C#简单数字图像处理程序
C#编写的简单数字图像处理程序，数字图像处理的平时成绩和编程作业竟然占50%，那就把最近做的事写个札记吧。
先放个最终做成提交的效果看看：
1.直方图均衡化
2.算子锐化
3.空域增强
一、要达到的目的和效果
1.打开，保存图片；
2.获取图像灰度值，图像坐标；
3.进行线性变换，直方图均衡化处理；
4.直方图变换增强，以及各种滤波处理；
5.图像锐化（Kirsch,Laplace,sobel等算子）。
二、编程环境及语言
C#-WindowsForm-VS2015
三、图标
最近发现了一个完全免费的矢量图标网站阿里妈妈iconfont，超级好用。
当然也可以自己动手画一个
四、创建窗体
1.先建一个C#Windows窗体应用程序，设置好保存路径和项目名称；
2.打开工具箱，找到menuscript，加到窗体中，依次填写菜单以及子菜单的名称，菜单里将完成主要的图像处理操作；
3.因为要显示处理前后的图片，所以再添加两个picturebox控件，可以设置停靠模式为stretchImage；再加两个groupbox，每个groupbox里添加label和textbox控件，用来显示图像灰度值及坐标，这样窗体基本搭建完成，还是挺简单的。
五、主要代码
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Drawing.Imaging;
using System.Linq;
using System.Text;
using System.Windows.Forms;
namespace text1
{
public partial class ImageEnhancement : Form
{
public ImageEnhancement()
{
InitializeComponent();
}
Bitmap bitmap;
int iw, ih;
//打开文件
private void 打开ToolStripMenuItem_Click(object sender, EventArgs e)
{
pictureBox1.Image = null;//先设置两个picturebox为空
pictureBox2.Image = null;
//使用 OpenFileDialog类打开图片
OpenFileDialog open = new OpenFileDialog();
open.Filter = "图像文件(*.bmp;*.jpg;*gif;*png;*.tif;*.wmf)|"
+ "*.bmp;*jpg;*gif;*png;*.tif;*.wmf";
if (open.ShowDialog() == DialogResult.OK)
{
try
{
bitmap = (Bitmap)Image.FromFile(open.FileName);
}
catch (Exception exp) { MessageBox.Show(exp.Message); }
pictureBox1.Refresh();
pictureBox1.Image = bitmap;
label6.Text = "原图";
iw = bitmap.Width;
ih = bitmap.Height;
}
}
//保存文件
private void 保存ToolStripMenuItem_Click(object sender, EventArgs e)
{
string str;
SaveFileDialog saveFileDialog1 = new SaveFileDialog();
saveFileDialog1.Filter = "图像文件(*.BMP)|*.BMP|All File(*.*)|*.*";
saveFileDialog1.ShowDialog();
str = saveFileDialog1.FileName;
pictureBox2.Image.Save(str);
}
//退出
private void 退出ToolStripMenuItem_Click(object sender, EventArgs e)
{
this.Close();
}
private void label5_Click(object sender, EventArgs e)
{
}
//读取灰度值及坐标
private void pictureBox1_MouseDown(object sender, MouseEventArgs e)
{
Color pointRGB = bitmap.GetPixel(e.X, e.Y);
textBox1.Text = pointRGB.R.ToString();
textBox2.Text = pointRGB.G.ToString();
textBox3.Text = pointRGB.B.ToString();
textBox4.Text = e.X.ToString();
textBox5.Text = e.Y.ToString();
int a = int.Parse(textBox1.Text);
}
//线性变换部分
private void linearPO_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
linearPOForm linearForm = new linearPOForm();
if (linearForm.ShowDialog() == DialogResult.OK)
{
Rectangle rect = new Rectangle(0, 0, bitmap.Width, bitmap.Height);
System.Drawing.Imaging.BitmapData bmpData = bitmap.LockBits(rect,
System.Drawing.Imaging.ImageLockMode.ReadWrite,
bitmap.PixelFormat);
IntPtr ptr = bmpData.Scan0;
//int bytes = bitmap.Width *;
}
}
}
private void textBox4_TextChanged(object sender, EventArgs e)
{
}
private void label3_Click(object sender, EventArgs e)
{
}
//对比度扩展
private void 对比度扩展ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
strechDialog dialog = new strechDialog();
if (dialog.ShowDialog() == DialogResult.OK)
{
this.Text = " 图像增强 对比度扩展 ";
Bitmap bm = new Bitmap(pictureBox1.Image);
int x1 = Convert.ToInt32(dialog.getX01);
int y1 = Convert.ToInt32(dialog.getY01);
int x2 = Convert.ToInt32(dialog.getX02);
int y2 = Convert.ToInt32(dialog.getY02);
//计算灰度映射表
int[] pixMap = pixelsMap(x1, y1, x2, y2);
//线性拉伸
bm = stretch(bm, pixMap, iw, ih);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = "对比度扩展结果";
}
}
}
//计算灰度映射表
public int[] pixelsMap(int x1, int y1, int x2, int y2)
{
int[] pMap = new int[256]; //映射表
if (x1 > 0)
{
double k1 = y1 / x1; //第1段斜率k1
//按第1段斜率k1线性变换
for (int i = 0; i <= x1; i++)
pMap[i] = (int)(k1 * i);
}
double k2 = (y2 - y1) / (x2 - x1); //第2段斜率k2
//按第2段斜率k2线性变换
for (int i = x1 + 1; i <= x2; i++)
if (x2 != x1)
pMap[i] = y1 + (int)(k2 * (i - x1));
else
pMap[i] = y1;
if (x2 < 255)
{
double k3 = (255 - y2) / (255 - x2);//第2段斜率k2
//按第3段斜率k3线性变换
for (int i = x2 + 1; i < 256; i++)
pMap[i] = y2 + (int)(k3 * (i - x2));
}
return pMap;
}
//对比度扩展函数
public Bitmap stretch(Bitmap bm, int[] map, int iw, int ih)
{
Color c = new Color();
int r, g, b;
for (int j = 0; j < ih; j++)
{
for (int i = 0; i < iw; i++)
{
c = bm.GetPixel(i, j);
r = map[c.R];
g = map[c.G];
b = map[c.B];
if (r >= 255) r = 255;
if (r < 0) r = 0;
if (g >= 255) g = 255;
if (g < 0) g = 0;
if (b >= 255) b = 255;
if (b < 0) b = 0;
bm.SetPixel(i, j, Color.FromArgb(r, g, b));
}
}
return bm;
}
private void 直方图均衡化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
this.Text = " 图像增强 直方图均衡化";
Bitmap bm = new Bitmap(pictureBox1.Image);
//获取直方图
int[] hist = gethist(bm, iw, ih);
//直方图均匀化
bm = histequal(bm, hist, iw, ih);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = "直方图均衡化结果";
flag = true;
}
}
bool flag = false; //直方图均衡化标志
//显示直方图
private void 显示直方图ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (flag)
{
Bitmap b1 = new Bitmap(pictureBox1.Image);
Bitmap b2 = new Bitmap(pictureBox2.Image);
int[] hist1 = gethist(b1, iw, ih);
int[] hist2 = gethist(b2, iw, ih);
drawHist(hist1, hist2);
}
}
//获取直方图
public int[] gethist(Bitmap bm, int iw, int ih)
{
int[] h = new int[256];
for (int j = 0; j < ih; j++)
{
for (int i = 0; i < iw; i++)
{
int grey = (bm.GetPixel(i, j)).R;
h[grey]++;
}
}
return h;
}
//直方图均衡化
public Bitmap histequal(Bitmap bm, int[] hist, int iw, int ih)
{
Color c = new Color();
double p = (double)255 / (iw * ih);
double[] sum = new double[256];
int[] outg = new int[256];
int r, g, b;
sum[0] = hist[0];
for (int i = 1; i < 256; i++)
sum[i] = sum[i - 1] + hist[i];
//灰度变换:i-->outg[i]
for (int i = 0; i < 256; i++)
outg[i] = (int)(p * sum[i]);
for (int j = 0; j < ih; j++)
{
for (int i = 0; i < iw; i++)
{
r = (bm.GetPixel(i, j)).R;
g = (bm.GetPixel(i, j)).G;
b = (bm.GetPixel(i, j)).B;
c = Color.FromArgb(outg[r], outg[g], outg[b]);
bm.SetPixel(i, j, c);
}
}
return bm;
}
public void drawHist(int[] h1, int[] h2)
{
//画原图直方图------------------------------------------
Graphics g = pictureBox1.CreateGraphics();
Pen pen1 = new Pen(Color.Blue);
g.Clear(this.BackColor);
//找出最大的数,进行标准化.
int maxn = h1[0];
for (int i = 1; i < 256; i++)
if (maxn < h1[i])
maxn = h1[i];
for (int i = 0; i < 256; i++)
h1[i] = h1[i] * 250 / maxn;
g.FillRectangle(new SolidBrush(Color.White), 0, 0, 255, 255);
pen1.Color = Color.Red;
for (int i = 0; i < 256; i++)
g.DrawLine(pen1, i, 255, i, 255 - h1[i]);
g.DrawString("" + maxn, this.Font, new SolidBrush(Color.Blue), 0, 0);
label6.Text = "原图直方图";
//画均衡化后直方图------------------------------------------
g = pictureBox2.CreateGraphics();
pen1 = new Pen(Color.Blue);
g.Clear(this.BackColor);
//找出最大的数,进行标准化.
maxn = h2[0];
for (int i = 1; i < 256; i++)
if (maxn < h2[i])
maxn = h2[i];
for (int i = 0; i < 256; i++)
h2[i] = h2[i] * 250 / maxn;
g.FillRectangle(new SolidBrush(Color.White), 0, 0, 255, 255);
pen1.Color = Color.Red;
for (int i = 0; i < 256; i++)
g.DrawLine(pen1, i, 255, i, 255 - h2[i]);
g.DrawString("" + maxn, this.Font, new SolidBrush(Color.Blue), 0, 0);
label7.Text = "均衡化后直方图";
flag = false;
}
private void 阈值滤波ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
this.Text = "图像增强 阈值滤波";
Bitmap bm = new Bitmap(pictureBox1.Image);
//阈值滤波
bm = threshold(bm, iw, ih);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = "阈值滤波结果";
}
}
//3×3阈值滤波
public Bitmap threshold(Bitmap bm, int iw, int ih)
{
Bitmap obm = new Bitmap(pictureBox1.Image);
int avr, //灰度平均
sum, //灰度和
num = 0, //计数器
nT = 4, //计数器阈值
T = 50; //阈值
int pij, pkl, //(i,j),(i+k,j+l)处灰度值
err; //误差
for (int j = 1; j < ih - 1; j++)
{
for (int i = 1; i < iw - 1; i++)
{
//取3×3块的9个象素, 求和
sum = 0;
for (int k = -1; k < 2; k++)
{
for (int l = -1; l < 2; l++)
{
if ((k != 0) || (l != 0))
{
pkl = (bm.GetPixel(i + k, j + l)).R;
pij = (bm.GetPixel(i, j)).R;
err = Math.Abs(pkl - pij);
sum = sum + pkl;
if (err > T) num++;
}
}
}
avr = (int)(sum / 8.0f); //平均值
if (num > nT)
obm.SetPixel(i, j, Color.FromArgb(avr, avr, avr));
}
}
return obm;
}
private void 均值滤波ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
this.Text = "数字图像处理";
Bitmap bm = new Bitmap(pictureBox1.Image);
bm = average(bm, iw, ih);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = "均值滤波结果";
}
}
//均值滤波
public Bitmap average(Bitmap bm, int iw, int ih)
{
Bitmap obm = new Bitmap(pictureBox1.Image);
for (int j = 1; j < ih - 1; j++)
{
for (int i = 1; i < iw - 1; i++)
{
int avr;
int avr1;
int avr2;
int sum = 0;
int sum1 = 0;
int sum2 = 0;
for (int k = -1; k <= 1; k++)
{
for (int l = -1; l <= 1; l++)
{
sum = sum + (bm.GetPixel(i + k, j + 1).R);
sum1 = sum1 + (bm.GetPixel(i + k, j + 1).G);
sum2 = sum2 + (bm.GetPixel(i + k, j + 1).B);
}
}
avr = (int)(sum / 9.0f);
avr1 = (int)(sum1 / 9.0f);
avr2 = (int)(sum2 / 9.0f);
obm.SetPixel(i, j, Color.FromArgb(avr, avr1, avr2));
}
}
return obm;
}
private void 中值滤波ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
this.Text = "图像增强 中值滤波";
Bitmap bm = new Bitmap(pictureBox1.Image);
int num =3;
//中值滤波
bm = median(bm, iw, ih, num);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label2.Location = new Point(370, 280);
if (num == 1) label7.Text = "1X5窗口滤波结果";
else if (num == 2) label7.Text = "5X1窗口滤波结果";
else if (num == 3) label7.Text = "5X5窗口滤波结果";
}
}
//中值滤波方法
public Bitmap median(Bitmap bm, int iw, int ih, int n)
{
Bitmap obm = new Bitmap(pictureBox1.Image);
for (int j = 2; j < ih - 2; j++)
{
int[] dt;
int[] dt1;
int[] dt2;
for (int i = 2; i < iw - 2; i++)
{
int m = 0, r = 0, r1 = 0, r2 = 0, a = 0, b = 0;
if (n == 3)
{
dt = new int[25];
dt1 = new int[25];
dt2 = new int[25];
//取5×5块的25个象素
for (int k = -2; k < 3; k++)
{
for (int l = -2; l < 3; l++)
{
//取(i+k,j+l)处的象素，赋于数组dt
dt[m] = (bm.GetPixel(i + k, j + l)).R;
dt1[a] = (bm.GetPixel(i + k, j + l)).G;
dt2[b] = (bm.GetPixel(i + k, j + l)).B;
m++;
a++;
b++;
}
}
//冒泡排序,输出中值
r = median_sorter(dt, 25); //中值
r1 = median_sorter(dt1, 25);
r2 = median_sorter(dt2, 25);
}
else if (n == 1)
{
dt = new int[5];
//取1×5窗口5个像素
dt[0] = (bm.GetPixel(i, j - 2)).R;
dt[1] = (bm.GetPixel(i, j - 1)).R;
dt[2] = (bm.GetPixel(i, j)).R;
dt[3] = (bm.GetPixel(i, j + 1)).R;
dt[4] = (bm.GetPixel(i, j + 2)).R;
r = median_sorter(dt, 5); //中值
dt1 = new int[5];
//取1×5窗口5个像素
dt1[0] = (bm.GetPixel(i, j - 2)).G;
dt1[1] = (bm.GetPixel(i, j - 1)).G;
dt1[2] = (bm.GetPixel(i, j)).G;
dt1[3] = (bm.GetPixel(i, j + 1)).G;
dt1[4] = (bm.GetPixel(i, j + 2)).G;
r1 = median_sorter(dt1, 5); //中值
dt2 = new int[5];
//取1×5窗口5个像素
dt2[0] = (bm.GetPixel(i, j - 2)).B;
dt2[1] = (bm.GetPixel(i, j - 1)).B;
dt2[2] = (bm.GetPixel(i, j)).B;
dt2[3] = (bm.GetPixel(i, j + 1)).B;
dt2[4] = (bm.GetPixel(i, j + 2)).B;
r2 = median_sorter(dt2, 5); //中值
}
else if (n == 2)
{
dt = new int[5];
//取5×1窗口5个像素
dt[0] = (bm.GetPixel(i - 2, j)).R;
dt[1] = (bm.GetPixel(i - 1, j)).R;
dt[2] = (bm.GetPixel(i, j)).R;
dt[3] = (bm.GetPixel(i + 1, j)).R;
dt[4] = (bm.GetPixel(i + 2, j)).R;
r = median_sorter(dt, 5); //中值 dt = new int[5];
//取5×1窗口5个像素
dt1 = new int[5];
dt1[0] = (bm.GetPixel(i - 2, j)).G;
dt1[1] = (bm.GetPixel(i - 1, j)).G;
dt1[2] = (bm.GetPixel(i, j)).G;
dt1[3] = (bm.GetPixel(i + 1, j)).G;
dt1[4] = (bm.GetPixel(i + 2, j)).G;
r1 = median_sorter(dt1, 5); //中值
//取5×1窗口5个像素
dt2 = new int[5];
dt2[0] = (bm.GetPixel(i - 2, j)).B;
dt2[1] = (bm.GetPixel(i - 1, j)).B;
dt2[2] = (bm.GetPixel(i, j)).B;
dt2[3] = (bm.GetPixel(i + 1, j)).B;
dt2[4] = (bm.GetPixel(i + 2, j)).B;
r2 = median_sorter(dt2, 5); //中值
}
obm.SetPixel(i, j, Color.FromArgb(r, r1, r2)); //输出
}
}
return obm;
}
//冒泡排序,输出中值
public int median_sorter(int[] dt, int m)
{
int tem;
for (int k = m - 1; k >= 1; k--)
for (int l = 1; l <= k; l++)
if (dt[l - 1] > dt[l])
{
tem = dt[l];
dt[l] = dt[l - 1];
dt[l - 1] = tem;
}
return dt[(int)(m / 2)];
}
private void pictureBox1_Click(object sender, EventArgs e)
{
}
private void 图像锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
}
/*
* pix --待检测图像数组
* iw, ih --待检测图像宽高
* num --算子代号.1:Kirsch算子;2:Laplace算子;3:Prewitt算子;5:Sobel算子
*/
public Bitmap detect(Bitmap bm, int iw, int ih, int num)
{
Bitmap b1 = new Bitmap(pictureBox1.Image);
Color c = new Color();
int i, j, r;
int[,] inr = new int[iw, ih]; //红色分量矩阵
int[,] ing = new int[iw, ih]; //绿色分量矩阵
int[,] inb = new int[iw, ih]; //蓝色分量矩阵
int[,] gray = new int[iw, ih];//灰度图像矩阵
//转变为灰度图像矩阵
for (j = 0; j < ih; j++)
{
for (i = 0; i < iw; i++)
{
c = bm.GetPixel(i, j);
inr[i, j] = c.R;
ing[i, j] = c.G;
inb[i, j] = c.B;
gray[i, j] = (int)((c.R + c.G + c.B) / 3.0);
}
}
if (num == 1)//Kirsch
{
int[,] kir0 = {{ 5, 5, 5},
{-3, 0,-3},
{-3,-3,-3}},//kir0
kir1 = {{-3, 5, 5},
{-3, 0, 5},
{-3,-3,-3}},//kir1
kir2 = {{-3,-3, 5},
{-3, 0, 5},
{-3,-3, 5}},//kir2
kir3 = {{-3,-3,-3},
{-3, 0, 5},
{-3, 5, 5}},//kir3
kir4 = {{-3,-3,-3},
{-3, 0,-3},
{ 5, 5, 5}},//kir4
kir5 = {{-3,-3,-3},
{ 5, 0,-3},
{ 5, 5,-3}},//kir5
kir6 = {{ 5,-3,-3},
{ 5, 0,-3},
{ 5,-3,-3}},//kir6
kir7 = {{ 5, 5,-3},
{ 5, 0,-3},
{-3,-3,-3}};//kir7
//边缘检测
int[,] edge0 = new int[iw, ih];
int[,] edge1 = new int[iw, ih];
int[,] edge2 = new int[iw, ih];
int[,] edge3 = new int[iw, ih];
int[,] edge4 = new int[iw, ih];
int[,] edge5 = new int[iw, ih];
int[,] edge6 = new int[iw, ih];
int[,] edge7 = new int[iw, ih];
edge0 = edgeEnhance(gray, kir0, iw, ih);
edge1 = edgeEnhance(gray, kir1, iw, ih);
edge2 = edgeEnhance(gray, kir2, iw, ih);
edge3 = edgeEnhance(gray, kir3, iw, ih);
edge4 = edgeEnhance(gray, kir4, iw, ih);
edge5 = edgeEnhance(gray, kir5, iw, ih);
edge6 = edgeEnhance(gray, kir6, iw, ih);
edge7 = edgeEnhance(gray, kir7, iw, ih);
int[] tem = new int[8];
int max;
for (j 【转自：荷兰服务器 】= 0; j < ih; j++)
{
for (i = 0; i < iw; i++)
{
tem[0] = edge0[i, j];
tem[1] = edge1[i, j];
tem[2] = edge2[i, j];
tem[3] = edge3[i, j];
tem[4] = edge4[i, j];
tem[5] = edge5[i, j];
tem[6] = edge6[i, j];
tem[7] = edge7[i, j];
max = 0;
for (int k = 0; k < 8; k++)
if (tem[k] > max) max = tem[k];
if (max > 255) max = 255;
r = 255 - max;
b1.SetPixel(i, j, Color.FromArgb(r, r, r));
}
}
}
else if (num == 2) //Laplace
{
int[,] lap1 = {{ 1, 1, 1},
{ 1,-8, 1},
{ 1, 1, 1}};
/*byte[][] lap2 = {{ 0, 1, 0},
{ 1,-4, 1},
{ 0, 1, 0}}; */
//边缘增强
int[,] edge = edgeEnhance(gray, lap1, iw, ih);
for (j = 0; j < ih; j++)
{
for (i = 0; i < iw; i++)
{
r = edge[i, j];
if (r > 255) r = 255;
if (r < 0) r = 0;
c = Color.FromArgb(r, r, r);
b1.SetPixel(i, j, c);
}
}
}
else if (num == 3)//Prewitt
{
//Prewitt算子D_x模板
int[,] pre1 = {{ 1, 0,-1},
{ 1, 0,-1},
{ 1, 0,-1}};
//Prewitt算子D_y模板
int[,] pre2 = {{ 1, 1, 1},
{ 0, 0, 0},
{-1,-1,-1}};
int[,] edge1 = edgeEnhance(gray, pre1, iw, ih);
int[,] edge2 = edgeEnhance(gray, pre2, iw, ih);
for (j = 0; j < ih; j++)
{
for (i = 0; i < iw; i++)
{
r = Math.Max(edge1[i, j], edge2[i, j]);
if(r > 255) r = 255;
c = Color.FromArgb(r, r, r);
b1.SetPixel(i, j, c);
}
}
}
else if (num == 5) //Sobel
{
int[,] sob1 = {{ 1, 0,-1},
{ 2, 0,-2},
{ 1, 0,-1}};
int[,] sob2 = {{ 1, 2, 1},
{ 0, 0, 0},
{-1,-2,-1}},
int[,] edge1 = edgeEnhance(gray, sob1, iw, ih);
int[,] edge2 = edgeEnhance(gray, sob2, iw, ih);
for (j = 0; j < ih; j++)
{
for (i = 0; i < iw; i++)
{
r = Math.Max(edge1[i, j], edge2[i, j]);
if(r > 255) r = 255;
c = Color.FromArgb(r, r, r);
b1.SetPixel(i, j, c);
}
}
}
return b1;
}
private void kirsch算子锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
// this.Text = " 图像 - 图像锐化 - Kirsch算子";
Bitmap bm = new Bitmap(pictureBox1.Image);
//1: Kirsch锐化
bm = detect(bm, iw, ih, 1)
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = " Kirsch算子 锐化结果";
}
}
public int[,] edgeEnhance(int[,] ing, int[,] tmp, int iw, int ih)
{
int[,] ed = new int[iw, ih];
for (int j = 1; j < ih - 1; j++)
{
for (int i = 1; i < iw - 1; i++)
{
ed[i, j] = Math.Abs(tmp[0, 0] * ing[i - 1, j - 1]
+ tmp[0, 1] * ing[i - 1, j] + tmp[0, 2] * ing[i - 1, j + 1]
+ tmp[1, 0] * ing[i, j - 1] + tmp[1, 1] * ing[i, j]
+ tmp[1, 2] * ing[i, j + 1] + tmp[2, 0] * ing[i + 1, j - 1]
+ tmp[2, 1] * ing[i + 1, j] + tmp[2, 2] * ing[i + 1, j + 1]);
}
}
return ed;
}
//Laplace算子
private void laplace算子锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
Bitmap bm = new Bitmap(pictureBox1.Image);
//2: Laplace锐化
bm = detect(bm, iw, ih, 2);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = "Laplace算子 锐化结果";
}
}
//Prewitt算子
private void prewitt算子锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
Bitmap bm = new Bitmap(pictureBox1.Image);
//3:Prewitt锐化
bm = detect(bm, iw, ih, 3);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label2.Location = new Point(390, 280);
label7.Text = " Prewitt算子 锐化结果";
}
}
//Roberts算子
private void roberts算子锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
Bitmap bm = new Bitmap(pictureBox1.Image);
//Robert边缘检测
bm = robert(bm, iw, ih);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label2.Location = new Point(390, 280);
label7.Text = "Roberts算子 锐化结果";
}
}
//roberts算法
public Bitmap robert(Bitmap bm, int iw, int ih)
{
int r, r0, r1, r2, r3, g, g0, g1, g2, g3, b, b0, b1, b2, b3;
Bitmap obm = new Bitmap(pictureBox1.Image);
int[,] inr = new int[iw, ih];//红色分量矩阵
int[,] ing = new int[iw, ih];//绿色分量矩阵
int[,] inb = new int[iw, ih];//蓝色分量矩阵
int[,] gray = new int[iw, ih];//灰度图像矩阵
for (int j = 1; j < ih - 1; j++)
{
for (int i = 1; i < iw - 1; i++)
{
r0 = (bm.GetPixel(i, j)).R;
r1 = (bm.GetPixel(i, j + 1)).R;
r2 = (bm.GetPixel(i + 1, j)).R;
r3 = (bm.GetPixel(i + 1, j + 1)).R;
r = (int)Math.Sqrt((r0 - r3) * (r0 - r3) + (r1 - r2) * (r1 - r2));
g0 = (bm.GetPixel(i, j)).G;
g1 = (bm.GetPixel(i, j + 1)).G;
g2 = (bm.GetPixel(i + 1, j)).G;
g3 = (bm.GetPixel(i + 1, j + 1)).G;
g = (int)Math.Sqrt((g0 - g3) * (g0 - g3) + (g1 - g2) * (g1 - g2));
b0 = (bm.GetPixel(i, j)).B;
b1 = (bm.GetPixel(i, j + 1)).B;
b2 = (bm.GetPixel(i + 1, j)).B;
b3 = (bm.GetPixel(i + 1, j + 1)).B;
b = (int)Math.Sqrt((b0 - b3) * (b0 - b3)
+ (b1 - b2) * (b1 - b2));
if (r < 0)
r = 0; //黑色，边缘点
if (r > 255)
r = 255;
obm.SetPixel(i, j, Color.FromArgb(r, r, r));
}
}
return obm;
}
//Sobel算子
private void sobel算子锐化ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
Bitmap bm = new Bitmap(pictureBox1.Image);
//5: Sobel锐化
bm = detect(bm, 256, 256, 5);
pictureBox2.Refresh();
pictureBox2.Image = bm;
label7.Text = " Sobel算子 锐化结果";
}
}
private void 低通滤波ToolStripMenuItem_Click(object sender, EventArgs e)
{
if (bitmap != null)
{
Bitmap bm = new Bitmap(pictureBox1.Image);
int num ;
for (num = 1; num < 4; num++)
{
//低通滤波
bm = lowpass(bm, iw, ih, num);
pictureBox2.Refresh();
pictureBox2.Image = bm;
if (num == 1) label7.Text = "1*5模板低通滤波结果";
else if (num == 2) label7.Text = "5*1模板低通滤波结果";
else if (num == 3) label7.Text = "5*5模板低通滤波结果";
}
}
}
//3×3低通滤波方法
public Bitmap lowpass(Bitmap bm, int iw, int ih, int n)
{
Bitmap obm = new Bitmap(pictureBox1.Image);
int[,] h;
//定义扩展输入图像矩阵
int[,] ex_inpix = exinpix(bm, iw, ih);
//低通滤波
for (int j = 1; j < ih + 1; j++)
{
for (int i = 1; i < iw + 1; i++)
{
int r = 0, sum = 0;
//低通模板
h = low_matrix(n);
//求3×3窗口9个像素加权和
for (int k = -1; k < 2; k++)
for (int l = -1; l < 2; l++)
sum = sum + h[k + 1, l + 1] * ex_inpix[i + k, j + l];
if (n == 1)
r = (int)(sum / 9); //h1平均值
else if (n == 2)
r = (int)(sum / 10); //h2
else if (n == 3)
r = (int)(sum / 16); //h3
obm.SetPixel(i - 1, j - 1, Color.FromArgb(r, r, r)); //输出
}
}
return obm;
}
//定义扩展输入图像矩阵
public int[,] exinpix(Bitmap bm, int iw, int ih)
{
int[,] ex_inpix = new int[iw + 2, ih + 2];
//获取非边界灰度值
for (int j = 0; j < ih; j++)
for (int i = 0; i < iw; i++)
ex_inpix[i + 1, j + 1] = (bm.GetPixel(i, j)).R;
//四角点处理
ex_inpix[0, 0] = ex_inpix[1, 1];
ex_inpix[0, ih + 1] = ex_inpix[1, ih];
ex_inpix[iw + 1, 0] = ex_inpix[iw, 1];
ex_inpix[iw + 1, ih + 1] = ex_inpix[iw, ih];
//上下边界处理
for (int j = 1; j < ih + 1; j++)
{
ex_inpix[0, j] = ex_inpix[1, j]; //上边界
ex_inpix[iw + 1, j] = ex_inpix[iw, j];//下边界
}
//左右边界处理
for (int i = 1; i < iw + 1; i++)
{
ex_inpix[i, 0] = ex_inpix[i, 1]; //左边界
ex_inpix[i, ih + 1] = ex_inpix[i, ih];//右边界
}
return ex_inpix;
}
//低通滤波模板
public int[,] low_matrix(int n)
{
int[,] h = new int[3, 3];
if (n == 1) //h1
{
h[0, 0] = 1; h[0, 1] = 1; h[0, 2] = 1;
h[1, 0] = 1; h[1, 1] = 1; h[1, 2] = 1;
h[2, 0] = 1; h[2, 1] = 1; h[2, 2] = 1;
}
else if (n == 2)//h2
{
h[0, 0] = 1; h[0, 1] = 1; h[0, 2] = 1;
h[1, 0] = 1; h[1, 1] = 2; h[1, 2] = 1;
h[2, 0] = 1; h[2, 1] = 1; h[2, 2] = 1;
}
else if (n == 3)//h3
{
h[0, 0] = 1; h[0, 1] = 2; h[0, 2] = 1;
h[1, 0] = 2; h[1, 1] = 4; h[1, 2] = 2;
h[2, 0] = 1; h[2, 1] = 2; h[2, 2] = 1;
}
return h;
}
}
}
六、参考书籍
《C#数字图像处理算法典型实例》
以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持海外IDC网。