使用Directional Lifting Wavelet Transform处理RGB图像并且有batc_size和 通道数的python代码及其逆变换

以下是使用Directional Lifting Wavelet Transform处理RGB图像的Python代码，其中包括批量大小和通道数：

import numpy as np
import pywt

def forward_DWT_rgb(img, batch_size=1):
    # 将RGB图像分解为YCbCr通道
    ycbcr = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    y, cb, cr = cv2.split(ycbcr)
    
    # 对每个通道进行DWT变换
    y = forward_DWT(y, batch_size=batch_size)
    cb = forward_DWT(cb, batch_size=batch_size)
    cr = forward_DWT(cr, batch_size=batch_size)
    
    # 合并通道
    ycbcr = cv2.merge((y, cb, cr))
    result = cv2.cvtColor(ycbcr, cv2.COLOR_YCrCb2RGB)
    return result

def inverse_DWT_rgb(img, batch_size=1):
    # 将RGB图像分解为YCbCr通道
    ycbcr = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    y, cb, cr = cv2.split(ycbcr)
    
    # 对每个通道进行DWT逆变换
    y = inverse_DWT(y, batch_size=batch_size)
    cb = inverse_DWT(cb, batch_size=batch_size)
    cr = inverse_DWT(cr, batch_size=batch_size)
    
    # 合并通道
    ycbcr = cv2.merge((y, cb, cr))
    result = cv2.cvtColor(ycbcr, cv2.COLOR_YCrCb2RGB)
    return result

def forward_DWT(img, batch_size=1):
    # 获取图像的行和列数
    rows, cols = img.shape
    
    # 计算需要增加的零填充数目
    add_rows = 0 if rows % 2 == 0 else 1
    add_cols = 0 if cols % 2 == 0 else 1
    
    # 零填充图像
    img = cv2.copyMakeBorder(img, 0, add_rows, 0, add_cols, cv2.BORDER_CONSTANT, value=0)
    
    # 将图像切分为LL、LH、HL和HH子带
    LL, (LH, HL, HH) = pywt.dwt2(img, 'haar')
    
    # 将子带拼接成一个数组
    output = np.stack((LL, LH, HL, HH), axis=-1)
    
    # 递归地对LL子带进行DWT变换
    if batch_size > 1 and rows > batch_size and cols > batch_size:
        for i in range(0, rows, batch_size):
            for j in range(0, cols, batch_size):
                output[i:i+batch_size, j:j+batch_size, 0] = forward_DWT(output[i:i+batch_size, j:j+batch_size, 0], batch_size=batch_size)
    elif rows > 1 and cols > 1:
        output[:rows//2, :cols//2, 0] = forward_DWT(output[:rows//2, :cols//2, 0])
    
    return output

def inverse_DWT(img, batch_size=1):
    # 获取图像的行和列数
    rows, cols, _ = img.shape
    
    # 递归地对LL子带进行DWT逆变换
    if batch_size > 1 and rows > batch_size and cols > batch_size:
        for i in range(0, rows, batch_size):
            for j in range(0, cols, batch_size):
                img[i:i+batch_size, j:j+batch_size, 0] = inverse_DWT(img[i:i+batch_size, j:j+batch_size, 0], batch_size=batch_size)
    elif rows > 1 and cols > 1:
        img[:rows//2, :cols//2, 0] = inverse_DWT(img[:rows//2, :cols//2, 0])
    
    # 将子带拆分为LL、LH、HL和HH
    LL, LH, HL, HH = np.split(img, 4, axis=-1)
    
    # 进行逆DWT变换
    output = pywt.idwt2((LL, (LH, HL, HH)), 'haar')
    
    # 去除零填充
    output = output[:rows-1, :cols-1]
    
    return output

使用示例：

import cv2

# 读取图像
img = cv2.imread('test.jpg')

# 对图像进行DWT变换
result = forward_DWT_rgb(img, batch_size=32)

# 对图像进行DWT逆变换
result = inverse_DWT_rgb(result, batch_size=32)

# 显示原始图像和变换后的图像
cv2.imshow('Original', img)
cv2.imshow('Transformed', result)
cv2.waitKey(0)