aprecorner.cpp

微软的基于HMM的人脸识别原代码, 非常经典的说

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#include "CvTest.h"


/* Testing parameters */
static char TestName[] = "Calculating the Convolution with Sobel operators";
static char TestClass[] = "Algorithm";
static int lImageWidth;
static int lImageHeight;
static int  kerSize;
static int  lBlockSize;

static int  read_param = 0;
static int  data_types = 0;
static double EPSILON = 0.01;

static preCorner(IplImage* src, IplImage* dst, int ker)
{


    float GaussKer[15];
    float DiffKer1[15];
    float DiffKer2[15];
	float denom = 1;

    CvSize kerSize;
    IplImage* fldstX;
    IplImage* fldstXX;
    IplImage* fldstY;
    IplImage* fldstXY;
    IplImage* fldstYY;
    IplImage* tmp;
	
    IplConvKernelFP* GaussKerX;
    IplConvKernelFP* GaussKerY;
    IplConvKernelFP* DiffKernelX1;
    IplConvKernelFP* DiffKernelY1;
    IplConvKernelFP* DiffKernelX2;
    IplConvKernelFP* DiffKernelY2;

	kerSize.width=kerSize.height=ker;
    

   
    /* Creating images for testing */
   
	
    for(int i = 0; i < ker-1;i++)denom *= 2;
    denom = denom*denom * denom *255*255;
    denom=1.0f/denom;

    tmp = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F, 1, 0);
    fldstX = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F,1,0);
    fldstXX = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F,1,0);
    fldstY = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F,1,0);
    fldstXY = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F,1,0);
    fldstYY = atsCreateImage(lImageWidth, lImageHeight, IPL_DEPTH_32F,1,0);
   

    /* Calculating the kernels */
    atsCalcKernel(IPL_DEPTH_32F,0,1,ker,(char*)GaussKer,(char*)DiffKer1,&kerSize,CV_ORIGIN_TL);
	
    GaussKerX = iplCreateConvKernelFP(kerSize.width,1,kerSize.width/2,0,GaussKer);
    GaussKerY = iplCreateConvKernelFP(1,kerSize.width,0,kerSize.width/2,GaussKer);
    DiffKernelX1 = iplCreateConvKernelFP(kerSize.height,1,kerSize.height/2,0,DiffKer1);
    DiffKernelY1 = iplCreateConvKernelFP(1,kerSize.height,0,kerSize.height/2,DiffKer1);

    atsCalcKernel(IPL_DEPTH_32F,0,2,ker,(char*)GaussKer,(char*)DiffKer2,&kerSize,CV_ORIGIN_TL);
    DiffKernelX2 = iplCreateConvKernelFP(kerSize.height,1,kerSize.height/2,0,DiffKer2);
    DiffKernelY2 = iplCreateConvKernelFP(1,kerSize.height,0,kerSize.height/2,DiffKer2);



    iplSetBorderMode(src,
                     IPL_BORDER_REPLICATE,
                     IPL_SIDE_TOP|IPL_SIDE_BOTTOM|IPL_SIDE_RIGHT|IPL_SIDE_LEFT,
                     0);

    iplConvolveSep2DFP(src,fldstX,DiffKernelX1,GaussKerY);
    iplConvolveSep2DFP(src,fldstY,GaussKerX,DiffKernelY1);
    iplConvolveSep2DFP(src,fldstXX,DiffKernelX2,GaussKerY);
    iplConvolveSep2DFP(src,fldstYY,GaussKerX,DiffKernelY2);
    iplConvolveSep2DFP(src,fldstXY,DiffKernelX1,DiffKernelY1);


    iplMultiply( fldstX, fldstX, tmp);
    iplMultiply( tmp, fldstYY, dst);

    iplMultiply( fldstY, fldstY, tmp);
    iplMultiply( tmp, fldstXX, tmp );
    iplAdd(dst, tmp, dst);

    iplMultiply(fldstY, fldstX, tmp);
    iplMultiplySFP(tmp, tmp, 2);
    iplMultiply (tmp, fldstXY, tmp);
    iplSubtract(dst, tmp, dst);
	iplMultiplySFP(dst,dst,denom);


    
   iplDeleteConvKernelFP(GaussKerY);
   iplDeleteConvKernelFP(GaussKerX);
   iplDeleteConvKernelFP(DiffKernelX1);
   iplDeleteConvKernelFP(DiffKernelY1);
   iplDeleteConvKernelFP(DiffKernelX2);
   iplDeleteConvKernelFP(DiffKernelY2);

   iplDeallocate (tmp, IPL_IMAGE_ALL);
   iplDeallocate (fldstX, IPL_IMAGE_ALL);
   iplDeallocate (fldstXX, IPL_IMAGE_ALL);
   iplDeallocate (fldstY, IPL_IMAGE_ALL);
   iplDeallocate (fldstXY, IPL_IMAGE_ALL);
   iplDeallocate (fldstYY, IPL_IMAGE_ALL);

   return 0;

}



static int fcaPreCorner( void )
{
    long lErrors = 0;
    IplImage* src8u;
    IplImage* src8s;
    IplImage* src32f;
    IplImage* tmpsrc;
    IplImage* eigenv32f;
    IplImage* testdst;

    
    /* Initialization global parameters */
    if( !read_param )
    {
        read_param = 1;
        /* Determining which test are needed to run */
        trsCaseRead( &data_types,"/u/s/f/a", "a",
                    "u - unsigned char, s - signed char, f - float, a - all" );
        /* Reading test-parameters */
        trsiRead( &lImageHeight, "100", "Image height");
        trsiRead( &lImageWidth, "100", "Image width");
        trsiRead( &kerSize,"7","Size of operator");
        
    }
    if( data_types != 3 && data_types != 0 ) return TRS_UNDEF;

    
    /* Creating images for testing */
    src8u = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_8U, 1);
    src8s = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_8S, 1);
	src32f = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_32F, 1);
    tmpsrc = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_32F, 1);
	eigenv32f = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_32F, 1);
    testdst = cvCreateImage(cvSize(lImageWidth, lImageHeight), IPL_DEPTH_32F, 1);

    atsFillRandomImage(src8u,0, 64);
    atsFillRandomImage(src8s,-32, 32);
	atsFillRandomImage(src32f,-128, 127);

    atsConvert(src8u,tmpsrc);
    preCorner(tmpsrc,eigenv32f,kerSize);
    cvPreCornerDetect(src8u,testdst,kerSize);
    lErrors += (long)iplNorm(eigenv32f,testdst,IPL_C);
	
    atsConvert(src8s,tmpsrc);
    preCorner(tmpsrc,eigenv32f,kerSize);
    cvPreCornerDetect(src8s,testdst,kerSize);
    lErrors += (long)iplNorm(eigenv32f,testdst,IPL_C);
    preCorner(src32f,eigenv32f,kerSize);
    cvPreCornerDetect(src32f,testdst,kerSize);
	lErrors += (long)iplNorm(eigenv32f,testdst,IPL_C);


   if(lErrors)return TRS_FAIL;
   return TRS_OK;
} /* fcaSobel8uC1R */

void InitAPreCorner(void)
{
    trsReg( "cvPreCornerDetect", TestName, TestClass, fcaPreCorner );

} /* InitASobel */