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/* ----------------------------------------------------------
* 文件名称：Binarize.cs
* 作者：秦建辉
*
* QQ：36748897
*
* 博客：http://www.firstsolver.com/wordpress/
*
* 开发环境：
* Visual Studio V2012
* .NET Framework 4.5
*
* 版本历史：
* V1.2 2013年05月17日
* 将WPF版本修改为WinForm版本
*
* V1.1 2012年04月17日
* 实现迭代法阈值计算方法
*
* V1.0 2012年04月16日
* 实现大津法阈值计算方法
------------------------------------------------------------ */
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;
}
}
}