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 * 19595 + posClr.G * 38469 + posClr.B * 7472) >> 16; } /// <summary> /// 灰度转换,逐点方式 /// </summary> public Bitmap 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)); } } return bmpobj; } /// <summary> /// 去图形边框 /// </summary> /// <param name="borderWidth"></param> public Bitmap 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)); } } return bmpobj; } /// <summary> /// 灰度转换,逐行方式 /// </summary> public Bitmap 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); 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 = 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> /// 去掉噪点 /// </summary> /// <param name="dgGrayValue"></param> /// <param name="MaxNearPoints"></param> public Bitmap ClearNoise(int dgGrayValue, int MaxNearPoints) { Color piexl; int nearDots = 0; int XSpan, YSpan, tmpX, tmpY; //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)); } 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, 0); // 2/12 g.Transform = X; // Draw image //Rectangle cloneRect = GetPicValidByValue(128); //Get Valid Pic Rectangle Rectangle cloneRect = new Rectangle(0, 0, bmpobj.Width, bmpobj.Height); Bitmap tmpBmp = bmpobj.Clone(cloneRect, bmpobj.PixelFormat); g.DrawImage(tmpBmp, new Rectangle(0, 0, bmpobj.Width, bmpobj.Height), 0, 0, 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[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 int step = 1; //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> /// 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[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 } } } }
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, 2); 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(10000, 400000); bitmap.Save(Pth + "/ajax1.png"); // var client = new Baidu.Aip.Ocr.Ocr(API_KEY, SECRET_KEY); client.Timeout = 60000; // 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 = 60000; // 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