WPF：图像处理（五）迭代法

[csharp]
using System; 
 
namespace Splash.Imaging 
{ 
    /// <summary> 
    /// 图像处理：迭代法二值化阈值计算方法 
    /// </summary> 
    public static partial class Binarize 
    { 
        /// <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 www.zzzyk.com  
            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; 
        }         
    } 
} 



作者 秦建辉

补充：Web开发 , ASP.Net ,