log gabor滤波器

Log Gabor 滤波器是一种用于图像处理的滤波器，它可以用于图像的特征提取和图像的压缩。它是一种基于 Gabor 滤波器的改进，可以更好地处理图像的低频和高频信息。具体实现可以参考相关的论文和代码实现。

相关问题

帮我用pytorch定义一个loggabor_fn函数，用于生成LogGabor滤波器的参数，以及一个LogGaborConv2d类，用于创建一个神经网络层，其中需使用LogGabor滤波器

好的，以下是定义 loggabor_fn 函数的代码：

import torch
import math

def loggabor_fn(sigma, theta, lambd, gamma, psi, ksize):
    """
    生成 LogGabor 滤波器的参数
    :param sigma: float, 控制高斯包络的标准差
    :param theta: float, 控制滤波器的方向
    :param lambd: float, 控制滤波器的波长
    :param gamma: float, 控制高斯包络的宽度
    :param psi: float, 控制相位
    :param ksize: int, 滤波器的大小
    :return: LogGabor 滤波器的参数
    """
    half_size = ksize // 2
    x, y = torch.meshgrid(torch.arange(-half_size, half_size + 1), torch.arange(-half_size, half_size + 1))
    x_theta = x * math.cos(theta) + y * math.sin(theta)
    y_theta = -x * math.sin(theta) + y * math.cos(theta)
    sigma_x = sigma
    sigma_y = sigma / gamma
    envelope = torch.exp(-0.5 * ((x_theta ** 2) / (sigma_x ** 2) + (y_theta ** 2) / (sigma_y ** 2)))
    wavelength = lambd
    phase = torch.exp(1j * 2 * math.pi * x_theta / wavelength + psi)
    return envelope * phase

接下来是定义 LogGaborConv2d 类的代码：

import torch.nn as nn

class LogGaborConv2d(nn.Module):
    """
    创建一个神经网络层，其中使用 LogGabor 滤波器
    """
    def __init__(self, in_channels, out_channels, kernel_size, sigma, theta, lambd, gamma, psi, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(LogGaborConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = groups
        self.weight = nn.Parameter(torch.zeros((out_channels, in_channels, kernel_size, kernel_size), dtype=torch.complex64))
        self.bias = nn.Parameter(torch.zeros(out_channels)) if bias else None
        for i in range(out_channels):
            for j in range(in_channels):
                self.weight[i, j] = loggabor_fn(sigma=sigma, theta=theta, lambd=lambd, gamma=gamma, psi=psi, ksize=kernel_size)
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.kaiming_uniform_(self.weight.real, a=math.sqrt(5))
        nn.init.zeros_(self.weight.imag)
        if self.bias is not None:
            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight.real)
            bound = 1 / math.sqrt(fan_in)
            nn.init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        return torch.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

希望这些代码能够帮到你！

log-gabor滤波器python代码

以下是 log-gabor 滤波器的 Python 代码：

import numpy as np
import cv2

def log_gabor_filter(image, sigma, theta, Lambda, psi, gamma):
    # 计算图像的傅里叶变换
    fft_image = np.fft.fft2(image)
    fft_shift_image = np.fft.fftshift(fft_image)

    # 构造 log-gabor 滤波器
    rows, cols = image.shape
    u, v = np.meshgrid(np.arange(-cols/2, cols/2), np.arange(-rows/2, rows/2))
    radius = np.sqrt(u**2 + v**2)
    radius[0, 0] = 1
    theta = theta * np.pi / 180
    wavelength = Lambda
    fo = 1.0 / wavelength
    sigma_f = fo / np.pi * np.sqrt(np.log(2) / 2) * (np.power(2, gamma) + 1) / (np.power(2, gamma) - 1)
    sigma_x = sigma / fo
    sigma_y = sigma / fo / gamma
    x_theta = u * np.cos(theta) + v * np.sin(theta)
    y_theta = -u * np.sin(theta) + v * np.cos(theta)
    gb = np.exp(-0.5 * (np.power(x_theta, 2) / np.power(sigma_x, 2) + np.power(y_theta, 2) / np.power(sigma_y, 2))) * np.cos(2 * np.pi * fo * x_theta + psi)
    gb = gb * (radius <= radius.max() / 2)

    # 对图像进行滤波
    filtered_image = np.fft.ifft2(np.fft.ifftshift(fft_shift_image * gb))

    return np.abs(filtered_image)

# 测试代码
if __name__ == '__main__':
    image = cv2.imread('lena.jpg', cv2.IMREAD_GRAYSCALE)
    filtered_image = log_gabor_filter(image, 10, 0, 10, 0, 0.5)
    cv2.imshow('filtered image', filtered_image.astype(np.uint8))
    cv2.waitKey(0)
    cv2.destroyAllWindows()

希望能对你有所帮助！