小波变换批量图像融合python代码

以下是基于PyWavelets库实现的小波变换批量图像融合Python代码：

import os
import numpy as np
import cv2
import pywt

# 小波变换融合函数
def wavelet_fusion(img1, img2, method='max', level=1, wavelet='haar'):
    # 将图像转换为灰度图
    img1_gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
    img2_gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
    
    # 将图像进行小波分解
    coeffs1 = pywt.wavedec2(img1_gray, wavelet, mode='symmetric', level=level)
    coeffs2 = pywt.wavedec2(img2_gray, wavelet, mode='symmetric', level=level)
    
    # 将第一幅图像的低频系数与第二幅图像的高频系数进行合并
    fused_coeffs = []
    for i in range(len(coeffs1)):
        if i == 0:
            fused_coeffs.append(coeffs1[0])
        else:
            if method == 'max':
                fused_coeffs.append(np.maximum(coeffs1[i], coeffs2[i]))
            elif method == 'min':
                fused_coeffs.append(np.minimum(coeffs1[i], coeffs2[i]))
            elif method == 'mean':
                fused_coeffs.append((coeffs1[i] + coeffs2[i]) / 2)
    
    # 将合并后的系数进行小波重构
    fused_img = pywt.waverec2(fused_coeffs, wavelet)
    
    # 将图像进行归一化
    fused_img = cv2.normalize(fused_img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
    
    return fused_img

# 批量图像融合函数
def batch_fusion(img_dir, method='max', level=1, wavelet='haar'):
    # 读取所有图像
    img_names = os.listdir(img_dir)
    img_names.sort()
    imgs = []
    for img_name in img_names:
        img_path = os.path.join(img_dir, img_name)
        img = cv2.imread(img_path)
        imgs.append(img)
    
    # 进行图像融合
    fused_img = imgs[0]
    for img in imgs[1:]:
        fused_img = wavelet_fusion(fused_img, img, method, level, wavelet)
    
    return fused_img

# 测试函数
if __name__ == '__main__':
    img_dir = 'image_folder'
    fused_img = batch_fusion(img_dir, method='max', level=1, wavelet='haar')
    cv2.imshow('Fused Image', fused_img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

其中，`wavelet_fusion`函数实现了单幅图像的小波变换融合，`batch_fusion`函数实现了批量图像融合，`method`参数指定了融合方式（最大值、最小值或平均值），`level`参数指定了小波变换的层数，`wavelet`参数指定了小波基函数。在测试函数中，将融合后的图像显示出来。