图像的二值化之大津法和迭代法

using System;using System.Drawing;using System.Drawing.Imaging;using System.Runtime.InteropServices; namespace Splash.Imaging{    /// <summary>    /// 图像二值化方法：大津法和迭代法    /// </summary>    public enum BinarizationMethods    {                Otsu,       // 大津法        Iterative   // 迭代法    }     /// <summary>    /// WinForm：图像的二值化    /// </summary>    public static partial class ImageUtils    {              /// <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, BinarizationMethods method, out Int32 threshold)        {   // 位图转换为灰度数组            Byte[,] GrayArray = bmp.ToGrayArray();                         // 计算全局阈值            if (method == BinarizationMethods.Otsu)                threshold = OtsuThreshold(GrayArray);            else                threshold = IterativeThreshold(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>        /// 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>二值化图像</returns>        public static Bitmap ToBinaryBitmap(this Bitmap bmp, BinarizationMethods method, out Int32 threshold)        {   // 位图转换为灰度数组            Byte[,] GrayArray = bmp.ToGrayArray();             // 计算全局阈值            if (method == BinarizationMethods.Otsu)                threshold = OtsuThreshold(GrayArray);            else                threshold = IterativeThreshold(GrayArray);             // 将灰度数组转换为二值数据            Int32 PixelHeight = bmp.Height;            Int32 PixelWidth = bmp.Width;            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 (GrayArray[i, j] > threshold)                    {                        Pixels[Base + (j >> 3)] |= Convert.ToByte(0x80 >> (j & 0x7));                    }                }            }             // 从二值数据中创建黑白图像            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>        /// 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>        /// 迭代法计算阈值        /// </summary>        /// <param name="grayArray">灰度数组</param>        /// <returns>二值化阈值</returns>         public static Int32 IterativeThreshold(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            Int32 SumU = 0;            for (Int32 i = 1; i < 256; i++)            {                SumU += i * Histogram[i];     // 总的质量矩                            }             // 确定初始阈值            Int32 MinGrayLevel = Array.FindIndex(Histogram, NonZero);       // 最小灰度值            Int32 MaxGrayLevel = Array.FindLastIndex(Histogram, NonZero);   // 最大灰度值            Int32 T0 = (MinGrayLevel + MaxGrayLevel) >> 1;            if (MinGrayLevel != MaxGrayLevel)            {                for (Int32 Iteration = 0; Iteration < 100; Iteration++)                {   // 计算目标的质量矩和点数                    Int32 U0 = 0;                    Int32 C0 = 0;                    for (Int32 i = MinGrayLevel; i <= T0; i++)                    {   // 目标的质量矩和点数                                        U0 += i * Histogram[i];                        C0 += Histogram[i];                    }                     // 目标的平均灰度值和背景的平均灰度值的中心值                    Int32 T1 = (U0 / C0 + (SumU - U0) / (SumC - C0)) >> 1;                    if (T0 == T1) break; else T0 = T1;                }            }             // 返回最佳阈值            return T0;        }    }}