cvsnakes.cpp

opencv库在TI DM6437上的移植,目前包括两个库cv.lib和cxcore.lib的工程

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#include "_cv.h"

#define _CV_SNAKE_BIG 2.e+38f
#define _CV_SNAKE_IMAGE 1
#define _CV_SNAKE_GRAD  2


/*F///////////////////////////////////////////////////////////////////////////////////////
//    Name:      icvSnake8uC1R     
//    Purpose:   
//    Context:   
//    Parameters:
//               src - source image,
//               srcStep - its step in bytes,
//               roi - size of ROI,
//               pt - pointer to snake points array
//               n - size of points array, 
//               alpha - pointer to coefficient of continuity energy, 
//               beta - pointer to coefficient of curvature energy,  
//               gamma - pointer to coefficient of image energy,  
//               coeffUsage - if CV_VALUE - alpha, beta, gamma point to single value
//                            if CV_MATAY - point to arrays
//               criteria - termination criteria.
//               scheme - image energy scheme
//                         if _CV_SNAKE_IMAGE - image intensity is energy
//                         if _CV_SNAKE_GRAD  - magnitude of gradient is energy
//    Returns:   
//F*/

static CvStatus
icvSnake8uC1R( unsigned char *src,
               int srcStep,
               CvSize roi,
               CvPoint * pt,
               int n,
               float *alpha,
               float *beta,
               float *gamma,
               int coeffUsage, CvSize win, CvTermCriteria criteria, int scheme )
{
    int i, j, k;
    int neighbors = win.height * win.width;

    int centerx = win.width >> 1;
    int centery = win.height >> 1;

    float invn;
    int iteration = 0;
    int converged = 0;
	

    float *Econt;
    float *Ecurv;
    float *Eimg;
    float *E;

    float _alpha, _beta, _gamma;

    /*#ifdef GRAD_SNAKE */
    float *gradient = NULL;
    uchar *map = NULL;
    int map_width = ((roi.width - 1) >> 3) + 1;
    int map_height = ((roi.height - 1) >> 3) + 1;
    CvSepFilter pX, pY;
    #define WTILE_SIZE 8
    #define TILE_SIZE (WTILE_SIZE + 2)        
    short dx[TILE_SIZE*TILE_SIZE], dy[TILE_SIZE*TILE_SIZE];
    CvMat _dx = cvMat( TILE_SIZE, TILE_SIZE, CV_16SC1, dx );
    CvMat _dy = cvMat( TILE_SIZE, TILE_SIZE, CV_16SC1, dy );
    CvMat _src = cvMat( roi.height, roi.width, CV_8UC1, src );

    /* inner buffer of convolution process */
    //char ConvBuffer[400];

    /*#endif */


    /* check bad arguments */
    if( src == NULL )
        return CV_NULLPTR_ERR;
    if( (roi.height <= 0) || (roi.width <= 0) )
        return CV_BADSIZE_ERR;
    if( srcStep < roi.width )
        return CV_BADSIZE_ERR;
    if( pt == NULL )
        return CV_NULLPTR_ERR;
    if( n < 3 )
        return CV_BADSIZE_ERR;
    if( alpha == NULL )
        return CV_NULLPTR_ERR;
    if( beta == NULL )
        return CV_NULLPTR_ERR;
    if( gamma == NULL )
        return CV_NULLPTR_ERR;
    if( coeffUsage != CV_VALUE && coeffUsage != CV_ARRAY )
        return CV_BADFLAG_ERR;
    if( (win.height <= 0) || (!(win.height & 1)))
        return CV_BADSIZE_ERR;
    if( (win.width <= 0) || (!(win.width & 1)))
        return CV_BADSIZE_ERR;

    invn = 1 / ((float) n);

    if( scheme == _CV_SNAKE_GRAD )
    {
        pX.init_deriv( TILE_SIZE+2, CV_8UC1, CV_16SC1, 1, 0, 3 );
        pY.init_deriv( TILE_SIZE+2, CV_8UC1, CV_16SC1, 0, 1, 3 );

        gradient = (float *) cvAlloc( roi.height * roi.width * sizeof( float ));

        if( !gradient )
            return CV_OUTOFMEM_ERR;
        map = (uchar *) cvAlloc( map_width * map_height );
        if( !map )
        {
            cvFree( &gradient );
            return CV_OUTOFMEM_ERR;
        }
        /* clear map - no gradient computed */
        memset( (void *) map, 0, map_width * map_height );
    }
    Econt = (float *) cvAlloc( neighbors * sizeof( float ));
    Ecurv = (float *) cvAlloc( neighbors * sizeof( float ));
    Eimg = (float *) cvAlloc( neighbors * sizeof( float ));
    E = (float *) cvAlloc( neighbors * sizeof( float ));

    while( !converged )
    {
        float ave_d = 0;
        int moved = 0;

        converged = 0;
        iteration++;
        /* compute average distance */
        for( i = 1; i < n; i++ )
        {
            int diffx = pt[i - 1].x - pt[i].x;
            int diffy = pt[i - 1].y - pt[i].y;

            ave_d += cvSqrt( (float) (diffx * diffx + diffy * diffy) );
        }
        ave_d += cvSqrt( (float) ((pt[0].x - pt[n - 1].x) *
                                  (pt[0].x - pt[n - 1].x) +
                                  (pt[0].y - pt[n - 1].y) * (pt[0].y - pt[n - 1].y)));

        ave_d *= invn;
        /* average distance computed */
        for( i = 0; i < n; i++ )
        {
            /* Calculate Econt */
            float maxEcont = 0;
            float maxEcurv = 0;
            float maxEimg = 0;
            float minEcont = _CV_SNAKE_BIG;
            float minEcurv = _CV_SNAKE_BIG;
            float minEimg = _CV_SNAKE_BIG;
            float Emin = _CV_SNAKE_BIG;

            int offsetx = 0;
            int offsety = 0;
            float tmp;

            /* compute bounds */
            int left = MIN( pt[i].x, win.width >> 1 );
            int right = MIN( roi.width - 1 - pt[i].x, win.width >> 1 );
            int upper = MIN( pt[i].y, win.height >> 1 );
            int bottom = MIN( roi.height - 1 - pt[i].y, win.height >> 1 );

            maxEcont = 0;
            minEcont = _CV_SNAKE_BIG;
            for( j = -upper; j <= bottom; j++ )
            {
                for( k = -left; k <= right; k++ )
                {
                    int diffx, diffy;
                    float energy;

                    if( i == 0 )
                    {
                        diffx = pt[n - 1].x - (pt[i].x + k);