个图像的低频子带系数的加权平均值 def low_frequency_fusion(coeffs1, coeffs2, alpha=0.5): cA1 = coeffs1[0] cA2 = coeffs2[0] cA = alpha * cA1 + (1 - alpha) * cA2 return cA # 融合两幅图像的小波系数 fused_coeffs = [] for i in range(len(coeffs)): if i == 0: # 对低频子带系数进行融合 fused_coeffs.append(low_frequency_fusion(coeffs[i], coeffs2[i], alpha=0.5)) else: # 对高频子带系数进行融合 fused_coeffs.append(high_frequency_fusion(coeffs[i], coeffs2[i])) # 进行小波变换反变换，重构图像 fused_img = pywt.waverec2(fused_coeffs, wavelet_type) # 显示原图像和融合后的图像 cv2.imshow('Original Image', img) cv2.imshow('Fused Image', fused_img.astype(np.uint8)) cv2.waitKey(0) cv2.destroyAllWindows()怎么改能定义coeffs2

要定义 `coeffs2`，您可以先读取另一张待融合的图像，然后进行小波变换，得到对应的小波系数 `coeffs2`。具体的代码如下：

# 读取第二张待融合的图像
img2 = cv2.imread('tupianji/peizhuntu2.jpg', cv2.IMREAD_GRAYSCALE)

# 进行小波变换
coeffs2 = pywt.wavedec2(img2, wavelet_type, level=level)

在这个代码中，我们假设第二张待融合的图像的文件名为 `peizhuntu2.jpg`，并且它位于 `tupianji` 文件夹中。您可以根据实际情况修改文件名和文件路径。

然后，您可以将 `coeffs2` 作为参数传递给 `low_frequency_fusion` 和 `high_frequency_fusion` 函数。修改后的代码如下：

# 读取图像
img = cv2.imread('tupianji/peizhuntu.jpg', cv2.IMREAD_GRAYSCALE)

# 定义小波变换类型和层数
wavelet_type = 'db4'
level = 3

# 进行小波变换
coeffs = pywt.wavedec2(img, wavelet_type, level=level)

# 高频子带融合方法：取两个图像的高频子带系数的平均值
def high_frequency_fusion(coeffs1, coeffs2):
    fused_coeffs = []
    for i in range(1, len(coeffs1)):
        if isinstance(coeffs1[i], tuple):
            cH1, cV1, cD1 = coeffs1[i]
            cH2, cV2, cD2 = coeffs2[i]
            cH = (cH1 + cH2) / 2
            cV = (cV1 + cV2) / 2
            cD = (cD1 + cD2) / 2
            fused_coeffs.append((cH, cV, cD))
        else:
            cA1, cA2 = coeffs1[i], coeffs2[i]
            cA = (cA1 + cA2) / 2
            fused_coeffs.append(cA)
    return tuple(fused_coeffs)

# 低频子带融合方法：取两个图像的低频子带系数的加权平均值
def low_frequency_fusion(coeffs1, coeffs2, alpha=0.5):
    cA1 = coeffs1[0]
    cA2 = coeffs2[0]
    cA = alpha * cA1 + (1 - alpha) * cA2
    return cA

# 读取第二张待融合的图像
img2 = cv2.imread('tupianji/peizhuntu2.jpg', cv2.IMREAD_GRAYSCALE)

# 进行小波变换
coeffs2 = pywt.wavedec2(img2, wavelet_type, level=level)

# 融合两幅图像的小波系数
fused_coeffs = []
for i in range(len(coeffs)):
    if i == 0:
        # 对低频子带系数进行融合
        fused_coeffs.append(low_frequency_fusion(coeffs[i], coeffs2[i], alpha=0.5))
    else:
        # 对高频子带系数进行融合
        fused_coeffs.append(high_frequency_fusion(coeffs[i], coeffs2[i]))

# 进行小波变换反变换，重构图像
fused_img = pywt.waverec2(fused_coeffs, wavelet_type)

# 显示原图像和融合后的图像
cv2.imshow('Original Image', img)
cv2.imshow('Fused Image', fused_img.astype(np.uint8))
cv2.waitKey(0)
cv2.destroyAllWindows()

在这个修改后的代码中，我们首先读取了第二张待融合的图像，然后使用 `pywt.wavedec2` 函数对它进行小波变换，得到了 `coeffs2` 变量，然后将其作为参数传递给 `low_frequency_fusion` 和 `high_frequency_fusion` 函数。