C Sharp代码实现Gabor滤波器

可以调用Emgu CV库中的Gabor滤波器实现。以下是示例代码：

using Emgu.CV;

using Emgu.CV.CvEnum;

using Emgu.CV.Structure;

...

// 定义Gabor核的参数

double lambda = 4;

double theta = Math.PI / 4;

double psi = Math.PI / 4;

double sigma = 2;

double gamma = 0.5;

// 调用Gabor滤波器

Image<Gray, float> srcImg = ...

Image<Gray, float> dstImg = new Image<Gray, float>(srcImg.Size);

CvInvoke.cvFilter2D(srcImg.Ptr, dstImg.Ptr, CvEnum.CV_DEPTH.CV_32F,

CvInvoke.cvGetGaborKernel(new Size(9, 9), sigma, theta, lambda, gamma, psi, Emgu.CV.CvEnum.GABOR_TYPE.GABOR_REAL),

new Point(-1, -1));

// 显示结果

ImageViewer.Show(dstImg, "Gabor Filtered Image");

相关问题

图像Gabor滤波器算法C Sharp代码实现

由于Gabor滤波器算法是一种较为复杂的算法，需要进行多次卷积和高斯平滑等操作，因此需要使用一些专业的图像处理库来实现。以下是一个使用AForge.NET库实现Gabor滤波器算法的C#代码示例：

using System;
using System.Drawing;
using AForge.Imaging;
using AForge.Imaging.Filters;
using AForge.Math;

public class GaborFilter
{
    private double lambda;
    private double theta;
    private double psi;
    private double sigma;
    private double gamma;

    public GaborFilter(double lambda, double theta, double psi, double sigma, double gamma)
    {
        this.lambda = lambda;
        this.theta = theta;
        this.psi = psi;
        this.sigma = sigma;
        this.gamma = gamma;
    }

    public Bitmap Apply(Bitmap input)
    {
        // Convert the input image to grayscale
        Grayscale grayscale = new Grayscale(0.2125, 0.7154, 0.0721);
        Bitmap grayImage = grayscale.Apply(input);

        // Create a Gabor filter
        GaborFilter filter = new GaborFilter(lambda, theta, psi, sigma, gamma);

        // Apply the filter to the input image
        Bitmap filteredImage = filter.Apply(grayImage);

        // Return the filtered image
        return filteredImage;
    }

    private Bitmap Apply(Bitmap inputImage)
    {
        // Get the size of the input image
        int width = inputImage.Width;
        int height = inputImage.Height;

        // Create a complex image to store the filtered image
        ComplexImage complexImage = ComplexImage.FromBitmap(inputImage);

        // Create a kernel for the Gabor filter
        double[,] kernel = CreateGaborKernel(width, height);

        // Apply the kernel to the complex image
        complexImage.ForwardFourierTransform();
        complexImage.ApplyFilter(kernel);
        complexImage.BackwardFourierTransform();

        // Convert the complex image to a grayscale image
        Grayscale grayscale = new Grayscale(0.2125, 0.7154, 0.0721);
        Bitmap outputImage = grayscale.Apply(complexImage.ToBitmap());

        // Return the filtered image
        return outputImage;
    }

    private double[,] CreateGaborKernel(int width, int height)
    {
        double[,] kernel = new double[width, height];

        // Calculate the maximum distance from the center of the kernel
        double maxDistance = Math.Sqrt(Math.Pow(width / 2, 2) + Math.Pow(height / 2, 2));

        // Calculate the sine and cosine of the angle theta
        double sinTheta = Math.Sin(theta);
        double cosTheta = Math.Cos(theta);

        // Calculate the constants for the kernel
        double sigmaX = sigma;
        double sigmaY = sigma / gamma;
        double twoSigmaXSquared = 2 * Math.Pow(sigmaX, 2);
        double twoSigmaYSquared = 2 * Math.Pow(sigmaY, 2);
        double cos2Theta = Math.Cos(2 * theta);
        double sin2Theta = Math.Sin(2 * theta);

        // Calculate the elements of the kernel
        for (int x = 0; x < width; x++)
        {
            for (int y = 0; y < height; y++)
            {
                // Calculate the distance from the center of the kernel
                double distance = Math.Sqrt(Math.Pow(x - (width / 2), 2) + Math.Pow(y - (height / 2), 2));

                // Calculate the x and y components of the rotated coordinates
                double xPrime = (x - (width / 2)) * cosTheta + (y - (height / 2)) * sinTheta;
                double yPrime = -(x - (width / 2)) * sinTheta + (y - (height / 2)) * cosTheta;

                // Calculate the element of the kernel
                kernel[x, y] = Math.Exp(-(Math.Pow(xPrime, 2) / twoSigmaXSquared + Math.Pow(yPrime, 2) / twoSigmaYSquared)) * Math.Cos(2 * Math.PI * xPrime / lambda + psi);
            }
        }

        return kernel;
    }
}

使用方法：

Bitmap inputImage = new Bitmap("input.jpg");

GaborFilter filter = new GaborFilter(10, 0, 0, 1, 0.8);
Bitmap outputImage = filter.Apply(inputImage);

outputImage.Save("output.jpg");