浅谈Visual C#进行图像处理（读取、保存以及对像素的访问）

这里之所以说“浅谈”是因为我这里只是简单的介绍如何使用Visual C#进行图像的读入、保存以及对像素的访问。而不涉及太多的算法。

一、读取图像

在Visual C#中我们可以使用一个Picture Box控件来显示图片，如下：

代码如下:

private void btnOpenImage_Click(object sender, EventArgs e)
{
    OpenFileDialog ofd = new OpenFileDialog();
    ofd.Filter = "BMP Files(*.bmp)|*.bmp|JPG Files(*.jpg;*.jpeg)|*.jpg;*.jpeg|All Files(*.*)|*.*";
    ofd.CheckFileExists = true;
    ofd.CheckPathExists = true;
    if (ofd.ShowDialog() == DialogResult.OK)
    {
        //pbxShowImage.ImageLocation = ofd.FileName;
        bmp = new Bitmap(ofd.FileName);
        if (bmp==null)
        {
            MessageBox.Show("加载图片失败!", "错误");
            return;
        }
        pbxShowImage.Image = bmp;
        ofd.Dispose();
    }
}

其中bmp为类的一个对象：private Bitmap bmp=null;
在使用Bitmap类和BitmapData类之前，需要使用using System.Drawing.Imaging;

二、保存图像

代码如下:

private void btnSaveImage_Click(object sender, EventArgs e)
{
    if (bmp == null) return;
    SaveFileDialog sfd = new SaveFileDialog();
    sfd.Filter = "BMP Files(*.bmp)|*.bmp|JPG Files(*.jpg;*.jpeg)|*.jpg;*.jpeg|All Files(*.*)|*.*";
    if (sfd.ShowDialog() == DialogResult.OK)
    {
        pbxShowImage.Image.Save(sfd.FileName);
        MessageBox.Show("保存成功!","提示");
        sfd.Dispose();
    }
}

三、对像素的访问

我们可以来建立一个GrayBitmapData类来做相关的处理。整个类的程序如下：

代码如下:

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Drawing.Imaging;
using System.Windows.Forms;
namespace ImageElf
{
    class GrayBitmapData
    {
        public byte[,] Data;//保存像素矩阵
        public int Width;//图像的宽度
        public int Height;//图像的高度
        public GrayBitmapData()
        {
            this.Width = 0;
            this.Height = 0;
            this.Data = null;
        }
        public GrayBitmapData(Bitmap bmp)
        {
            BitmapData bmpData = bmp.LockBits(new Rectangle(0, 0, bmp.Width, bmp.Height), ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);
            this.Width = bmpData.Width;
            this.Height = bmpData.Height;
            Data = new byte[Height, Width];
            unsafe
            {
                byte* ptr = (byte*)bmpData.Scan0.ToPointer();
                for (int i = 0; i < Height; i++)
                {
                    for (int j = 0; j < Width; j++)
                    {
    //将24位的RGB彩色图转换为灰度图
                        int temp = (int)(0.114 * (*ptr++)) + (int)(0.587 * (*ptr++))+(int)(0.299 * (*ptr++));
                        Data[i, j] = (byte)temp;
                    }
                    ptr += bmpData.Stride - Width * 3;//指针加上填充的空白空间
                }
            }
            bmp.UnlockBits(bmpData);
        }
        public GrayBitmapData(string path)
            : this(new Bitmap(path))
        {
        }
        public Bitmap ToBitmap()
        {
            Bitmap bmp=new Bitmap(Width,Height,PixelFormat.Format24bppRgb);
            BitmapData bmpData=bmp.LockBits(new Rectangle(0,0,Width,Height),ImageLockMode.WriteOnly,PixelFormat.Format24bppRgb);
            unsafe
            {
                byte* ptr=(byte*)bmpData.Scan0.ToPointer();
                for(int i=0;i<Height;i++)
                {
                    for(int j=0;j<Width;j++)
                    {
                        *(ptr++)=Data[i,j];
                        *(ptr++)=Data[i,j];
                        *(ptr++)=Data[i,j];
                    }
                    ptr+=bmpData.Stride-Width*3;
                }
            }
            bmp.UnlockBits(bmpData);
            return bmp;
        }
        public void ShowImage(PictureBox pbx)
        {
            Bitmap b = this.ToBitmap();
            pbx.Image = b;
            //b.Dispose();
        }
        public void SaveImage(string path)
        {
            Bitmap b=ToBitmap();
            b.Save(path);
            //b.Dispose();
        }
//均值滤波
        public void AverageFilter(int windowSize)
        {
            if (windowSize % 2 == 0)
            {
                return;
            }
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    int sum = 0;
                    for (int g = -(windowSize - 1) / 2; g <= (windowSize - 1) / 2; g++)
                    {
                        for (int k = -(windowSize - 1) / 2; k <= (windowSize - 1) / 2; k++)
                        {
                            int a = i + g, b = j + k;
                            if (a < 0) a = 0;
                            if (a > Height - 1) a = Height - 1;
                            if (b < 0) b = 0;
                            if (b > Width - 1) b = Width - 1;
                            sum += Data[a, b];
                        }
                    }
                    Data[i,j]=(byte)(sum/(windowSize*windowSize));
                }
            }
        }
//中值滤波
        public void MidFilter(int windowSize)
        {
            if (windowSize % 2 == 0)
            {
                return;
            }
            int[] temp = new int[windowSize * windowSize];
            byte[,] newdata = new byte[Height, Width];
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    int n = 0;
                    for (int g = -(windowSize - 1) / 2; g <= (windowSize - 1) / 2; g++)
                    {
                        for (int k = -(windowSize - 1) / 2; k <= (windowSize - 1) / 2; k++)
                        {
                            int a = i + g, b = j + k;
                            if (a < 0) a = 0;
                            if (a > Height - 1) a = Height - 1;
                            if (b < 0) b = 0;
                            if (b > Width - 1) b = Width - 1;
                            temp[n++]= Data[a, b];
                        }
                    }
                    newdata[i, j] = GetMidValue(temp,windowSize*windowSize);
                }
            }
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    Data[i, j] = newdata[i, j];
                }
            }
        }
//获得一个向量的中值
        private byte GetMidValue(int[] t, int length)
        {
            int temp = 0;
            for (int i = 0; i < length - 2; i++)
            {
                for (int j = i + 1; j < length - 1; j++)
                {
                    if (t[i] > t[j])
                    {
                        temp = t[i];
                        t[i] = t[j];
                        t[j] = temp;
                    }
                }
            }
            return (byte)t[(length - 1) / 2];
        }
//一种新的滤波方法，是亮的更亮、暗的更暗
        public void NewFilter(int windowSize)
        {
            if (windowSize % 2 == 0)
            {
                return;
            }
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    int sum = 0;
                    for (int g = -(windowSize - 1) / 2; g <= (windowSize - 1) / 2; g++)
                    {
                        for (int k = -(windowSize - 1) / 2; k <= (windowSize - 1) / 2; k++)
                        {
                            int a = i + g, b = j + k;
                            if (a < 0) a = 0;
                            if (a > Height - 1) a = Height - 1;
                            if (b < 0) b = 0;
                            if (b > Width - 1) b = Width - 1;
                            sum += Data[a, b];
                        }
                    }
                    double avg = (sum+0.0) / (windowSize * windowSize);
                    if (avg / 255 < 0.5)
                    {
                        Data[i, j] = (byte)(2 * avg / 255 * Data[i, j]);
                    }
                    else
                    {
                        Data[i,j]=(byte)((1-2*(1-avg/255.0)*(1-Data[i,j]/255.0))*255);
                    }
                }
            }
        }
//直方图均衡
        public void HistEqual()
        {
            double[] num = new double[256] ;
            for(int i=0;i<256;i++) num[i]=0;
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    num[Data[i, j]]++;
                }
            }
            double[] newGray = new double[256];
            double n = 0;
            for (int i = 0; i < 256; i++)
            {
                n += num[i];
                newGray[i] = n * 255 / (Height * Width);
            }
            for (int i = 0; i < Height; i++)
            {
                for (int j = 0; j < Width; j++)
                {
                    Data[i,j]=(byte)newGray[Data[i,j]];
                }
            }
        }
}
}

在GrayBitmapData类中，只要我们对一个二维数组Data进行一系列的操作就是对图片的操作处理。在窗口上，我们可以使用
一个按钮来做各种调用：

代码如下:

//均值滤波
private void btnAvgFilter_Click(object sender, EventArgs e)
{
    if (bmp == null) return;
    GrayBitmapData gbmp = new GrayBitmapData(bmp);
    gbmp.AverageFilter(3);
    gbmp.ShowImage(pbxShowImage);
}
//转换为灰度图
private void btnToGray_Click(object sender, EventArgs e)
{
    if (bmp == null) return;
    GrayBitmapData gbmp = new GrayBitmapData(bmp);
    gbmp.ShowImage(pbxShowImage);
}

四、总结

在Visual c#中对图像进行处理或访问，需要先建立一个Bitmap对象，然后通过其LockBits方法来获得一个BitmapData类的对象，然后通过获得其像素数据的首地址来对Bitmap对象的像素数据进行操作。当然，一种简单但是速度慢的方法是用Bitmap类的GetPixel和SetPixel方法。其中BitmapData类的Stride属性为每行像素所占的字节。