主要功能：变灰，去噪，等提高清晰度等
代码类博客，无需多说，如下：
public class UnCodebase
{
public Bitmap bmpobj; public UnCodebase(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(Color posClr)
{
return (posClr.R * + posClr.G * + posClr.B * ) >> ;
} /// <summary>
/// 灰度转换,逐点方式
/// </summary>
public Bitmap GrayByPixels()
{
for (int i = ; i < bmpobj.Height; i++)
{
for (int j = ; j < bmpobj.Width; j++)
{
int tmpValue = GetGrayNumColor(bmpobj.GetPixel(j, i));
bmpobj.SetPixel(j, i, Color.FromArgb(tmpValue, tmpValue, tmpValue));
}
}
return bmpobj;
} /// <summary>
/// 去图形边框
/// </summary>
/// <param name="borderWidth"></param>
public Bitmap ClearPicBorder(int borderWidth)
{
for (int i = ; i < bmpobj.Height; i++)
{
for (int j = ; j < bmpobj.Width; j++)
{
if (i < borderWidth || j < borderWidth || j > bmpobj.Width - - borderWidth ||
i > bmpobj.Height - - borderWidth)
bmpobj.SetPixel(j, i, Color.FromArgb(, , ));
}
}
return bmpobj;
} /// <summary>
/// 灰度转换,逐行方式
/// </summary>
public Bitmap GrayByLine()
{
Rectangle rec = new Rectangle(, , 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, , len); //对图片进行处理
int GrayValue = ;
for (int i = ; i < len; i++)
{
GrayValue = GetGrayNumColor(Color.FromArgb(pixels[i]));
pixels[i] = (byte)(Color.FromArgb(GrayValue, GrayValue, GrayValue)).ToArgb(); //Color转byte
} bmpobj.UnlockBits(bmpData);
return bmpobj;
} /// <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 = ;
int posy2 = ;
for (int i = ; i < bmpobj.Height; i++) //找有效区
{
for (int j = ; 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 + ) % CharsCount; //可整除的差额数
if (Span < CharsCount)
{
int leftSpan = Span / ; //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 + , posy2 - posy1 + );
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 = ;
int posy2 = ;
for (int i = ; i < bmpobj.Height; i++) //找有效区
{
for (int j = ; 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 + , posy2 - posy1 + );
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 = ;
int posy2 = ;
for (int i = ; i < singlepic.Height; i++) //找有效区
{
for (int j = ; 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 + , posy2 - posy1 + );
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 == || ColNum == )
return null;
int singW = bmpobj.Width / RowNum;
int singH = bmpobj.Height / ColNum;
Bitmap[] PicArray = new Bitmap[RowNum * ColNum]; Rectangle cloneRect;
for (int i = ; i < ColNum; i++) //找有效区
{
for (int j = ; 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 = ; posy < singlepic.Height; posy++)
for (int posx = ; posx < singlepic.Width; posx++)
{
piexl = singlepic.GetPixel(posx, posy);
if (piexl.R < dgGrayValue) // Color.Black )
code = code + "";
else
code = code + "";
}
return code;
} /// <summary>
/// 去掉噪点
/// </summary>
/// <param name="dgGrayValue"></param>
/// <param name="MaxNearPoints"></param>
public Bitmap ClearNoise(int dgGrayValue, int MaxNearPoints)
{
Color piexl;
int nearDots = ;
int XSpan, YSpan, tmpX, tmpY;
//逐点判断
for (int i = ; i < bmpobj.Width; i++)
for (int j = ; j < bmpobj.Height; j++)
{
piexl = bmpobj.GetPixel(i, j);
if (piexl.R < dgGrayValue)
{
nearDots = ;
//判断周围8个点是否全为空
if (i == || i == bmpobj.Width - || j == || j == bmpobj.Height - ) //边框全去掉
{
bmpobj.SetPixel(i, j, Color.FromArgb(, , ));
}
else
{
if (bmpobj.GetPixel(i - , j - ).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i, j - ).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + , j - ).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i - , j).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + , j).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i - , j + ).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i, j + ).R < dgGrayValue) nearDots++;
if (bmpobj.GetPixel(i + , j + ).R < dgGrayValue) nearDots++;
} if (nearDots < MaxNearPoints)
bmpobj.SetPixel(i, j, Color.FromArgb(, , )); //去掉单点 && 粗细小3邻边点
}
else //背景
bmpobj.SetPixel(i, j, Color.FromArgb(, , ));
}
return bmpobj;
} /// <summary>
/// 扭曲图片校正
/// </summary>
public Bitmap ReSetBitMap()
{
Graphics g = Graphics.FromImage(bmpobj);
Matrix X = new Matrix();
// X.Rotate(30);
X.Shear((float)0.16666666667, ); // 2/12
g.Transform = X;
// Draw image
//Rectangle cloneRect = GetPicValidByValue(128); //Get Valid Pic Rectangle
Rectangle cloneRect = new Rectangle(, , bmpobj.Width, bmpobj.Height);
Bitmap tmpBmp = bmpobj.Clone(cloneRect, bmpobj.PixelFormat);
g.DrawImage(tmpBmp,
new Rectangle(, , bmpobj.Width, bmpobj.Height),
, , tmpBmp.Width,
tmpBmp.Height,
GraphicsUnit.Pixel); return tmpBmp;
} // <summary>
/// 得到灰度图像前景背景的临界值 最大类间方差法，yuanbao,2007.08
/// </summary>
/// <returns>前景背景的临界值</returns>
public int GetDgGrayValue()
{
int[] pixelNum = new int[]; //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 = ; // 阈值
int step = ;
//生成直方图
for (int i = ; i < bmpobj.Width; i++)
{
for (int j = ; 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 = ; k <= ; k++)
{
total = ;
for (t = -; t <= ; 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 < ) //越界处理
q = ;
if (q > )
q = ;
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 = ;
//计算总的图象的点数和质量矩，为后面的计算做准备
for (k = ; k <= ; 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 = ;
for (k = ; k < ; 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 == ) { continue; } //没有分出前景后景
n2 = n - n1; //n2为背景图象的点数
if (n2 == ) { 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>
/// 3×3中值滤波除杂，yuanbao,2007.10
/// </summary>
/// <param name="dgGrayValue"></param>
public void ClearNoise(int dgGrayValue)
{
int x, y;
byte[] p = new byte[]; //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 = ; y < bmpobj.Height - ; y++) //--第一行和最后一行无法取窗口
{
for (x = ; x < bmpobj.Width - ; x++)
{
//取9个点的值
p[] = bmpobj.GetPixel(x - , y - ).R;
p[] = bmpobj.GetPixel(x, y - ).R;
p[] = bmpobj.GetPixel(x + , y - ).R;
p[] = bmpobj.GetPixel(x - , y).R;
p[] = bmpobj.GetPixel(x, y).R;
p[] = bmpobj.GetPixel(x + , y).R;
p[] = bmpobj.GetPixel(x - , y + ).R;
p[] = bmpobj.GetPixel(x, y + ).R;
p[] = bmpobj.GetPixel(x + , y + ).R;
//计算中值
for (j = ; j < ; j++)
{
for (i = j + ; i < ; 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[], p[], p[])); //给有效值付中值
}
}
}
}
上述代码用于变灰，去噪点等功能，下面我们结合BaiDuAi 来实现读取验证码的功能<实验证明，baiduAi提供的Api仅仅能读取比较清晰的文字，像验证码这种，读取的不是太好>
namespace BaiduAi.ORC
{
class Program
{
static string APP_ID = "";
static string API_KEY = "";
static string SECRET_KEY = ""; static void Main(string[] args)
{
string Pth = Environment.CurrentDirectory;
Image img = Image.FromFile(Pth + "/ajax.png");
Bitmap bitmap = new Bitmap(img);
UnCodebase Ub = new UnCodebase(bitmap);
bitmap = Ub.GrayByPixels();
bitmap.Save(Pth + "/he.png");
int GV = Ub.GetDgGrayValue();
Ub.GetPicValidByValue(bitmap, GV);
Ub.ClearNoise(GV, );
bitmap.Save(Pth + "/12.png");
GeneralBasicDemo();
Console.ReadKey();
} public static void GeneralBasicDemo()
{
string Pth = Environment.CurrentDirectory;
Image img = Image.FromFile(Pth + "/12.png");
Bitmap bitmap = new Bitmap(img);
UnCodebase Ub = new UnCodebase(bitmap);
Ub.ClearNoise(, );
bitmap.Save(Pth + "/ajax1.png");
//
var client = new Baidu.Aip.Ocr.Ocr(API_KEY, SECRET_KEY);
client.Timeout = ; // 修改超时时间
var image = File.ReadAllBytes(Pth + "/ajax.png");
// 调用通用文字识别, 图片参数为本地图片，可能会抛出网络等异常，请使用try/catch捕获
var result = client.GeneralBasic(image);
Console.WriteLine(result);
// 如果有可选参数
var options = new Dictionary<string, object>{
{"language_type", "CHN_ENG"},
{"detect_direction", "true"},
{"detect_language", "true"},
{"probability", "true"}
};
// 带参数调用通用文字识别, 图片参数为本地图片
result = client.GeneralBasic(image, options);
Console.WriteLine(result);
}
public static void GeneralBasicUrlDemo()
{
var client = new Baidu.Aip.Ocr.Ocr(API_KEY, SECRET_KEY);
client.Timeout = ; // 修改超时时间
var url = "http://www.xiaozhu.com/ajax.php?op=AJAX_GetVerifyCode&nocache=1524468631393"; // 调用通用文字识别, 图片参数为远程url图片，可能会抛出网络等异常，请使用try/catch捕获
var result = client.GeneralBasicUrl(url);
Console.WriteLine(result);
// 如果有可选参数
var options = new Dictionary<string, object>{
{"language_type", "CHN_ENG"},
{"detect_direction", "true"},
{"detect_language", "true"},
{"probability", "true"}
};
// 带参数调用通用文字识别, 图片参数为远程url图片
result = client.GeneralBasicUrl(url, options);
Console.WriteLine(result);
}
}
}
上述的AppID AppKey等是百度开发者相关的参数！
首先我们来看看验证的原图：
这样一个彩色的验证码，
变灰和去噪点处理后，变成了这样：
彩色的字母变成了灰色/黑色
最后调用百度的接口，读取图片的内容！
验证码的内容是AvHv
Api读成了：aviv 和 H 两个部分，而且还多了. : 等符号、所有本篇并非读取验证码的解决方案！
此外说说BaiduAi ：http://ai.baidu.com/
看到了吗？各种人工智能！百度还是相当牛逼的！呵呵呵！上述验证码识别用到的是文字识别 所谓文字识别，百度提供了识别车牌号，身份证号，税务号等等，总之，我认为所谓的车牌号。身份证号等都应该是非常清晰的图片！而不像验证码，他亲妈都认不出来！特别是12306的！擦X
有时间在研究这些东西吧！
@陈卧龙的博客