C#实现高斯模糊（图像处理）

在C#中实现高斯模糊，可以使用System.Drawing库。高斯模糊是一种基于高斯函数的滤波器，它可以有效地平滑图像。以下是详细的步骤，包括生成高斯核并应用到图像上的代码示例。

1. 生成高斯核

首先，我们需要编写一个方法来生成高斯核。

public static float[,] CreateGaussianKernel(int length, float weight)
{
    float[,] kernel = new float[length, length];
    float sumTotal = 0;
    int kernelRadius = length / 2;
    float distance = 0;
    float calculatedEuler = 1.0f / (2.0f * (float)Math.PI * weight * weight);

    for (int filterY = -kernelRadius; filterY <= kernelRadius; filterY++)
    {
        for (int filterX = -kernelRadius; filterX <= kernelRadius; filterX++)
        {
            distance = ((filterX * filterX) + (filterY * filterY)) / (2 * (weight * weight));
            kernel[filterY + kernelRadius, filterX + kernelRadius] = calculatedEuler * (float)Math.Exp(-distance);
            sumTotal += kernel[filterY + kernelRadius, filterX + kernelRadius];
        }
    }

    for (int y = 0; y < length; y++)
    {
        for (int x = 0; x < length; x++)
        {
            kernel[y, x] = kernel[y, x] * (1.0f / sumTotal);
        }
    }

    return kernel;
}

2. 应用卷积滤波器

编写一个方法来应用卷积滤波器，将高斯核应用到图像上。

public static Bitmap ApplyConvolutionFilter(Bitmap sourceImage, float[,] kernel)
{
    int width = sourceImage.Width;
    int height = sourceImage.Height;
    BitmapData srcData = sourceImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb);
    Bitmap resultImage = new Bitmap(width, height);
    BitmapData resultData = resultImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb);

    int bytesPerPixel = 4;
    int stride = srcData.Stride;
    IntPtr srcScan0 = srcData.Scan0;
    IntPtr resultScan0 = resultData.Scan0;
    int kernelWidth = kernel.GetLength(1);
    int kernelHeight = kernel.GetLength(0);
    int kernelOffset = kernelWidth / 2;

    unsafe
    {
        byte* srcPtr = (byte*)srcScan0.ToPointer();
        byte* resultPtr = (byte*)resultScan0.ToPointer();

        for (int y = kernelOffset; y < height - kernelOffset; y++)
        {
            for (int x = kernelOffset; x < width - kernelOffset; x++)
            {
                float blue = 0.0f;
                float green = 0.0f;
                float red = 0.0f;

                for (int ky = -kernelOffset; ky <= kernelOffset; ky++)
                {
                    for (int kx = -kernelOffset; kx <= kernelOffset; kx++)
                    {
                        int pixelPos = ((y + ky) * stride) + ((x + kx) * bytesPerPixel);
                        blue += srcPtr[pixelPos] * kernel[ky + kernelOffset, kx + kernelOffset];
                        green += srcPtr[pixelPos + 1] * kernel[ky + kernelOffset, kx + kernelOffset];
                        red += srcPtr[pixelPos + 2] * kernel[ky + kernelOffset, kx + kernelOffset];
                    }
                }

                int resultPos = (y * stride) + (x * bytesPerPixel);
                resultPtr[resultPos] = (byte)Math.Min(Math.Max(blue, 0), 255);
                resultPtr[resultPos + 1] = (byte)Math.Min(Math.Max(green, 0), 255);
                resultPtr[resultPos + 2] = (byte)Math.Min(Math.Max(red, 0), 255);
                resultPtr[resultPos + 3] = 255; // Alpha channel
            }
        }
    }

    sourceImage.UnlockBits(srcData);
    resultImage.UnlockBits(resultData);

    return resultImage;
}

3. 使用高斯模糊处理图像

最后，编写一个程序来加载图像、应用高斯模糊滤波器，并保存处理后的图像。

using System;using System.Drawing;using System.Drawing.Imaging;
class Program
{
    static void Main()
    {
        // 加载原始图像
        Bitmap sourceImage = new Bitmap("path_to_your_image.jpg");

        // 创建高斯核
        float weight = 1.0f;
        int kernelSize = 5; // 核大小可以根据需要调整
        float[,] kernel = CreateGaussianKernel(kernelSize, weight);

        // 应用高斯模糊滤波器
        Bitmap resultImage = ApplyConvolutionFilter(sourceImage, kernel);

        // 保存处理后的图像
        resultImage.Save("path_to_save_filtered_image.jpg");
    }

    public static float[,] CreateGaussianKernel(int length, float weight)
    {
        float[,] kernel = new float[length, length];
        float sumTotal = 0;
        int kernelRadius = length / 2;
        float distance = 0;
        float calculatedEuler = 1.0f / (2.0f * (float)Math.PI * weight * weight);

        for (int filterY = -kernelRadius; filterY <= kernelRadius; filterY++)
        {
            for (int filterX = -kernelRadius; filterX <= kernelRadius; filterX++)
            {
                distance = ((filterX * filterX) + (filterY * filterY)) / (2 * (weight * weight));
                kernel[filterY + kernelRadius, filterX + kernelRadius] = calculatedEuler * (float)Math.Exp(-distance);
                sumTotal += kernel[filterY + kernelRadius, filterX + kernelRadius];
            }
        }

        for (int y = 0; y < length; y++)
        {
            for (int x = 0; x < length; x++)
            {
                kernel[y, x] = kernel[y, x] * (1.0f / sumTotal);
            }
        }

        return kernel;
    }

    public static Bitmap ApplyConvolutionFilter(Bitmap sourceImage, float[,] kernel)
    {
        int width = sourceImage.Width;
        int height = sourceImage.Height;
        BitmapData srcData = sourceImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.ReadOnly, PixelFormat.Format32bppArgb);
        Bitmap resultImage = new Bitmap(width, height);
        BitmapData resultData = resultImage.LockBits(new Rectangle(0, 0, width, height), ImageLockMode.WriteOnly, PixelFormat.Format32bppArgb);

        int bytesPerPixel = 4;
        int stride = srcData.Stride;
        IntPtr srcScan0 = srcData.Scan0;
        IntPtr resultScan0 = resultData.Scan0;
        int kernelWidth = kernel.GetLength(1);
        int kernelHeight = kernel.GetLength(0);
        int kernelOffset = kernelWidth / 2;

        unsafe
        {
            byte* srcPtr = (byte*)srcScan0.ToPointer();
            byte* resultPtr = (byte*)resultScan0.ToPointer();

            for (int y = kernelOffset; y < height - kernelOffset; y++)
            {
                for (int x = kernelOffset; x < width - kernelOffset; x++)
                {
                    float blue = 0.0f;
                    float green = 0.0f;
                    float red = 0.0f;

                    for (int ky = -kernelOffset; ky <= kernelOffset; ky++)
                    {
                        for (int kx = -kernelOffset; kx <= kernelOffset; kx++)
                        {
                            int pixelPos = ((y + ky) * stride) + ((x + kx) * bytesPerPixel);
                            blue += srcPtr[pixelPos] * kernel[ky + kernelOffset, kx + kernelOffset];
                            green += srcPtr[pixelPos + 1] * kernel[ky + kernelOffset, kx + kernelOffset];
                            red += srcPtr[pixelPos + 2] * kernel[ky + kernelOffset, kx + kernelOffset];
                        }
                    }

                    int resultPos = (y * stride) + (x * bytesPerPixel);
                    resultPtr[resultPos] = (byte)Math.Min(Math.Max(blue, 0), 255);
                    resultPtr[resultPos + 1] = (byte)Math.Min(Math.Max(green, 0), 255);
                    resultPtr[resultPos + 2] = (byte)Math.Min(Math.Max(red, 0), 255);
                    resultPtr[resultPos + 3] = 255; // Alpha channel
                }
            }
        }

        sourceImage.UnlockBits(srcData);
        resultImage.UnlockBits(resultData);

        return resultImage;
    }
}