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

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