2015年4月29日
C#实现图像骨架提取zhang-Suen算法
图像骨架是一种图像对象结构的表示方法，Zhang-Suen算法是由Zhang T Y在1984年提出的一种并行模板匹配骨架提取算法．该算法是一种基于删除的骨架提取算法。OpenCV中貌似没有类似功能的函数，但Github上有一个名为cuda-fingerprinting的项目(感谢原作者)包含了该算法，我将相关代码提取出来，做了一些简单的改动，将其封装成一个轻量级的可为字符或图像瘦身的类，这里分享给大家。
效果图如下：
以下代码中位图转灰度数组等代码参考了这里http://www.firstsolver.com/wordpress/?p=1014(原链接已失效)；Zhang-Suen算法的实现参考了cuda-fingerprinting的相关代码，这部分我还未完全理解其原理，故代码中暂无注释，感谢原作者!
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using System; using System.Collections.Generic; using System.Linq; using System.Text; using System.Threading.Tasks; using System.Drawing; using System.Drawing.Imaging; using System.Runtime.InteropServices; /* -------------------------------------------------------- * 作者：livezingy * * 博客：https://www.livezingy.com * * 开发环境： * Visual Studio V2012 * .NET Framework 4.5 * * 版本历史： * V1.0 2015年04月29日 * 为图像瘦身 --------------------------------------------------------- */ namespace BinarizationThinning { 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 = ToBinaryArray(bmp, out Threshold); Byte[,] m_DesImage = BinarizationThinning.Thining.ThinPicture(m_SourceImage); Bitmap bmpThin = 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; } /// <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; } } } |
