DSP

某些opencv函数改成c语言实现

2019-07-13 19:12发布

站内文章 / DSP
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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 "); 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 ",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 ",intercept); return intercept; } //-------------Sobel算法----------// void Sobel(const unsigned char *pSobelSource) { //printf("come into Sobel "); //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 "); 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  

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