一维最大熵二值化方法

static int Get1DMaxEntropyThreshold(const int* HistGram, int const  in_Start, int const in_End)
    {
        int X, Y, Amount = 0;
        double HistGramD[256];
        double SumIntegral, EntropyBack, EntropyFore, MaxEntropy;
        int MinValue = in_Start, MaxValue = in_End;
        int Threshold = 0;

        for (MinValue = in_Start; MinValue < in_End && HistGram[MinValue] == 0; MinValue++) ;
        for (MaxValue = in_End; MaxValue > MinValue && HistGram[MinValue] == 0; MaxValue--) ;
        if (MaxValue == MinValue) return MaxValue;          // 图像中只有一个颜色             
        if (MinValue + 1 == MaxValue) return MinValue;      // 图像中只有二个颜色

        for (Y = MinValue; Y <= MaxValue; Y++) Amount += HistGram[Y];        //  像素总数

        for (Y = MinValue; Y <= MaxValue; Y++) HistGramD[Y] = (double)HistGram[Y] / Amount + 1e-17;

        MaxEntropy = -10000000;
        for (Y = MinValue + 1; Y < MaxValue; Y++)
        {
            SumIntegral = 0;
            for (X = MinValue; X <= Y; X++) SumIntegral += HistGramD[X];
            EntropyBack = 0;
            for (X = MinValue; X <= Y; X++) EntropyBack += (-HistGramD[X] / SumIntegral * log(HistGramD[X] / SumIntegral));
            EntropyFore = 0;
            for (X = Y + 1; X <= MaxValue; X++) EntropyFore += (-HistGramD[X] / (1 - SumIntegral) * log(HistGramD[X] / (1 - SumIntegral)));
            if (MaxEntropy < EntropyBack + EntropyFore)
            {				
                Threshold = Y;
                MaxEntropy = EntropyBack + EntropyFore;
            }
        }
        return Threshold;
    }