某些opencv函数改成c语言实现

根据项目需求，需要将c++代码改成c语言，最后移植到DSP上。这里记录了opencv中的高斯滤波、大津阈值、直方图均衡、膨胀、Sobel算法、霍夫变换求截距六个函数改成c语言的算法。在此记录下。

//opencv函数改写c
void Kernel(int size,float sigma);
void GaussianFilter (const unsigned char* pGauBlurSource);
void OtsuThreshold(const unsigned char* pOtsuSource);
void HisEqualization(const unsigned char* pHistEqualSource);
void Dilation(const unsigned char* pDilationSource);
void Sobel(const unsigned char* pSobelSource);
int HoughLines(const unsigned char* pHoughLinesSource);
void KernelSignal(int size,float sigma);
void ImprovedGaussianFilter (unsigned char* pImprovedGauBlurSource);
void ImprovedDilation(const unsigned char *pDilationSource);
void ImprovedSobel(unsigned char* pSobelSource);

//循环遍历计数
int i;
int j;
int m;
int n;

//高斯滤波的模板，由高斯函数生成
float CoefArray[9]={0.106997,0.113110,0.106997,0.113110,0.119572,0.113110,0.106997,0.113110,0.106997};
//高斯滤波的模板，由高斯函数生成
const float ImprovedCoefArray[3]={0.327104,0.345791,0.327104};
const int GrayScale = 256;
int wSize = 1500;
int hSize = 180;
const int pixelSum = 270000;
const float PI = 3.141596;
//高斯滤波用到的局部变量
unsigned char* pGauBlurResult;
int CoefArray_index = 0;
unsigned char sum = 0;
//大津阈值用到的局部变量
int pixelCount[256];
float pixelPro[256];
int threshold;
float w0, w1, u0tmp, u1tmp, u0, u1, u, deltaMax ;
float deltatmp;
unsigned char* pOtsuSourceTmp;
unsigned char* pOtsuResult;
//直方图均衡用到的局部变量
unsigned char* ptr;
unsigned int tmp_hist[256];
unsigned int map[256]; //灰度映射表
int  histogram[256];//图像像素值个数统计
int *pHistogram;      //指针定义
unsigned char* pHistEqualSourceTmp;
unsigned char* pHistResult;
//膨胀用到的局部变量
unsigned char* pDilationResult;
int flag;
//Sobel用到的局部变量
unsigned char* pSobelResult;
unsigned char amplitudeTmp;
unsigned char gradX;
unsigned char gradY;
//改进版的Sobel
unsigned char* pSobelSourceTmp;
float threshold = 0.6*255;
int pix;

//霍夫变换求截距用到的局部变量
const int theta = 180;
//const int diagonalDistance = 1510;//对角线长度
int distance;
int diagonal;
int tmpDiatance;
int tmpX;
int tmpY;
int intercept;
int** linesCount;
int** linesY;

//----------高斯滤波----------//
void GaussianFilter (const unsigned char* pGauBlurSource)
{
    //边缘不做处理
    for(i=0;i 255)
                sum = 255;
            pGauBlurResult[i*wSize+j] = sum;
        }
    }

}
//------------改进版高斯滤波------------//
void ImprovedGaussianFilter (unsigned char* pImprovedGauBlurSource)
{
    //边缘不做处理
    for(i=0;i 255)
                sum = 255;
            pGauBlurResult[i*wSize+j] = sum;
        }
    }
    for (j=1;j 255)
                sum = 255;
            pGauBlurResult[i*wSize+j] = sum;
        }
    }
}
//-----------大津阈值------------//
void OtsuThreshold(const unsigned char* pOtsuSource)
{
    //printf("come into OtsuThreshold\n");
    deltaMax = 0;
    threshold = 0;
    pOtsuSourceTmp = pOtsuSource;
    for (i = 0; i < GrayScale; i++)
    {
        pixelCount[i] = 0;
        pixelPro[i] = 0.0;
    }

    //统计灰度级中每个像素在整幅图像中的个数
    for(i = 0;i < pixelSum;i++)
    {
        pixelCount[(int)(*pOtsuSourceTmp)] ++;  //将像素值作为计数数组的下标
        pOtsuSourceTmp++;
    }

    //计算每个像素在整幅图像中的比例
    for (i = 0; i < GrayScale; i++)
        pixelPro[i] = (float)pixelCount[i] / pixelSum;

    //w0为背景像素点占整幅图像的比例;u0tmp为背景像素点的平均灰度值;w1为前景像素点占整幅图像的比例;u1tmp为前景像素点的平均灰度值;u为整幅图像的平均灰度
    //遍历灰度级[0,255]
    for (i = 0; i < GrayScale; i++)     // i作为阈值
    {
        w0 = w1 = u0tmp = u1tmp = u0 = u1 = u = 0.0;
        deltatmp = 0.0;
        for (j = 0; j < GrayScale; j++)
        {
            if (j <= i)   //背景部分
            {
                w0 += pixelPro[j];
                u0tmp += j * pixelPro[j];
            }
            else   //前景部分
            {
                w1 += pixelPro[j];
                u1tmp += j * pixelPro[j];
            }
        }
        u0 = u0tmp/w0;
        u1 = u1tmp/w1;
        u = u0tmp + u1tmp;

        deltatmp = w0*w1*(u0-u1)*(u0-u1);//w0 * (u0 - u) * (u0 - u) + w1 * (u1 - u) * (u0 - u);

        if (deltatmp > deltaMax)
        {
            deltaMax = deltatmp;
            threshold = i;
        }
    }
    //printf("threshold=%d\n",threshold);
    //根据阈值threshold进行分割
    pOtsuSourceTmp = pOtsuSource;
    for(i = 0;i threshold)
            *pOtsuResult = 255;
        else
            *pOtsuResult = 0;
        pOtsuSourceTmp++;
        pOtsuResult++;
    }
    pOtsuResult = &Pixel_Otsu[0];

}
//--------------直方图均衡--------------//
void HisEqualization(const unsigned char *pHistEqualSource)
{
  pHistEqualSourceTmp = pHistEqualSource;
  pHistogram = histogram;

  for(i=0;i<256;i++)
     histogram[i]=0;
  //统计各个灰度值的个数
  pHistEqualSourceTmp=pHistEqualSource;
  for(i=0;itmpDiatance)
            {
                tmpDiatance = linesCount[i][j];
                tmpX = i;
                tmpY = j;

            }
        }
    }

    intercept = linesY[tmpX][tmpY]/linesCount[tmpX][tmpY];
    printf("intercept = %d\n",intercept);
    return intercept;
}
//-------------Sobel算法----------//
void Sobel(const unsigned char *pSobelSource)
{
    //printf("come into Sobel\n");
    //边缘不做处理
    for(i=0;i255)
                amplitudeTmp = 255;
            pSobelResult[i*wSize+j] = amplitudeTmp;
        }
    }
}
//-------------改进版Sobel算法----------//
void ImprovedSobel(unsigned char* pSobelSource)
{
    //printf("come into ImprovedSobel\n");
    pSobelSourceTmp = pSobelSource;

    //边缘不做处理
    for(i=0;igradX?gradY:gradX;
        *(pSobelResult+i)= pix>threshold?255:0;
    }
}
//--------------计算高斯核系数-------------//
void Kernel(int size,float sigma)
{
   //计算sigmaX的值
   float sigmaX;
   float sum = 0;
   float gaus[3][3];
   const float PI=4.0*atan(1.0); //圆周率π赋值
   int center=size/2;
   int k =0;
   if(sigma>0)
     sigmaX = sigma;
   else
     sigmaX = ((size-1)*0.5 - 1)*0.3 +0.8;

   for(i=0;i0)
     sigmaX = sigma;
   else
     sigmaX = ((size-1)*0.5 - 1)*0.3 +0.8;

   for(i=0;i