2015年6月26日
C#实现验证码中粘连字符分割（七）
5月份发布了《C#实现验证码中粘连字符分割》的一系列文章，初步构建了粘连字符分割的软件框架。近期经过进一步的学习，修正了之前发布的代码中的BUG，字符分割的准确度提升了一点点，我选取了9组原图与分割图的组合。
这次发布的代码更新了如下内容：
1.更新了被判定为谷点的像素类型组合，新增了寻找下谷点的代码；
2.修正了水滴算法的滴水路径，新增了向上滴落的滴水算法；
3.修正了水滴算法以及根据最终路径分割字符中的部分BUG；
4.更新了谷点过滤算法，但仍然简单粗暴。
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/* -------------------------------------------------------- * 作者：livezingy * * 博客：https://www.livezingy.com * * 开发环境： * Visual Studio V2012 * .NET Framework 4.5 * * * 版本历史： * V1.1 2015年06月26日 * 实现粘粘字符的分割 * 1.分别寻找上谷点与下谷点，先对上下谷点分别进行过滤，过滤原则仍根据相邻像素的距离，以及与边界的距离进行过滤； * 2.上下谷点再综合过滤，最终保留3个谷点，综合过滤原则：上下谷点距离在1/8距离以内时，上下谷点优先取上谷点； * 3.3个谷点确定后，上谷点采用向下滴落；下谷点采用向上滴落 * --------------------------------------------------------- */ using System; using System.Collections.Generic; using System.Collections; using System.Linq; using System.Text; using System.Drawing; using System.Drawing.Imaging; using System.Runtime.InteropServices; namespace ImageSegmention { /// <summary> /// 通常我们关注的字符并不会填满整张图片，找到我们所关注字符的边界并将其记录。 /// </summary> public class ImgBoundary { private int _heightMax = 0; public int heightMax { get { return _heightMax; } set { _heightMax = value; } } private int _heightMin = 0; public int heightMin { get { return _heightMin; } set { _heightMin = value; } } private int _widthMax = 0; public int widthMax { get { return _widthMax; } set { _widthMax = value; } } private int _widthMin = 0; public int widthMin { get { return _widthMin; } set { _widthMin = value; } } } /// <summary> /// 记录图片中顶部的谷点集合和底部的谷点集合 /// </summary> public class ImgValley { private ArrayList _upValley; public ArrayList upValley { get { return _upValley; } set { _upValley = value; } } private ArrayList _downValley; public ArrayList downValley { get { return _downValley; } set { _downValley = value; } } } public class PixPos { private int _widthPos = 0; public int widthPos { get { return _widthPos; } set { _widthPos = value; } } private int _heightPos = 0; public int heightPos { get { return _heightPos; } set { _heightPos = value; } } } public class SegmentFunction { /// <summary> /// 将输入的含粘粘字符的图片转换为分割后的字符图片 /// </summary> /// <param name="imageSrcPath">粘粘字符图片的路径</param> /// <param name="imageSrcPath">分割后图片的存储路径</param> /// <param name="segNum">分割路径的数量</param> /// <returns>字符分割后的图片为位图</returns> public static Bitmap ImageSegment(string imageSrcPath, string imageDestPath, int segNum) { Bitmap bmp = new Bitmap(imageSrcPath); int Threshold = 0; Byte[,] BinaryArray = ImageBinarization.ToBinaryArray(bmp, out Threshold); ImgBoundary Boundary = getImgBoundary(BinaryArray); ImgValley iniValley = getImgValley(BinaryArray, Boundary); ImgValley filterValley = filterImgValley(iniValley, Boundary, segNum); ArrayList segPathList = getSegmentPath(filterValley, Boundary, BinaryArray); Bitmap segmentBmp = getDivideImg(BinaryArray, segPathList); segmentBmp.Save(imageDestPath, System.Drawing.Imaging.ImageFormat.Jpeg); return segmentBmp; } /// <summary> /// 获取含粘粘字符图片的谷点 /// </summary> /// <param name="BinaryArray">原始图片的二值化数组</param> /// <returns>谷点列表</returns> //public static ImgValley getImgValley(Byte[,] BinaryArray) //public static Bitmap getImgValley(Byte[,] BinaryArray) public static ImgValley getImgValley(Byte[,] BinaryArray,ImgBoundary Boundary) { int imageHeight = BinaryArray.GetLength(0); int imageWidth = BinaryArray.GetLength(1); ArrayList upValley = new ArrayList(); ArrayList downValley = new ArrayList(); ImgValley Valley = new ImgValley(); Byte[,] m_DesImage = Thining.ThinPicture(BinaryArray); byte flg = 1; byte P0 = 0; byte P1 = 0; byte P2 = 0; byte P3 = 0; byte P4 = 0; byte P5 = 0; byte P6 = 0; byte P7 = 0; byte P8 = 0; byte P9 = 0; byte P10 = 0; byte P11 = 0; byte P12 = 0; byte P13 = 0; byte P14 = 0; //get the up valley point for (int j = 2; j < (imageWidth - 2); j++) { flg = 1; for (int i = 2; ((i < imageHeight) && (1 == flg)); i++) { if (0 == m_DesImage[i, j]) { flg = 0; P0 = m_DesImage[i - 2, j - 2]; P1 = m_DesImage[i - 2, j - 1]; P2 = m_DesImage[i - 2, j]; P3 = m_DesImage[i - 2, j + 1]; P4 = m_DesImage[i - 2, j + 2]; P5 = m_DesImage[i - 1, j - 2]; P6 = m_DesImage[i - 1, j - 1]; P7 = m_DesImage[i - 1, j]; P8 = m_DesImage[i - 1, j + 1]; P9 = m_DesImage[i - 1, j + 2]; P10 = m_DesImage[i, j - 2]; P11 = m_DesImage[i, j - 1]; P12 = m_DesImage[i, j]; P13 = m_DesImage[i, j + 1]; P14 = m_DesImage[i, j + 2]; if ((0 == P0) && (0 == P6) && (0 == P12) && (255 == P1) && (255 == P2) && (255 == P7) && ( ((0 == P13) && (0 == P14) && (255 == P3) && (255 == P8) && (255 == P4) && (255 == P9)) || ((255 == P3) && (0 == P8) && (0 == P4)) || ((0 == P13) && (0 == P8)) || ((0 == P3) && (0 == P8)) || ((0 == P13) && (0 == P9) && (255 == P8)) || ((0 == P8) && (0 == P9) && (255 == P3) && (255 == P4)))) { upValley.Add(j); } if ((0 == P4) && (0 == P8) && (0 == P12) && (255 == P3) && (255 == P2) && (255 == P7) && (((0 == P10) && (0 == P11) && (255 == P0) && (255 == P1) && (255 == P5) && (255 == P6)) || ((0 == P6) && (0 == P11)) || ((0 == P6) && (0 == P1)) || ((255 == P6) && (0 == P5) && (0 == P11)) || ((0 == P5) && (0 == P6) && (255 == P1) && (255 == P0)))) { upValley.Add(j); } if ((0 == P5) && (0 == P12)&& (0 == P9) && (255 == P1) && (255 == P2) && (255 == P3) && (255 == P7) && ( ((0 == P6)&& (0 == P8) ) ||((255 == P6)&& (0 == P8) && (0 == P11)) || ((0 == P6) && (255 == P8) && (0 == P13)))) { upValley.Add(j); } if((0 == P11) && (0 == P12) && (0 == P13) && (255 == P7) && (255 == P1) && (255 == P2) && (255 == P3) && (((255 == P6) && (0 == P8)) || ((0 == P6) && (255 == P8)))) { upValley.Add(j); } } } } //get the down valley point for (int j = 2; j < (imageWidth - 2); j++) { flg = 1; for (int i = imageHeight - 3; ((i > 0) && (1 == flg)); i--) { P0 = m_DesImage[i, j - 2]; P1 = m_DesImage[i, j - 1]; P2 = m_DesImage[i, j]; P3 = m_DesImage[i, j + 1]; P4 = m_DesImage[i, j + 2]; P5 = m_DesImage[i + 1, j - 2]; P6 = m_DesImage[i + 1, j - 1]; P7 = m_DesImage[i + 1, j]; P8 = m_DesImage[i + 1, j + 1]; P9 = m_DesImage[i + 1, j + 2]; P10 = m_DesImage[i + 2, j - 2]; P11 = m_DesImage[i + 2, j - 1]; P12 = m_DesImage[i + 2, j]; P13 = m_DesImage[i + 2, j + 1]; P14 = m_DesImage[i + 2, j + 2]; if (0 == m_DesImage[i, j]) { flg = 0; if ((0 == P2) && (0 == P6) && (0 == P10) && (255 == P7) && (255 == P11) && (255 == P12) && (((0 == P3) && (0 == P4) && (255 == P8) && (255 == P9) && (255 == P13) && (255 == P14)) || ((255 == P13) && (0 == P8) && (0 == P14)) || ((0 == P13) && (0 == P8)) || ((0 == P3) && (0 == P8)) || ((0 == P3) && (0 == P9) && (255 == P8)) || ((0 == P8) && (0 == P9) && (255 == P13) && (255 == P14)))) { downValley.Add(j); } if ((0 == P2) && (0 == P8) && (0 == P14) && (255 == P13) && (255 == P12) && (255 == P7) && (((0 == P0) && (0 == P1) && (255 == P10) && (255 == P11) && (255 == P5) && (255 == P6)) || ((0 == P6) && (0 == P11)) || ((0 == P6) && (0 == P1)) || ((0 == P1) && (0 == P5) && (255 == P6)) || ((0 == P5) && (0 == P6) && (255 == P1) && (255 == P0)))) { downValley.Add(j); } if ((0 == P5) && (0 == P2)&& (0 == P9) && (255 == P11) && (255 == P12) && (255 == P13) && (255 == P7) && (((0 == P6) && (0 == P8)) || ((255 == P6) && (0 == P8) && (0 == P1)) || ((0 == P6) && (255 == P8) && (0 == P3)))) { downValley.Add(j); } if ((0 == P1) && (0 == P2) && (0 == P3) && (255 == P7) && (255 == P11) && (255 == P12) && (255 == P13) && (((255 == P6) && (0 == P8)) || ((0 == P6) && (255 == P8)))) { downValley.Add(j); } } } } Valley.upValley = upValley; Valley.downValley = downValley; Bitmap valleyBmp = ImageBinarization.GrayArrayToGrayBitmap(m_DesImage); // return valleyBmp; return Valley; } /// <summary> ///在四字符的前提下对谷点进行过滤，过滤原则: ///1.与左右边界间隔距离小于1/8字符宽度的谷点淘汰； ///2.若两个谷点之间的宽度差距小于3个像素点，则靠左侧的像素点被淘汰 /// </summary> /// <param name="valleyCollection">谷点列表集合</param> /// <param name="Boundary">图片中字符所在区域的边界</param> /// <returns>过滤后的谷点列表</returns> public static ImgValley filterImgValley(ImgValley valleyCollection, ImgBoundary Boundary, int valleyNum) { ArrayList valley = new ArrayList();//valleyCollection.upValley; ArrayList filterUpValley = new ArrayList(); ArrayList filterDownValley = new ArrayList(); foreach (var widVal in valleyCollection.upValley) { valley.Add(widVal); } foreach(var widVal in valleyCollection.downValley) { if(!(valley.Contains(widVal))) { valley.Add(widVal); } } valley.Sort(); int count = valley.Count; int leftWidth = Boundary.widthMin; int rightWidth = Boundary.widthMax; int filterWidth = (rightWidth - leftWidth) >> 3; int tmpWidth0 = 0; int tmpWidth1 = 0; int i = count - 1;//循环用变量 bool bFlg = true;//记录是否是首次进入i = count - 1的条件 do { if (bFlg && ((count - 1) == i))////首先确定最靠右侧的点,与右侧边界的距离为优先判定原则 { tmpWidth0 = (int)valley[i]; if ((rightWidth - tmpWidth0) < filterWidth) { valley.RemoveAt(i); i--; } else { i = 0;//右侧点满足条件后，再确定左侧点 bFlg = false; } } else if(0 == i)//确定最左侧的点，以与左侧边界的距离为优先判定原则 { tmpWidth0 = (int)valley[i]; if ((tmpWidth0 - leftWidth) < filterWidth) { valley.RemoveAt(i); } else { i ++; } } else if (i < (count - 1)) { tmpWidth1 = (int)valley[i]; if (((tmpWidth1 - tmpWidth0) < filterWidth) || ((rightWidth - tmpWidth1) < filterWidth)) { valley.RemoveAt(i); } else { tmpWidth0 = tmpWidth1; i ++; } } count = valley.Count; } while (((i < (count - 1)) && (count > valleyNum)) || bFlg); if(count > valleyNum) { int tmpVal = 0; tmpWidth0 = (int)valley[0]; tmpWidth1 = (int)valley[count - 1]; valley.RemoveAt(count - 1); valley.RemoveAt(0); foreach(var widVal in valley) { tmpVal += (int)widVal; } tmpVal = tmpVal / valley.Count; valley.Clear(); valley.Add(tmpWidth0); valley.Add(tmpVal); valley.Add(tmpWidth1); } foreach(var widVal in valley) { if(valleyCollection.upValley.Contains(widVal)) { filterUpValley.Add(widVal); } else { filterDownValley.Add(widVal); } } ImgValley filterValley = new ImgValley(); filterValley.upValley = filterUpValley; filterValley.downValley = filterDownValley; return filterValley; } /// <summary> ///根据谷点与边界获取图片分割路径 /// </summary> /// <param name="valleyCollection">谷点列表</param> /// <param name="Boundary">图片中字符所在区域的边界</param> /// <param name="BinaryArray">二值化图片数组</param> /// <returns>返回粘粘图片的分割路径</returns> public static ArrayList getSegmentPath(ImgValley valleyCollection, ImgBoundary Boundary, Byte[,] BinaryArray) { ArrayList segPathList = new ArrayList(); int yIndex2 = Boundary.widthMax; int yIndex1 = Boundary.widthMin; int xIndex2 = Boundary.heightMax; int xIndex1 = Boundary.heightMin; int tmpWidth = 0; ArrayList valley = new ArrayList(); foreach (var widVal in valleyCollection.upValley) { valley.Add(widVal); } foreach (var widVal in valleyCollection.downValley) { valley.Add(widVal); } valley.Sort(); int valleyNum = valley.Count; //将图像最左侧的点所在直线作为第一条分割路径 ArrayList pathList = new ArrayList(); for (int a = xIndex1; a < xIndex2; a++) { PixPos newPos = new PixPos(); newPos.widthPos = yIndex1; newPos.heightPos = a; pathList.Add(newPos); } segPathList.Add(pathList); for (int i = 0; i < valleyNum; i++) { tmpWidth = (int)valley[i]; if(valleyCollection.upValley.Contains(tmpWidth)) { pathList = dropFallUp(tmpWidth, Boundary, BinaryArray); segPathList.Add(pathList); } else { pathList = dropFallDown(tmpWidth, Boundary, BinaryArray); segPathList.Add(pathList); } } //将图像最右侧的点所在直线作为最后一条分割路径 ArrayList pathListLast = new ArrayList(); for (int a = xIndex1; a < xIndex2; a++) { PixPos newPos = new PixPos(); newPos.widthPos = yIndex2; newPos.heightPos = a; pathListLast.Add(newPos); } segPathList.Add(pathListLast); return segPathList; } /// <summary> ///根据谷点获取图片的分割路径，水滴从上向下滴落 /// </summary> /// <param name="valleyCollection">谷点列表</param> /// <param name="Boundary">图片中字符所在区域的边界</param> /// <param name="BinaryArray">二值化图片数组</param> /// <returns>返回粘粘图片的分割路径</returns> public static ArrayList dropFallUp(int widVal, ImgBoundary Boundary, Byte[,] BinaryArray) { int yIndex2 = Boundary.widthMax; int yIndex1 = Boundary.widthMin; int xIndex2 = Boundary.heightMax; int xIndex1 = Boundary.heightMin; int imageHeight = BinaryArray.GetLength(0); int imageWidth = BinaryArray.GetLength(1); ArrayList pathList = new ArrayList(); int iniPointY = widVal; int iniPointX = xIndex1;//任一分割路径的高度起始点均为有效字符出现的第一个点 byte leftRightFlg = 0;//该标志位置位时，表示情况5与情况6可能会循环出现 while ((iniPointX <= xIndex2) && (iniPointY < yIndex2)) { PixPos newPos = new PixPos(); newPos.widthPos = iniPointY; newPos.heightPos = iniPointX; pathList.Add(newPos); if (((iniPointX + 1) >= imageHeight) || ((iniPointX - 1) <= 0) || ((iniPointY + 1) >= imageWidth) || ((iniPointY - 1) <= 0)) { break; } Byte pointLeft = BinaryArray[iniPointX, (iniPointY - 1)]; Byte pointRight = BinaryArray[iniPointX, (iniPointY + 1)]; Byte pointDown = BinaryArray[(iniPointX + 1), iniPointY]; Byte pointDownLeft = BinaryArray[(iniPointX + 1), (iniPointY - 1)]; Byte pointDownRight = BinaryArray[(iniPointX + 1), (iniPointY + 1)]; if (((0 == pointLeft) && (0 == pointRight) && (0 == pointDown) && (0 == pointDownLeft) && ((0 == pointDownRight)))// all black:11111 || ((255 == pointLeft) && (255 == pointRight) && (255 == pointDown) && (255 == pointDownLeft) && ((255 == pointDownRight))//all white:00000 || ((0 == pointDownLeft) && (255 == pointDown))))//情况1与情况3 // **10* {//down iniPointX = iniPointX + 1; leftRightFlg = 0; } else if (((0 == pointDown) && (255 == pointDownLeft) && (0 == pointDownRight))//**011 || ((255 == pointLeft) && (0 == pointDown) && (255 == pointDownLeft) && (255 == pointDownRight)))//0*010 {//left down //情况2 iniPointX = iniPointX + 1; iniPointY = iniPointY - 1; leftRightFlg = 0; } else if (((0 == pointDown) && (0 == pointDownLeft) && (255 == pointDownRight))//**110 || ((0 == pointLeft) && (255 == pointRight) && (0 == pointDown) && (255 == pointDownLeft) && (255 == pointDownRight)))//10010 {//情况4 iniPointX = iniPointX + 1; iniPointY = iniPointY + 1; leftRightFlg = 0; } else if ((255 == pointRight) && (0 == pointDown) && (0 == pointDownLeft) && (0 == pointDownRight))//*0111 {//情况5 if (0 == leftRightFlg) { iniPointY = iniPointY + 1; leftRightFlg = 1; } else//标志位为1时说明上一个点出现在情况6，而本次循环的点出现在情况5，此种情况将垂直渗透 { iniPointX = iniPointX + 1; leftRightFlg = 0; } } else if ((255 == pointLeft) && (0 == pointDown) && (0 == pointDownLeft) && (0 == pointDownRight))//01111 {//情况6 if (0 == leftRightFlg) { iniPointY = iniPointY - 1; leftRightFlg = 1; } else//标志位为1时说明上一个点出现在情况5，而本次循环的点出现在情况6，此种情况将垂直渗透 { iniPointX = iniPointX + 1; leftRightFlg = 0; } } else { iniPointX = iniPointX + 1; } } return pathList; } /// <summary> ///根据谷点获取图片的分割路径，水滴从下向上滴落 /// </summary> /// <param name="valleyCollection">谷点列表</param> /// <param name="Boundary">图片中字符所在区域的边界</param> /// <param name="BinaryArray">二值化图片数组</param> /// <returns>返回粘粘图片的分割路径</returns> public static ArrayList dropFallDown(int widVal, ImgBoundary Boundary, Byte[,] BinaryArray) { int yIndex2 = Boundary.widthMax; int yIndex1 = Boundary.widthMin; int xIndex2 = Boundary.heightMax; int xIndex1 = Boundary.heightMin; int imageHeight = BinaryArray.GetLength(0); int imageWidth = BinaryArray.GetLength(1); ArrayList pathList = new ArrayList(); int iniPointY = widVal; int iniPointX = xIndex2;//任一分割路径的高度起始点均为有效字符出现的第一个点 byte leftRightFlg = 0;//该标志位置位时，表示情况5与情况6可能会循环出现 while ((iniPointX >= xIndex1) && (iniPointY < yIndex2)) { PixPos newPos = new PixPos(); newPos.widthPos = iniPointY; newPos.heightPos = iniPointX; pathList.Add(newPos); if (((iniPointX - 1) <= 0) || ((iniPointY + 1) >= imageWidth) || ((iniPointY - 1) <= 0)) { break; } Byte pointLeft = BinaryArray[iniPointX, (iniPointY - 1)]; Byte pointRight = BinaryArray[iniPointX, (iniPointY + 1)]; Byte pointDown = BinaryArray[(iniPointX - 1), iniPointY]; Byte pointDownLeft = BinaryArray[(iniPointX - 1), (iniPointY - 1)]; Byte pointDownRight = BinaryArray[(iniPointX - 1), (iniPointY + 1)]; if (((0 == pointLeft) && (0 == pointRight) && (0 == pointDown) && (0 == pointDownLeft) && ((0 == pointDownRight)))// all black 11111 || ((255 == pointLeft) && (255 == pointRight) && (255 == pointDown) && (255 == pointDownLeft) && ((255 == pointDownRight))//all white 00000 || ((0 == pointDownLeft) && (255 == pointDown))))//情况1与情况3 //**10* {//down iniPointX = iniPointX - 1; leftRightFlg = 0; } else if (((0 == pointDown) && (255 == pointDownLeft) && (0 == pointDownRight))//**011 || ((255 == pointLeft) && (0 == pointDown) && (255 == pointDownLeft) && (255 == pointDownRight)))//0*010 {//left down //情况2 iniPointX = iniPointX - 1; iniPointY = iniPointY - 1; leftRightFlg = 0; } else if (((0 == pointDown) && (0 == pointDownLeft) && (255 == pointDownRight))//**110 || ((0 == pointLeft) && (255 == pointRight) && (0 == pointDown) && (255 == pointDownLeft) && (255 == pointDownRight)))//10010 {//情况4 iniPointX = iniPointX - 1; iniPointY = iniPointY + 1; leftRightFlg = 0; } else if ((255 == pointRight) && (0 == pointDown) && (0 == pointDownLeft) && (0 == pointDownRight))//*0111 {//情况5 if (0 == leftRightFlg) { iniPointY = iniPointY + 1; leftRightFlg = 1; } else//标志位为1时说明上一个点出现在情况6，而本次循环的点出现在情况5，此种情况将垂直渗透 { iniPointX = iniPointX - 1; leftRightFlg = 0; } } else if ((255 == pointLeft) && (0 == pointDown) && (0 == pointDownLeft) && (0 == pointDownRight))//01111 {//情况6 if (0 == leftRightFlg) { iniPointY = iniPointY - 1; leftRightFlg = 1; } else//标志位为1时说明上一个点出现在情况5，而本次循环的点出现在情况6，此种情况将垂直渗透 { iniPointX = iniPointX - 1; leftRightFlg = 0; } } else { iniPointX = iniPointX - 1; } } pathList.Reverse(); return pathList; } /// <summary> ///根据分割路径分割粘粘字符并另存为位图 /// </summary> /// <param name="valleyCollection">谷点列表</param> /// <param name="Boundary">图片中字符所在区域的边界</param> /// <param name="BinaryArray">二值化图片数组</param> /// <returns>返回粘粘图片的分割路径</returns> public static Bitmap getDivideImg(Byte[,] BinaryArray, ArrayList SegPathList) { int imageHeight = BinaryArray.GetLength(0); int imageWidth = BinaryArray.GetLength(1); int segCount = SegPathList.Count; int locationVal = imageWidth / (segCount - 1); int locationPos = 0; int iniWidthLeft = 0; Byte[,] divideArray = new Byte[imageHeight, imageWidth]; for (Int32 x = 0; x < imageHeight; x++) { for (Int32 y = 0; y < imageWidth; y++) { divideArray[x, y] = 255; } } PixPos divPosRight = new PixPos(); PixPos divPosLeft = new PixPos(); ArrayList pathListLeft = new ArrayList(); ArrayList pathListRight = new ArrayList(); int indexWidthRight = 0; int indexWidthLeft = 0; int indexHeightRight = 0; int indexHeightLeft = 0; int pointIndexLeft = 0; int pointIndexRight = 0; int posCountLeft = 0; int posCountRight = 0; for (int i = 0; i < segCount - 1; i++) { pathListLeft = (ArrayList)SegPathList[i]; pathListRight = (ArrayList)SegPathList[i + 1]; posCountLeft = pathListLeft.Count; posCountRight = pathListRight.Count; //遍历左侧路径中的点，找到左侧路径点中最小的宽度值，用于辅助分割后字符的定位 divPosRight = (PixPos)pathListRight[0]; iniWidthLeft = divPosRight.widthPos; for (pointIndexLeft = 0; pointIndexLeft < posCountLeft; pointIndexLeft++) { divPosLeft = (PixPos)pathListLeft[pointIndexLeft]; if (iniWidthLeft > divPosLeft.widthPos) { iniWidthLeft = divPosLeft.widthPos; } } locationPos = 5 + locationVal * i; pointIndexLeft = 0; pointIndexRight = 0; //目前所用的滴水算法下，同一高度值最多同时对应两个宽度值 while ((pointIndexLeft < posCountLeft) && (pointIndexRight < posCountRight)) { divPosRight = (PixPos)pathListRight[pointIndexRight]; divPosLeft = (PixPos)pathListLeft[pointIndexLeft]; pointIndexLeft++; pointIndexRight++; indexWidthLeft = divPosLeft.widthPos; indexWidthRight = divPosRight.widthPos; indexHeightRight = divPosLeft.heightPos; indexHeightLeft = divPosRight.heightPos; if(pointIndexLeft < posCountLeft) { divPosLeft = (PixPos)pathListLeft[pointIndexLeft]; if (indexHeightLeft == divPosLeft.heightPos)//若下一点高度值相同，索引值加1，宽度值更新 { pointIndexLeft++; indexWidthLeft = divPosLeft.widthPos; } } if (pointIndexRight < posCountRight) { divPosRight = (PixPos)pathListRight[pointIndexRight]; if (indexHeightRight == divPosRight.heightPos)//若下一点高度值相同，索引值加1，宽度值更新 { pointIndexRight++; indexWidthRight = divPosRight.widthPos; } } if ((indexWidthRight - iniWidthLeft + locationPos) >= imageWidth) { locationPos =imageWidth - indexWidthRight + iniWidthLeft; } for (Int32 y = indexWidthLeft; y < indexWidthRight; y++) { divideArray[indexHeightRight, (y - iniWidthLeft + locationPos)] = BinaryArray[indexHeightRight, y]; } } } Bitmap GrayBmp = ImageBinarization.BinaryArrayToBinaryBitmap(divideArray); return GrayBmp; } /// <summary> ///获取字符所在区域的边界 /// </summary> /// <param name="BinaryArray">二值化图片数组</param> /// <returns>返回字符所在区域的边界</returns> public static ImgBoundary getImgBoundary(Byte[,] BinaryArray) { ImgBoundary boundary = new ImgBoundary(); int imageHeight = BinaryArray.GetLength(0); int imageWidth = BinaryArray.GetLength(1); //记录图像出现的左右极限位置 int widthLeft = 0, widthRight = 0; //记录图像出现的上下极限位置 int heightUp = 0, heightDown = 0; byte loopFlg = 0;//当该变量置为1时将跳出双重循环 //外层循环从原点开始沿宽度方向值图像右侧 for (int x = 0; (x < imageWidth) && (0 == loopFlg); x++) { //内层循环从原点开始沿高度方向值图像下方 for (int y = 0; (y < imageHeight) && (0 == loopFlg); y++) { //遇到的第一个点为最接近原点宽度方向上的第一个点，左侧极限点 if (0 == BinaryArray[y, x]) { widthLeft = x; loopFlg = 1; } } } loopFlg = 0;//当该变量置为零时将跳出双重循环 //外层循环从图像右侧沿宽度方向到原点 for (int x = (imageWidth - 1); (x > 0) && (0 == loopFlg); x--) { //内层循环从原点开始沿高度方向到图像下方 for (int y = 0; (y < imageHeight) && (0 == loopFlg); y++) { //遇到的第一个点离原点最原的右侧极限点 if (0 == BinaryArray[y, x]) { widthRight = x; loopFlg = 1; } } } loopFlg = 0;//当该变量置为零时将跳出双重循环 //外层循环从图像下方开始向上接近原点方向，高度方向 for (int x = (imageHeight - 1); (x > 0) && (0 == loopFlg); x--) { //内层循环从原点开始，沿宽度方向 for (int y = 0; (y < imageWidth) && (0 == loopFlg); y++) { //遇到的第一个点为高度方向离原点最远的点，下方极限点 if (0 == BinaryArray[x, y]) { heightDown = x; loopFlg = 1; } } } loopFlg = 0;//当该变量置为零时将跳出双重循环 //外层循环从原点开始沿高度方向至图像下方 for (int x = 0; (x < imageHeight) && (0 == loopFlg); x++) { //内层循环从原点开始沿宽度方向至图像右侧 for (int y = 0; (y < imageWidth) && (0 == loopFlg); y++) { if (0 == BinaryArray[x, y])//遇到的第一个点为高度方向离原点最远的点，上方极限点 { heightUp = x; loopFlg = 1; } } } boundary.widthMin = widthLeft; boundary.widthMax = widthRight; boundary.heightMin = heightUp; boundary.heightMax = heightDown; return boundary; } } public static class Thining { //调用此函数即可实现提取图像骨架 public static void getThinPicture(string imageSrcPath, string imageDestPath) { Bitmap bmp = new Bitmap(imageSrcPath); int Threshold = 0; Byte[,] m_SourceImage = ImageBinarization.ToBinaryArray(bmp, out Threshold); Byte[,] m_DesImage = Thining.ThinPicture(m_SourceImage); Bitmap bmpThin = ImageBinarization.BinaryArrayToBinaryBitmap(m_DesImage); bmpThin.Save(imageDestPath, System.Drawing.Imaging.ImageFormat.Jpeg); } public static int B(Byte[,] picture, int x, int y) { return picture[x, y - 1] + picture[x + 1, y - 1] + picture[x + 1, y] + picture[x + 1, y + 1] + picture[x, y + 1] + picture[x - 1, y + 1] + picture[x - 1, y] + picture[x - 1, y - 1]; } public static int A(Byte[,] picture, int x, int y) { int counter = 0; if ((picture[x, y - 1] == 0) && (picture[x + 1, y - 1] == 1)) { counter++; } if ((picture[x + 1, y - 1] == 0) && (picture[x + 1, y] == 1)) { counter++; } if ((picture[x + 1, y] == 0) && (picture[x + 1, y + 1] == 1)) { counter++; } if ((picture[x + 1, y + 1] == 0) && (picture[x, y + 1] == 1)) { counter++; } if ((picture[x, y + 1] == 0) && (picture[x - 1, y + 1] == 1)) { counter++; } if ((picture[x - 1, y + 1] == 0) && (picture[x - 1, y] == 1)) { counter++; } if ((picture[x - 1, y] == 0) && (picture[x - 1, y - 1] == 1)) { counter++; } if ((picture[x - 1, y - 1] == 0) && (picture[x, y - 1] == 1)) { counter++; } return counter; } public static Byte[,] ThinPicture(Byte[,] newPicture) { Byte[,] picture = new Byte[newPicture.GetLength(0) + 2, newPicture.GetLength(1) + 2]; Byte[,] pictureToRemove = new Byte[newPicture.GetLength(0) + 2, newPicture.GetLength(1) + 2]; bool hasChanged; for (int i = 0; i < picture.GetLength(1); i++) { for (int j = 0; j < picture.GetLength(0); j++) { picture[j, i] = 255; pictureToRemove[j, i] = 0; } } for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { picture[j + 1, i + 1] = newPicture[j, i]; } } for (int i = 0; i < picture.GetLength(1); i++) { for (int j = 0; j < picture.GetLength(0); j++) { picture[j, i] = picture[j, i] == 0 ? picture[j, i] = 1 : picture[j, i] = 0; } } do { hasChanged = false; for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { if ((picture[j, i] == 1) && (2 <= B(picture, j, i)) && (B(picture, j, i) <= 6) && (A(picture, j, i) == 1) && (picture[j, i - 1] * picture[j + 1, i] * picture[j, i + 1] == 0) && (picture[j + 1, i] * picture[j, i + 1] * picture[j - 1, i] == 0)) { pictureToRemove[j, i] = 1; hasChanged = true; } } } for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { if (pictureToRemove[j, i] == 1) { picture[j, i] = 0; pictureToRemove[j, i] = 0; } } } for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { if ((picture[j, i] == 1) && (2 <= B(picture, j, i)) && (B(picture, j, i) <= 6) && (A(picture, j, i) == 1) && (picture[j, i - 1] * picture[j + 1, i] * picture[j - 1, i] == 0) && (picture[j, i - 1] * picture[j, i + 1] * picture[j - 1, i] == 0)) { pictureToRemove[j, i] = 1; hasChanged = true; } } } for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { if (pictureToRemove[j, i] == 1) { picture[j, i] = 0; pictureToRemove[j, i] = 0; } } } } while (hasChanged); for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { if ((picture[j, i] == 1) && (((picture[j, i - 1] * picture[j + 1, i] == 1) && (picture[j - 1, i + 1] != 1)) || ((picture[j + 1, i] * picture[j, i + 1] == 1) && (picture[j - 1, i - 1] != 1)) || //Небольшая модификцаия алгоритма для ещё большего утоньшения ((picture[j, i + 1] * picture[j - 1, i] == 1) && (picture[j + 1, i - 1] != 1)) || ((picture[j, i - 1] * picture[j - 1, i] == 1) && (picture[j + 1, i + 1] != 1)))) { picture[j, i] = 0; } } } for (int i = 0; i < picture.GetLength(1); i++) { for (int j = 0; j < picture.GetLength(0); j++) { // picture[j, i] = picture[j, i] == 0 ? 255 : 0; if (0 == picture[j, i]) { picture[j, i] = 255; } else { picture[j, i] = 0; } } } Byte[,] outPicture = new Byte[newPicture.GetLength(0), newPicture.GetLength(1)]; for (int i = 0; i < newPicture.GetLength(1); i++) { for (int j = 0; j < newPicture.GetLength(0); j++) { outPicture[j, i] = picture[j + 1, i + 1]; } } return outPicture; } } public static class ImageBinarization { /// <summary> /// 全局阈值图像二值化 /// </summary> /// <param name="bmp">原始图像</param> /// <param name="method">二值化方法</param> /// <param name="threshold">输出：全局阈值</param> /// <returns>二值化后的图像数组</returns> public static Byte[,] ToBinaryArray(this Bitmap bmp, out Int32 threshold) { // 位图转换为灰度数组 Byte[,] GrayArray = bmp.ToGrayArray(); // 计算全局阈值 threshold = OtsuThreshold(GrayArray); // 根据阈值进行二值化 Int32 PixelHeight = bmp.Height; Int32 PixelWidth = bmp.Width; Byte[,] BinaryArray = new Byte[PixelHeight, PixelWidth]; for (Int32 i = 0; i < PixelHeight; i++) { for (Int32 j = 0; j < PixelWidth; j++) { BinaryArray[i, j] = Convert.ToByte((GrayArray[i, j] > threshold) ? 255 : 0); } } return BinaryArray; } /// <summary> /// 将位图转换为灰度数组（256级灰度） /// </summary> /// <param name="bmp">原始位图</param> /// <returns>灰度数组</returns> public static Byte[,] ToGrayArray(this Bitmap bmp) { Int32 PixelHeight = bmp.Height; // 图像高度 Int32 PixelWidth = bmp.Width; // 图像宽度 Int32 Stride = ((PixelWidth * 3 + 3) >> 2) << 2; // 跨距宽度 Byte[] Pixels = new Byte[PixelHeight * Stride]; // 锁定位图到系统内存 BitmapData bmpData = bmp.LockBits(new Rectangle(0, 0, PixelWidth, PixelHeight), ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb); Marshal.Copy(bmpData.Scan0, Pixels, 0, Pixels.Length); // 从非托管内存拷贝数据到托管内存 bmp.UnlockBits(bmpData); // 从系统内存解锁位图 // 将像素数据转换为灰度数组 Byte[,] GrayArray = new Byte[PixelHeight, PixelWidth]; for (Int32 i = 0; i < PixelHeight; i++) { Int32 Index = i * Stride; for (Int32 j = 0; j < PixelWidth; j++) { GrayArray[i, j] = Convert.ToByte((Pixels[Index + 2] * 19595 + Pixels[Index + 1] * 38469 + Pixels[Index] * 7471 + 32768) >> 16); Index += 3; } } return GrayArray; } /// <summary> /// 大津法计算阈值 /// </summary> /// <param name="grayArray">灰度数组</param> /// <returns>二值化阈值</returns> public static Int32 OtsuThreshold(Byte[,] grayArray) { // 建立统计直方图 Int32[] Histogram = new Int32[256]; Array.Clear(Histogram, 0, 256); // 初始化 foreach (Byte b in grayArray) { Histogram[b]++; // 统计直方图 } // 总的质量矩和图像点数 Int32 SumC = grayArray.Length; // 总的图像点数 Double SumU = 0; // 双精度避免方差运算中数据溢出 for (Int32 i = 1; i < 256; i++) { SumU += i * Histogram[i]; // 总的质量矩 } // 灰度区间 Int32 MinGrayLevel = Array.FindIndex(Histogram, NonZero); // 最小灰度值 Int32 MaxGrayLevel = Array.FindLastIndex(Histogram, NonZero); // 最大灰度值 // 计算最大类间方差 Int32 Threshold = MinGrayLevel; Double MaxVariance = 0.0; // 初始最大方差 Double U0 = 0; // 初始目标质量矩 Int32 C0 = 0; // 初始目标点数 for (Int32 i = MinGrayLevel; i < MaxGrayLevel; i++) { if (Histogram[i] == 0) continue; // 目标的质量矩和点数 U0 += i * Histogram[i]; C0 += Histogram[i]; // 计算目标和背景的类间方差 Double Diference = U0 * SumC - SumU * C0; Double Variance = Diference * Diference / C0 / (SumC - C0); // 方差 if (Variance > MaxVariance) { MaxVariance = Variance; Threshold = i; } } // 返回类间方差最大阈值 return Threshold; } /// <summary> /// 检测非零值 /// </summary> /// <param name="value">要检测的数值</param> /// <returns> /// true：非零 /// false：零 /// </returns> private static Boolean NonZero(Int32 value) { return (value != 0) ? true : false; } /// <summary> /// 将二值化数组转换为二值化图像 /// </summary> /// <param name="binaryArray">二值化数组</param> /// <returns>二值化图像</returns> public static Bitmap BinaryArrayToBinaryBitmap(Byte[,] binaryArray) { // 将二值化数组转换为二值化数据 Int32 PixelHeight = binaryArray.GetLength(0); Int32 PixelWidth = binaryArray.GetLength(1); Int32 Stride = ((PixelWidth + 31) >> 5) << 2; Byte[] Pixels = new Byte[PixelHeight * Stride]; for (Int32 i = 0; i < PixelHeight; i++) { Int32 Base = i * Stride; for (Int32 j = 0; j < PixelWidth; j++) { if (binaryArray[i, j] != 0) { Pixels[Base + (j >> 3)] |= Convert.ToByte(0x80 >> (j & 0x7)); } } } // 创建黑白图像 Bitmap BinaryBmp = new Bitmap(PixelWidth, PixelHeight, PixelFormat.Format1bppIndexed); // 设置调色表 ColorPalette cp = BinaryBmp.Palette; cp.Entries[0] = Color.Black; // 黑色 cp.Entries[1] = Color.White; // 白色 BinaryBmp.Palette = cp; // 设置位图图像特性 BitmapData BinaryBmpData = BinaryBmp.LockBits(new Rectangle(0, 0, PixelWidth, PixelHeight), ImageLockMode.WriteOnly, PixelFormat.Format1bppIndexed); Marshal.Copy(Pixels, 0, BinaryBmpData.Scan0, Pixels.Length); BinaryBmp.UnlockBits(BinaryBmpData); return BinaryBmp; } /// <summary> /// 将灰度数组转换为灰度图像（256级灰度） /// </summary> /// <param name="grayArray">灰度数组</param> /// <returns>灰度图像</returns> public static Bitmap GrayArrayToGrayBitmap(Byte[,] grayArray) { // 将灰度数组转换为灰度数据 Int32 PixelHeight = grayArray.GetLength(0); // 图像高度 Int32 PixelWidth = grayArray.GetLength(1); // 图像宽度 Int32 Stride = ((PixelWidth + 3) >> 2) << 2; // 跨距宽度 Byte[] Pixels = new Byte[PixelHeight * Stride]; for (Int32 i = 0; i < PixelHeight; i++) { Int32 Index = i * Stride; for (Int32 j = 0; j < PixelWidth; j++) { Pixels[Index++] = grayArray[i, j]; } } // 创建灰度图像 Bitmap GrayBmp = new Bitmap(PixelWidth, PixelHeight, PixelFormat.Format8bppIndexed); // 设置调色表 ColorPalette cp = GrayBmp.Palette; for (int i = 0; i < 256; i++) cp.Entries[i] = Color.FromArgb(i, i, i); GrayBmp.Palette = cp; // 设置位图图像特性 BitmapData GrayBmpData = GrayBmp.LockBits(new Rectangle(0, 0, PixelWidth, PixelHeight), ImageLockMode.WriteOnly, PixelFormat.Format8bppIndexed); Marshal.Copy(Pixels, 0, GrayBmpData.Scan0, Pixels.Length); GrayBmp.UnlockBits(GrayBmpData); return GrayBmp; } } } |
