C#验证码识别类完整实例

本文实例讲述了C#验证码识别类。分享给大家供大家参考。具体实现方法如下：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Drawing;
using System.Drawing.Imaging;
using System.Runtime.InteropServices;
namespace 验证码处理
{
class VerifyCode
{
public Bitmap bmpobj;
public VerifyCode(Bitmap pic)
{
bmpobj = new Bitmap(pic);  //转换为Format32bppRgb
}
/// <summary>
/// 根据RGB，计算灰度值
/// </summary>
/// <param name="posClr">Color值</param>
/// <returns>灰度值，整型</returns>
private int GetGrayNumColor(System.Drawing.Color posClr)
{
return (posClr.R * 19595 + posClr.G * 38469 + posClr.B * 7472) >> 16;
}
/// <summary>
/// 灰度转换,逐点方式
/// </summary>
public void GrayByPixels()
{
for (int i = 0; i < bmpobj.Height; i++)
{
for (int j = 0; j < bmpobj.Width; j++)
{
int tmpValue = GetGrayNumColor(bmpobj.GetPixel(j, i));
bmpobj.SetPixel(j, i, Color.FromArgb(tmpValue, tmpValue, tmpValue));
}
}
}
/// <summary>
/// 去图形边框
/// </summary>
/// <param name="borderWidth"></param>
public void ClearPicBorder(int borderWidth)
{
for (int i = 0; i < bmpobj.Height; i++)
{
for (int j = 0; j < bmpobj.Width; j++)
{
if (i < borderWidth || j < borderWidth || j > bmpobj.Width - 1 - borderWidth || i > bmpobj.Height - 1 - borderWidth)
bmpobj.SetPixel(j, i, Color.FromArgb(255, 255, 255));
}
}
}
/// <summary>
/// 灰度转换,逐行方式
/// </summary>
public void GrayByLine()
{
Rectangle rec = new Rectangle(0, 0, bmpobj.Width, bmpobj.Height);
BitmapData bmpData = bmpobj.LockBits(rec, ImageLockMode.ReadWrite, bmpobj.PixelFormat);// PixelFormat.Format32bppPArgb);
//  bmpData.PixelFormat = PixelFormat.Format24bppRgb;
IntPtr scan0 = bmpData.Scan0;
int len = bmpobj.Width * bmpobj.Height;
int[] pixels = new int[len];
Marshal.Copy(scan0, pixels, 0, len);
//对图片进行处理
int GrayValue = 0;
for (int i = 0; i < len; i++)
{
GrayValue = GetGrayNumColor(Color.FromArgb(pixels[i]));
pixels[i] = (byte)(Color.FromArgb(GrayValue, GrayValue, GrayValue)).ToArgb();  //Color转byte
}
bmpobj.UnlockBits(bmpData);
////输出
//GCHandle gch = GCHandle.Alloc(pixels, GCHandleType.Pinned);
//bmpOutput = new Bitmap(bmpobj.Width, bmpobj.Height, bmpData.Stride, bmpData.PixelFormat, gch.AddrOfPinnedObject());
//gch.Free();
}
/// <summary>
/// 得到有效图形并调整为可平均分割的大小
/// </summary>
/// <param name="dgGrayValue">灰度背景分界值</param>
/// <param name="CharsCount">有效字符数</param>
/// <returns></returns>
public void GetPicValidByValue(int dgGrayValue, int CharsCount)
{
int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
int posx2 = 0; int posy2 = 0;
for (int i = 0; i < bmpobj.Height; i++)  //找有效区
{
for (int j = 0; j < bmpobj.Width; j++)
{
int pixelValue = bmpobj.GetPixel(j, i).R;
if (pixelValue < dgGrayValue)  //根据灰度值
{
if (posx1 > j) posx1 = j;
if (posy1 > i) posy1 = i;
if (posx2 < j) posx2 = j;
if (posy2 < i) posy2 = i;
};
};
};
// 确保能整除
int Span = CharsCount - (posx2 - posx1 + 1) % CharsCount; //可整除的差额数
if (Span < CharsCount)
{
int leftSpan = Span / 2;  //分配到左边的空列 ，如span为单数,则右边比左边大1
if (posx1 > leftSpan)
posx1 = posx1 - leftSpan;
if (posx2 + Span - leftSpan < bmpobj.Width)
posx2 = posx2 + Span - leftSpan;
}
//复制新图
Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
}
/// <summary>
/// 得到有效图形,图形为类变量
/// </summary>
/// <param name="dgGrayValue">灰度背景分界值</param>
/// <param name="CharsCount">有效字符数</param>
/// <returns></returns>
public void GetPicValidByValue(int dgGrayValue)
{
int posx1 = bmpobj.Width; int posy1 = bmpobj.Height;
int posx2 = 0; int posy2 = 0;
for (int i = 0; i < bmpobj.Height; i++)  //找有效区
{
for (int j = 0; j < bmpobj.Width; j++)
{
int pixelValue = bmpobj.GetPixel(j, i).R;
if (pixelValue < dgGrayValue)  //根据灰度值
{
if (posx1 > j) posx1 = j;
if (posy1 > i) posy1 = i;
if (posx2 < j) posx2 = j;
if (posy2 < i) posy2 = i;
};
};
};
//复制新图
Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
bmpobj = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
}
/// <summary>
/// 得到有效图形,图形由外面传入
/// </summary>
/// <param name="dgGrayValue">灰度背景分界值</param>
/// <param name="CharsCount">有效字符数</param>
/// <returns></returns>
public Bitmap GetPicValidByValue(Bitmap singlepic, int dgGrayValue)
{
int posx1 = singlepic.Width; int posy1 = singlepic.Height;
int posx2 = 0; int posy2 = 0;
for (int i = 0; i < singlepic.Height; i++)  //找有效区
{
for (int j = 0; j < singlepic.Width; j++)
{
int pixelValue = singlepic.GetPixel(j, i).R;
if (pixelValue < dgGrayValue)  //根据灰度值
{
if (posx1 > j) posx1 = j;
if (posy1 > i) posy1 = i;
if (posx2 < j) posx2 = j;
if (posy2 < i) posy2 = i;
};
};
};
//复制新图
Rectangle cloneRect = new Rectangle(posx1, posy1, posx2 - posx1 + 1, posy2 - posy1 + 1);
return singlepic.Clone(cloneRect, singlepic.PixelFormat);
}
/// <summary>
/// 平均分割图片
/// </summary>
/// <param name="RowNum">水平上分割数</param>
/// <param name="ColNum">垂直上分割数</param>
/// <returns>分割好的图片数组</returns>
public Bitmap [] GetSplitPics(int RowNum,int ColNum)
{
if (RowNum == 0 || ColNum == 0)
return null;
int singW = bmpobj.Width / RowNum;
int singH = bmpobj.Height / ColNum;
Bitmap [] PicArray=new Bitmap[RowNum*ColNum];
Rectangle cloneRect;
for (int i = 0; i < ColNum; i++)  //找有效区
{
for (int j = 0; j < RowNum; j++)
{
cloneRect = new Rectangle(j*singW, i*singH, singW , singH);
PicArray[i*RowNum+j]=bmpobj.Clone(cloneRect, bmpobj.PixelFormat);//复制小块图
}
}
return PicArray;
}
/// <summary>
/// 返回灰度图片的点阵描述字串，1表示灰点，0表示背景
/// </summary>
/// <param name="singlepic">灰度图</param>
/// <param name="dgGrayValue">背前景灰色界限</param>
/// <returns></returns>
public string GetSingleBmpCode(Bitmap singlepic, int dgGrayValue)
{
Color piexl;
string code = "";
for (int posy = 0; posy < singlepic.Height; posy++)
for (int posx = 0; posx < singlepic.Width; posx++)
{
piexl = singlepic.GetPixel(posx, posy);
if (piexl.R < dgGrayValue)  // Color.Black )
code = code + "1";
else
code = code + "0";
}
return code;
}
/// <summary>
/// 得到灰度图像前景背景的临界值 最大类间方差法
/// </summary>
/// <returns>前景背景的临界值</returns>
public int GetDgGrayValue()
{
int[] pixelNum = new int[256];   //图象直方图，共256个点
int n, n1, n2;
int total;        //total为总和，累计值
double m1, m2, sum, csum, fmax, sb;  //sb为类间方差，fmax存储最大方差值
int k, t, q;
int threshValue = 1;      // 阈值
//生成直方图
for (int i = 0; i < bmpobj.Width; i++)
{
for (int j = 0; j < bmpobj.Height; j++)
{
//返回各个点的颜色，以RGB表示
pixelNum[bmpobj.GetPixel(i, j).R]++;    //相应的直方图加1
}
}
//直方图平滑化
for (k = 0; k <= 255; k++)
{
total = 0;
for (t = -2; t <= 2; t++)    //与附近2个灰度做平滑化，t值应取较小的值
{
q = k + t;
if (q < 0)      //越界处理
q = 0;
if (q > 255)
q = 255;
total = total + pixelNum[q];  //total为总和，累计值
}
pixelNum[k] = (int)((float)total / 5.0 + 0.5);  //平滑化，左边2个+中间1个+右边2个灰度，共5个，所以总和除以5，后面加0.5是用修正值
}
//求阈值
sum = csum = 0.0;
n = 0;
//计算总的图象的点数和质量矩，为后面的计算做准备
for (k = 0; k <= 255; k++)
{
sum += (double)k * (double)pixelNum[k];  //x*f(x)质量矩，也就是每个灰度的值乘以其点数（归一化后为概率），sum为其总和
n += pixelNum[k];      //n为图象总的点数，归一化后就是累积概率
}
fmax = -1.0;       //类间方差sb不可能为负，所以fmax初始值为-1不影响计算的进行
n1 = 0;
for (k = 0; k < 256; k++)     //对每个灰度（从0到255）计算一次分割后的类间方差sb
{
n1 += pixelNum[k];     //n1为在当前阈值遍前景图象的点数
if (n1 == 0) { continue; }    //没有分出前景后景
n2 = n - n1;       //n2为背景图象的点数
if (n2 == 0) { break; }    //n2为0表示全部都是后景图象，与n1=0情况类似，之后的遍历不可能使前景点数增加，所以此时可以退出循环
csum += (double)k * pixelNum[k];  //前景的“灰度的值*其点数”的总和
m1 = csum / n1;      //m1为前景的平均灰度
m2 = (sum - csum) / n2;    //m2为背景的平均灰度
sb = (double)n1 * (double)n2 * (m1 - m2) * (m1 - m2); //sb为类间方差
if (sb > fmax)     //如果算出的类间方差大于前一次算出的类间方差
{
fmax = sb;      //fmax始终为最大类间方差（otsu）
threshValue = k;    //取最大类间方差时对应的灰度的k就是最佳阈值
}
}
return threshValue;
}
/// <summary>
/// 去掉杂点（适合杂点/杂线粗为1）
/// </summary>
/// <param name="dgGrayValue">背前景灰色界限</param>
/// <returns></returns>
public void ClearNoise(int dgGrayValue, int MaxNearPoints)
{
Color piexl;
int nearDots = 0;
//逐点判断
for (int i = 0; i < bmpobj.Width; i++)
for (int j = 0; j < bmpobj.Height; j++)
{
piexl = bmpobj.GetPixel(i, j);
if (piexl.R < dgGrayValue)
{
nearDots = 0;
//判断周围8个点是否全为空
if (i == 0 || i == bmpobj.Width - 1 || j == 0 || j == bmpobj.Height - 1) //边框全去掉
{
bmpobj.SetPixel(i, j, Color.FromArgb(255, 255, 255));
}
else
{
if (bmpobj.GetPixel(i - 1, j - 1).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i, j - 1).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + 1, j - 1).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i - 1, j).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + 1, j).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i - 1, j + 1).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i, j + 1).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + 1, j + 1).R < dgGrayValue) nearDots++;
}
if (nearDots < MaxNearPoints)
bmpobj.SetPixel(i, j, Color.FromArgb(255, 255, 255)); //去掉单点 && 粗细小3邻边点
}
else //背景
bmpobj.SetPixel(i, j, Color.FromArgb(255, 255, 255));
}
}
/// <summary>
/// 3×3中值滤波除杂
/// </summary>
/// <param name="dgGrayValue"></param>
public void ClearNoise(int dgGrayValue)
{
int x, y;
byte[] p = new byte[9]; //最小处理窗口3*3
byte s;
//byte[] lpTemp=new BYTE[nByteWidth*nHeight];
int i, j;
//--!!!!!!!!!!!!!!下面开始窗口为3×3中值滤波!!!!!!!!!!!!!!!!
for (y = 1; y < bmpobj.Height - 1; y++) //--第一行和最后一行无法取窗口
{
for (x = 1; x < bmpobj.Width - 1; x++)
{
//取9个点的值
p[0] = bmpobj.GetPixel(x - 1, y - 1).R;
p[1] = bmpobj.GetPixel(x, y - 1).R;
p[2] = bmpobj.GetPixel(x + 1, y - 1).R;
p[3] = bmpobj.GetPixel(x - 1, y).R;
p[4] = bmpobj.GetPixel(x, y).R;
p[5] = bmpobj.GetPixel(x + 1, y).R;
p[6] = bmpobj.GetPixel(x - 1, y + 1).R;
p[7] = bmpobj.GetPixel(x, y + 1).R;
p[8] = bmpobj.GetPixel(x + 1, y + 1).R;
//计算中值
for (j = 0; j < 5; j++)
{
for (i = j + 1; i < 9; i++)
{
if (p[j] > p[i])
{
s = p[j];
p[j] = p[i];
p[i] = s;
}
}
}
//  if (bmpobj.GetPixel(x, y).R < dgGrayValue)
bmpobj.SetPixel(x, y, Color.FromArgb(p[4], p[4], p[4]));  //给有效值付中值
}
}
}
/// <summary>
/// 该函数用于对图像进行腐蚀运算。结构元素为水平方向或垂直方向的三个点，
/// 中间点位于原点；或者由用户自己定义3×3的结构元素。
/// </summary>
/// <param name="dgGrayValue">前后景临界值</param>
/// <param name="nMode">腐蚀方式：0表示水平方向，1垂直方向，2自定义结构元素。</param>
/// <param name="structure"> 自定义的3×3结构元素</param>
public void ErosionPic(int dgGrayValue, int nMode, bool[,] structure)
{
int lWidth = bmpobj.Width;
int lHeight = bmpobj.Height;
Bitmap newBmp = new Bitmap(lWidth, lHeight);
int i, j, n, m;    //循环变量
if (nMode == 0)
{
//使用水平方向的结构元素进行腐蚀
// 由于使用1×3的结构元素，为防止越界，所以不处理最左边和最右边
// 的两列像素
for (j = 0; j < lHeight; j++)
{
for (i = 1; i < lWidth - 1; i++)
{
//目标图像中的当前点先赋成黑色
newBmp.SetPixel(i, j, Color.Black);
//如果源图像中当前点自身或者左右有一个点不是黑色，
//则将目标图像中的当前点赋成白色
if (bmpobj.GetPixel(i - 1, j).R > dgGrayValue ||
bmpobj.GetPixel(i, j).R > dgGrayValue ||
bmpobj.GetPixel(i + 1, j).R > dgGrayValue)
newBmp.SetPixel(i, j, Color.White);
}
}
}
else if (nMode == 1)
{
//使用垂真方向的结构元素进行腐蚀
// 由于使用3×1的结构元素，为防止越界，所以不处理最上边和最下边
// 的两行像素
for (j = 1; j < lHeight - 1; j++)
{
for (i = 0; i < lWidth; i++)
{
//目标图像中的当前点先赋成黑色
newBmp.SetPixel(i, j, Color.Black);
//如果源图像中当前点自身或者左右有一个点不是黑色，
//则将目标图像中的当前点赋成白色
if (bmpobj.GetPixel(i, j - 1).R > dgGrayValue ||
bmpobj.GetPixel(i, j).R > dgGrayValue ||
bmpobj.GetPixel(i, j + 1).R > dgGrayValue)
newBmp.SetPixel(i, j, Color.White);
}
}
}
else
{
if (structure.Length != 9) //检查自定义结构
return;
//使用自定义的结构元素进行腐蚀
// 由于使用3×3的结构元素，为防止越界，所以不处理最左边和最右边
// 的两列像素和最上边和最下边的两列像素
for (j = 1; j < lHeight - 1; j++)
{
for (i = 1; i < lWidth - 1; i++)
{
//目标图像中的当前点先赋成黑色
newBmp.SetPixel(i, j, Color.Black);
//如果原图像中对应结构元素中为黑色的那些点中有一个不是黑色，
//则将目标图像中的当前点赋成白色
for (m = 0; m < 3; m++)
{
for (n = 0; n < 3; n++)
{
if (!structure[m, n])
continue;
if (bmpobj.GetPixel(i + m - 1, j + n - 1).R > dgGrayValue)
{
newBmp.SetPixel(i, j, Color.White);
break;
}
}
}
}
}
}
bmpobj = newBmp;
}
/// <summary>
/// 该函数用于对图像进行细化运算。要求目标图像为灰度图像
/// </summary>
/// <param name="dgGrayValue"></param>
public void ThiningPic(int dgGrayValue)
{
int lWidth = bmpobj.Width;
int lHeight = bmpobj.Height;
// Bitmap newBmp = new Bitmap(lWidth, lHeight);
bool bModified;    //脏标记
int i, j, n, m;    //循环变量
//四个条件
bool bCondition1;
bool bCondition2;
bool bCondition3;
bool bCondition4;
int nCount;  //计数器
int[,] neighbour = new int[5, 5];  //5×5相邻区域像素值
bModified = true;
while (bModified)
{
bModified = false;
//由于使用5×5的结构元素，为防止越界，所以不处理外围的几行和几列像素
for (j = 2; j < lHeight - 2; j++)
{
for (i = 2; i < lWidth - 2; i++)
{
bCondition1 = false;
bCondition2 = false;
bCondition3 = false;
bCondition4 = false;
if (bmpobj.GetPixel(i, j).R > dgGrayValue)
{
if (bmpobj.GetPixel(i, j).R < 255)
bmpobj.SetPixel(i, j, Color.White);
continue;
}
//获得当前点相邻的5×5区域内像素值，白色用0代表，黑色用1代表
for (m = 0; m < 5; m++)
{
for (n = 0; n < 5; n++)
{
neighbour[m, n] = bmpobj.GetPixel(i + m - 2, j + n - 2).R < dgGrayValue ? 1 : 0;
}
}
//逐个判断条件。
//判断2<=NZ(P1)<=6
nCount = neighbour[1, 1] + neighbour[1, 2] + neighbour[1, 3]
+ neighbour[2, 1] + neighbour[2, 3] +
+neighbour[3, 1] + neighbour[3, 2] + neighbour[3, 3];
if (nCount >= 2 && nCount <= 6)
{
bCondition1 = true;
}
//判断Z0(P1)=1
nCount = 0;
if (neighbour[1, 2] == 0 && neighbour[1, 1] == 1)
nCount++;
if (neighbour[1, 1] == 0 && neighbour[2, 1] == 1)
nCount++;
if (neighbour[2, 1] == 0 && neighbour[3, 1] == 1)
nCount++;
if (neighbour[3, 1] == 0 && neighbour[3, 2] == 1)
nCount++;
if (neighbour[3, 2] == 0 && neighbour[3, 3] == 1)
nCount++;
if (neighbour[3, 3] == 0 && neighbour[2, 3] == 1)
nCount++;
if (neighbour[2, 3] == 0 && neighbour[1, 3] == 1)
nCount++;
if (neighbour[1, 3] == 0 && neighbour[1, 2] == 1)
nCount++;
if (nCount == 1)
bCondition2 = true;
//判断P2*P4*P8=0 or Z0(p2)!=1
if (neighbour[1, 2] * neighbour[2, 1] * neighbour[2, 3] == 0)
{
bCondition3 = true;
}
else
{
nCount = 0;
if (neighbour[0, 2] == 0 && neighbour[0, 1] == 1)
nCount++;
if (neighbour[0, 1] == 0 && neighbour[1, 1] == 1)
nCount++;
if (neighbour[1, 1] == 0 && neighbour[2, 1] == 1)
nCount++;
if (neighbour[2, 1] == 0 && neighbour[2, 2] == 1)
nCount++;
if (neighbour[2, 2] == 0 && neighbour[2, 3] == 1)
nCount++;
if (neighbour[2, 3] == 0 && neighbour[1, 3] == 1)
nCount++;
if (neighbour[1, 3] == 0 && neighbour[0, 3] == 1)
nCount++;
if (neighbour[0, 3] == 0 && neighbour[0, 2] == 1)
nCount++;
if (nCount != 1)
bCondition3 = true;
}
//判断P2*P4*P6=0 or Z0(p4)!=1
if (neighbour[1, 2] * neighbour[2, 1] * neighbour[3, 2] == 0)
{
bCondition4 = true;
}
else
{
nCount = 0;
if (neighbour[1, 1] == 0 && neighbour[1, 0] == 1)
nCount++;
if (neighbour[1, 0] == 0 && neighbour[2, 0] == 1)
nCount++;
if (neighbour[2, 0] == 0 && neighbour[3, 0] == 1)
nCount++;
if (neighbour[3, 0] == 0 && neighbour[3, 1] == 1)
nCount++;
if (neighbour[3, 1] == 0 && neighbour[3, 2] == 1)
nCount++;
if (neighbour[3, 2] == 0 && neighbour[2, 2] == 1)
nCount++;
if (neighbour[2, 2] == 0 && neighbour[1, 2] == 1)
nCount++;
if (neighbour[1, 2] == 0 && neighbour[1, 1] == 1)
nCount++;
if (nCount != 1)
bCondition4 = true;
}
if (bCondition1 && bCondition2 && bCondition3 && bCondition4)
{
bmpobj.SetPixel(i, j, Color.White);
bModified = true;
}
else
{
bmpobj.SetPixel(i, j, Color.Black);
}
}
}
}
// 复制细化后的图像
//  bmpobj = newBmp;
}
/// <summary>
/// 锐化要启用不安全代码编译
/// </summary>
/// <param name="val">锐化程度。取值[0,1]。值越大锐化程度越高</param>
/// <returns>锐化后的图像</returns>
public void Sharpen(float val)
{
int w = bmpobj.Width;
int h = bmpobj.Height;
Bitmap bmpRtn = new Bitmap(w, h, PixelFormat.Format24bppRgb);
BitmapData srcData = bmpobj.LockBits(new Rectangle(0, 0, w, h), ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);
BitmapData dstData = bmpRtn.LockBits(new Rectangle(0, 0, w, h), ImageLockMode.WriteOnly, PixelFormat.Format24bppRgb);
unsafe
{
byte* pIn = (byte*)srcData.Scan0.ToPointer();
byte* pOut = (byte*)dstData.Scan0.ToPointer();
int stride = srcData.Stride;
byte* p;
for (int y = 0; y < h; y++)
{
for (int x = 0; x < w; x++)
{
//取周围9点的值。位于边缘上的点不做改变。
if (x == 0 || x == w - 1 || y == 0 || y == h - 1)
{
//不做
pOut[0] = pIn[0];
pOut[1] = pIn[1];
pOut[2] = pIn[2];
}
else
{
int r1, r2, r3, r4, r5, r6, r7, r8, r0;
int g1, g2, g3, g4, g5, g6, g7, g8, g0;
int b1, b2, b3, b4, b5, b6, b7, b8, b0;
float vR, vG, vB;
//左上
p = pIn - stride - 3;
r1 = p[2];
g1 = p[1];
b1 = p[0];
//正上
p = pIn - stride;
r2 = p[2];
g2 = p[1];
b2 = p[0];
//右上
p = pIn - stride + 3;
r3 = p[2];
g3 = p[1];
b3 = p[0];
//左侧
p = pIn - 3;
r4 = p[2];
g4 = p[1];
b4 = p[0];
//右侧
p = pIn + 3;
r5 = p[2];
g5 = p[1];
b5 = p[0];
//右下
p = pIn + stride - 3;
r6 = p[2];
g6 = p[1];
b6 = p[0];
//正下
p = pIn + stride;
r7 = p[2];
g7 = p[1];
b7 = p[0];
//右下
p = pIn + stride + 3;
r8 = p[2];
g8 = p[1];
b8 = p[0];
//自己
p = pIn;
r0 = p[2];
g0 = p[1];
b0 = p[0];
vR = (float)r0 - (float)(r1 + r2 + r3 + r4 + r5 + r6 + r7 + r8) / 8;
vG = (float)g0 - (float)(g1 + g2 + g3 + g4 + g5 + g6 + g7 + g8) / 8;
vB = (float)b0 - (float)(b1 + b2 + b3 + b4 + b5 + b6 + b7 + b8) / 8;
vR = r0 + vR * val;
vG = g0 + vG * val;
vB = b0 + vB * val;
if (vR > 0)
{
vR = Math.Min(255, vR);
}
else
{
vR = Math.Max(0, vR);
}
if (vG > 0)
{
vG = Math.Min(255, vG);
}
else
{
vG = Math.Max(0, vG);
}
if (vB > 0)
{
vB = Math.Min(255, vB);
}
else
{
vB = Math.Max(0, vB);
}
pOut[0] = (byte)vB;
pOut[1] = (byte)vG;
pOut[2] = (byte)vR;
}
pIn += 3;
pOut += 3;
}// end of x
pIn += srcData.Stride - w * 3;
pOut += srcData.Stride - w * 3;
} // end of y
}
bmpobj.UnlockBits(srcData);
bmpRtn.UnlockBits(dstData);
bmpobj = bmpRtn;
}
/// <summary>
/// 图片二值化
/// </summary>
/// <param name="hsb"></param>
public void BitmapTo1Bpp(Double hsb)
{
int w = bmpobj.Width;
int h = bmpobj.Height;
Bitmap bmp = new Bitmap(w, h, PixelFormat.Format1bppIndexed);
BitmapData data = bmp.LockBits(new Rectangle(0, 0, w, h), ImageLockMode.ReadWrite, PixelFormat.Format1bppIndexed);
for (int y = 0; y < h; y++)
{
byte[] scan = new byte[(w + 7) / 8];
for (int x = 0; x < w; x++)
{
Color c = bmpobj.GetPixel(x, y);
if (c.GetBrightness() >= hsb) scan[x / 8] |= (byte)(0x80 >> (x % 8));
}
Marshal.Copy(scan, 0, (IntPtr)((int)data.Scan0 + data.Stride * y), scan.Length);
}
bmp.UnlockBits(data);
bmpobj = bmp;
}
}
}

希望本文所述对大家的C#程序设计有所帮助。

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