код:

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 Blur 
 Calculates convolution of source image with 
 blurring kernel. 
  
 IppStatus ippiBlur_<mod>(const Ipp<srcDatatype>* pSrc, int srcStep, 
 Ipp<dstDatatype>* pDst, int dstStep, IppiSize* pRoiSize, 
 IppiConvState* pState, int stage); 
  
 Supported values for mod : 
  
 Arguments 
 pSrc Pointer to the source image. 
 srcStep Step in bytes through the source image. 
 pDst Pointer to the destination image. 
 dstStep Step in bytes through the destination image. 
 pRoiSize Pointer to the ROI structure to be initialized with the current 
 image size. The function stores the number of obtained rows 
 in pRoiSize. 
 pState Pointer to the convolution structure. 
 stage Processing stage that can be one of the following: 
  
 IPPI_WHOLE functions process the whole image. 
 IPPI_START functions initialize the cyclic buffer and 
 process the top part of the large image. The 
 produced part is smaller than the source part, 
 and is equal to the source part if the 
 structuring element is of height 1. 
 IPPI_END functions flush the cyclic buffer and process 
 the bottom part of the large image. The 
 produced part is larger than the source part, 
 and is equal to source part if the structuring 
 element is of height 1. 
  
  
 Discussion 
 The function ippiBlur is declared in the ippcv.h file. This function calculates 
 convolution of the source image pSrc with the blurring kernel and stores the result in 
 pDst. The kernel is a matrix every element of which is 1: 
 1 1 1 
 1 1 1 
 1 1 1 
  
 Return Value 
 ippStsNoErr Indicates no error. Any other value indicates an error or a 
 warning. 
 ippStsNullPtrErr Indicates an error condition if any of the specified pointers 
 is NULL. 
 ippStsSizeErr Indicates an error condition if pRoiSize has a field with 
 zero or negative value. 
 ippStsStepErr Indicates an error condition if srcStep or dstStep is less 
 than pRoiSize.width * <pixelSize>. 
 ippStsNotEvenStepErr Indicates an error condition if steps for floating-point 
 images are not divisible by 4. 
 ippStsBadArgErr Indicates an error condition if stage is not equal to 
 IPP_START, IPP_MIDDLE, IPP_END or IPP_WHOLE.

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код:

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void 
 RecursiveGaussian::recursiveGaussianCPU(cl_uchar4* input, cl_uchar4* output, 
                                         const int width, const int height, 
                                         const float a0, const float a1,  
                                         const float a2, const float a3,  
                                         const float b1, const float b2,  
                                         const float coefp, const float coefn) 
 { 
  
     /* outer loop over all columns within image */ 
     for (int X = 0; X < width; X++) 
     { 
         // start forward filter pass 
         float xp[4] = {0.0f, 0.0f, 0.0f, 0.0f};  // previous input 
         float yp[4] = {0.0f, 0.0f, 0.0f, 0.0f};  // previous output 
         float yb[4] = {0.0f, 0.0f, 0.0f, 0.0f};  // previous output by 2 
  
         float xc[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
         float yc[4] = {0.0f, 0.0f, 0.0f, 0.0f};  
  
         for (int Y = 0; Y < height; Y++)  
         { 
             /* output position to write */ 
             int pos = Y * width + X; 
  
             /* convert input element to float4 */ 
             xc[0] = input[pos].s[0]; 
             xc[1] = input[pos].s[1]; 
             xc[2] = input[pos].s[2]; 
             xc[3] = input[pos].s[3]; 
  
             yc[0] = (a0 * xc[0]) + (a1 * xp[0]) - (b1 * yp[0]) - (b2 * yb[0]); 
             yc[1] = (a0 * xc[1]) + (a1 * xp[1]) - (b1 * yp[1]) - (b2 * yb[1]); 
             yc[2] = (a0 * xc[2]) + (a1 * xp[2]) - (b1 * yp[2]) - (b2 * yb[2]); 
             yc[3] = (a0 * xc[3]) + (a1 * xp[3]) - (b1 * yp[3]) - (b2 * yb[3]); 
  
             /* convert float4 element to output */ 
             output[pos].s[0] = (cl_uchar)yc[0]; 
             output[pos].s[1] = (cl_uchar)yc[1]; 
             output[pos].s[2] = (cl_uchar)yc[2]; 
             output[pos].s[3] = (cl_uchar)yc[3]; 
  
             for (int i = 0; i < 4; i++) 
             { 
                 xp[i] = xc[i];  
                 yb[i] = yp[i];  
                 yp[i] = yc[i];  
             } 
         } 
  
         // start reverse filter pass: ensures response is symmetrical 
         float xn[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
         float xa[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
         float yn[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
         float ya[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
  
  
         float fTemp[4] = {0.0f, 0.0f, 0.0f, 0.0f}; 
  
         for (int Y = height - 1; Y > -1; Y--)  
         { 
             int pos = Y * width + X; 
  
             /* convert uchar4 to float4 */ 
             xc[0] = input[pos].s[0]; 
             xc[1] = input[pos].s[1]; 
             xc[2] = input[pos].s[2]; 
             xc[3] = input[pos].s[3]; 
  
             yc[0] = (a2 * xn[0]) + (a3 * xa[0]) - (b1 * yn[0]) - (b2 * ya[0]); 
             yc[1] = (a2 * xn[1]) + (a3 * xa[1]) - (b1 * yn[1]) - (b2 * ya[1]); 
             yc[2] = (a2 * xn[2]) + (a3 * xa[2]) - (b1 * yn[2]) - (b2 * ya[2]); 
             yc[3] = (a2 * xn[3]) + (a3 * xa[3]) - (b1 * yn[3]) - (b2 * ya[3]); 
  
             for (int i = 0; i< 4; i++) 
             { 
                 xa[i] = xn[i];  
                 xn[i] = xc[i];  
                 ya[i] = yn[i];  
                 yn[i] = yc[i]; 
             } 
  
             /* convert uhcar4 to float4 */ 
             fTemp[0] = output[pos].s[0]; 
             fTemp[1] = output[pos].s[1]; 
             fTemp[2] = output[pos].s[2]; 
             fTemp[3] = output[pos].s[3]; 
  
             fTemp[0] += yc[0]; 
             fTemp[1] += yc[1]; 
             fTemp[2] += yc[2]; 
             fTemp[3] += yc[3]; 
  
             /* convert float4 to uchar4 */ 
             output[pos].s[0] = (cl_uchar)fTemp[0]; 
             output[pos].s[1] = (cl_uchar)fTemp[1]; 
             output[pos].s[2] = (cl_uchar)fTemp[2]; 
             output[pos].s[3] = (cl_uchar)fTemp[3]; 
         } 
     } 
  
 } 
  
 void  
 RecursiveGaussian::transposeCPU(cl_uchar4* input,  
                                 cl_uchar4* output, 
                                 const int width,  
                                 const int height) 
 { 
     /* transpose matrix */ 
     for(int Y = 0; Y < height; Y++)  
     { 
         for(int X = 0; X < width; X++)  
         { 
             output[Y + X*height] = input[X + Y*width]; 
         } 
     }   
 } 
  
 void  
 RecursiveGaussian::recursiveGaussianCPUReference() 
 { 
  
     /* Create a temp uchar4 array */ 
     cl_uchar4* temp = (cl_uchar4*)malloc(width * height * sizeof(cl_uchar4)); 
     if(temp == NULL) 
     { 
         sampleCommon->error("Failed to allocate host memory! (temp)"); 
         return; 
     } 
  
     std::string inputImageName = "RecursiveGaussian_Input.bmp"; 
     std::string filePath = sampleCommon->getPath() + inputImageName; 
  
     /* Load the image again */ 
     inputBitmap.load(filePath.c_str()); 
     if(!inputBitmap.isLoaded()) 
     { 
         sampleCommon->error("Failed to load input image!"); 
         return; 
     } 
  
     /* if image is loaded */ 
     pixelData = inputBitmap.getPixels(); 
  
     /* Copy pixel data into inputImageData */ 
     memcpy(inputImageData, pixelData, width * height * sizeof(cl_uchar4)); 
  
     /* Call recursive Gaussian CPU */ 
     recursiveGaussianCPU(inputImageData, temp, width, height,  
         oclGP.a0, oclGP.a1, oclGP.a2, oclGP.a3, 
         oclGP.b1, oclGP.b2, oclGP.coefp, oclGP.coefn); 
  
     /* Transpose the temp buffer */ 
     transposeCPU(temp, verificationOutput, width, height); 
  
     /* again Call recursive Gaussian CPU */ 
     recursiveGaussianCPU(verificationOutput, temp, height, width,  
         oclGP.a0, oclGP.a1, oclGP.a2, oclGP.a3, 
         oclGP.b1, oclGP.b2, oclGP.coefp, oclGP.coefn); 
  
     /* Do a final Transpose */ 
     transposeCPU(temp, verificationOutput, height, width);   
  
     if(temp) 
         free(temp); 
  
 } 
  
 void 
 RecursiveGaussian::computeGaussParms(float fSigma, int iOrder, GaussParms* pGP) 
 { 
     // pre-compute filter coefficients 
     pGP->nsigma = fSigma; // note: fSigma is range-checked and clamped >= 0.1f upstream 
     pGP->alpha = 1.695f / pGP->nsigma; 
     pGP->ema = exp(-pGP->alpha); 
     pGP->ema2 = exp(-2.0f * pGP->alpha); 
     pGP->b1 = -2.0f * pGP->ema; 
     pGP->b2 = pGP->ema2; 
     pGP->a0 = 0.0f; 
     pGP->a1 = 0.0f; 
     pGP->a2 = 0.0f; 
     pGP->a3 = 0.0f; 
     pGP->coefp = 0.0f; 
     pGP->coefn = 0.0f; 
  
     switch (iOrder)  
     { 
     case 0:  
         { 
             const float k = (1.0f - pGP->ema)*(1.0f - pGP->ema)/(1.0f + (2.0f * pGP->alpha * pGP->ema) - pGP->ema2); 
             pGP->a0 = k; 
             pGP->a1 = k * (pGP->alpha - 1.0f) * pGP->ema; 
             pGP->a2 = k * (pGP->alpha + 1.0f) * pGP->ema; 
             pGP->a3 = -k * pGP->ema2; 
         }  
         break; 
     case 1:  
         { 
             pGP->a0 = (1.0f - pGP->ema) * (1.0f - pGP->ema); 
             pGP->a1 = 0.0f; 
             pGP->a2 = -pGP->a0; 
             pGP->a3 = 0.0f; 
         }  
         break; 
     case 2:  
         { 
             const float ea = exp(-pGP->alpha); 
             const float k = -(pGP->ema2 - 1.0f)/(2.0f * pGP->alpha * pGP->ema); 
             float kn = -2.0f * (-1.0f + (3.0f * ea) - (3.0f * ea * ea) + (ea * ea * ea)); 
             kn /= (((3.0f * ea) + 1.0f + (3.0f * ea * ea) + (ea * ea * ea))); 
             pGP->a0 = kn; 
             pGP->a1 = -kn * (1.0f + (k * pGP->alpha)) * pGP->ema; 
             pGP->a2 = kn * (1.0f - (k * pGP->alpha)) * pGP->ema; 
             pGP->a3 = -kn * pGP->ema2; 
         }  
         break; 
     default: 
         // note: iOrder is range-checked and clamped to 0-2 upstream 
         return; 
     } 
     pGP->coefp = (pGP->a0 + pGP->a1)/(1.0f + pGP->b1 + pGP->b2); 
     pGP->coefn = (pGP->a2 + pGP->a3)/(1.0f + pGP->b1 + pGP->b2); 
 } 
 

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цитата:

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я так понимаю тебе эта тема больше не понадобится? тогда можешь перейти сразу к гуглению кода http://cimg.sourceforge.net/reference/group__cimg__tutorial.html например. Там есть кусок кода который все сделает ух ты, она дажа lgpl, совсем шикарно

цитата:

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А за совет начинающему учить С++ в приличном обществе бьют по лицу книгой Dive into Python.

цитата:

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Для школьника язык пока не важен,

цитата:

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после обрати внимание на функциональные языки.

цитата:

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Delphi, Java, Qt, etc - суть голая технология, она никому не нужна.