mirror of
https://github.com/opencv/opencv.git
synced 2024-11-29 13:47:32 +08:00
429 lines
15 KiB
C++
429 lines
15 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// Intel License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
#include "precomp.hpp"
|
|
|
|
#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;
|
|
#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 );
|
|
cv::Ptr<cv::FilterEngine> pX, pY;
|
|
|
|
/* 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 = cv::createDerivFilter( CV_8U, CV_16S, 1, 0, 3, cv::BORDER_REPLICATE );
|
|
pY = cv::createDerivFilter( CV_8U, CV_16S, 0, 1, 3, cv::BORDER_REPLICATE );
|
|
gradient = (float *) cvAlloc( roi.height * roi.width * sizeof( float ));
|
|
|
|
map = (uchar *) cvAlloc( map_width * map_height );
|
|
/* 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);
|
|
diffy = pt[n - 1].y - (pt[i].y + j);
|
|
}
|
|
else
|
|
{
|
|
diffx = pt[i - 1].x - (pt[i].x + k);
|
|
diffy = pt[i - 1].y - (pt[i].y + j);
|
|
}
|
|
Econt[(j + centery) * win.width + k + centerx] = energy =
|
|
(float) fabs( ave_d -
|
|
cvSqrt( (float) (diffx * diffx + diffy * diffy) ));
|
|
|
|
maxEcont = MAX( maxEcont, energy );
|
|
minEcont = MIN( minEcont, energy );
|
|
}
|
|
}
|
|
tmp = maxEcont - minEcont;
|
|
tmp = (tmp == 0) ? 0 : (1 / tmp);
|
|
for( k = 0; k < neighbors; k++ )
|
|
{
|
|
Econt[k] = (Econt[k] - minEcont) * tmp;
|
|
}
|
|
|
|
/* Calculate Ecurv */
|
|
maxEcurv = 0;
|
|
minEcurv = _CV_SNAKE_BIG;
|
|
for( j = -upper; j <= bottom; j++ )
|
|
{
|
|
for( k = -left; k <= right; k++ )
|
|
{
|
|
int tx, ty;
|
|
float energy;
|
|
|
|
if( i == 0 )
|
|
{
|
|
tx = pt[n - 1].x - 2 * (pt[i].x + k) + pt[i + 1].x;
|
|
ty = pt[n - 1].y - 2 * (pt[i].y + j) + pt[i + 1].y;
|
|
}
|
|
else if( i == n - 1 )
|
|
{
|
|
tx = pt[i - 1].x - 2 * (pt[i].x + k) + pt[0].x;
|
|
ty = pt[i - 1].y - 2 * (pt[i].y + j) + pt[0].y;
|
|
}
|
|
else
|
|
{
|
|
tx = pt[i - 1].x - 2 * (pt[i].x + k) + pt[i + 1].x;
|
|
ty = pt[i - 1].y - 2 * (pt[i].y + j) + pt[i + 1].y;
|
|
}
|
|
Ecurv[(j + centery) * win.width + k + centerx] = energy =
|
|
(float) (tx * tx + ty * ty);
|
|
maxEcurv = MAX( maxEcurv, energy );
|
|
minEcurv = MIN( minEcurv, energy );
|
|
}
|
|
}
|
|
tmp = maxEcurv - minEcurv;
|
|
tmp = (tmp == 0) ? 0 : (1 / tmp);
|
|
for( k = 0; k < neighbors; k++ )
|
|
{
|
|
Ecurv[k] = (Ecurv[k] - minEcurv) * tmp;
|
|
}
|
|
|
|
/* Calculate Eimg */
|
|
for( j = -upper; j <= bottom; j++ )
|
|
{
|
|
for( k = -left; k <= right; k++ )
|
|
{
|
|
float energy;
|
|
|
|
if( scheme == _CV_SNAKE_GRAD )
|
|
{
|
|
/* look at map and check status */
|
|
int x = (pt[i].x + k)/WTILE_SIZE;
|
|
int y = (pt[i].y + j)/WTILE_SIZE;
|
|
|
|
if( map[y * map_width + x] == 0 )
|
|
{
|
|
int l, m;
|
|
|
|
/* evaluate block location */
|
|
int upshift = y ? 1 : 0;
|
|
int leftshift = x ? 1 : 0;
|
|
int bottomshift = MIN( 1, roi.height - (y + 1)*WTILE_SIZE );
|
|
int rightshift = MIN( 1, roi.width - (x + 1)*WTILE_SIZE );
|
|
CvRect g_roi = { x*WTILE_SIZE - leftshift, y*WTILE_SIZE - upshift,
|
|
leftshift + WTILE_SIZE + rightshift, upshift + WTILE_SIZE + bottomshift };
|
|
CvMat _src1;
|
|
cvGetSubArr( &_src, &_src1, g_roi );
|
|
|
|
cv::Mat _src_ = cv::cvarrToMat(&_src1);
|
|
cv::Mat _dx_ = cv::cvarrToMat(&_dx);
|
|
cv::Mat _dy_ = cv::cvarrToMat(&_dy);
|
|
|
|
pX->apply( _src_, _dx_, cv::Rect(0,0,-1,-1), cv::Point(), true );
|
|
pY->apply( _src_, _dy_, cv::Rect(0,0,-1,-1), cv::Point(), true );
|
|
|
|
for( l = 0; l < WTILE_SIZE + bottomshift; l++ )
|
|
{
|
|
for( m = 0; m < WTILE_SIZE + rightshift; m++ )
|
|
{
|
|
gradient[(y*WTILE_SIZE + l) * roi.width + x*WTILE_SIZE + m] =
|
|
(float) (dx[(l + upshift) * TILE_SIZE + m + leftshift] *
|
|
dx[(l + upshift) * TILE_SIZE + m + leftshift] +
|
|
dy[(l + upshift) * TILE_SIZE + m + leftshift] *
|
|
dy[(l + upshift) * TILE_SIZE + m + leftshift]);
|
|
}
|
|
}
|
|
map[y * map_width + x] = 1;
|
|
}
|
|
Eimg[(j + centery) * win.width + k + centerx] = energy =
|
|
gradient[(pt[i].y + j) * roi.width + pt[i].x + k];
|
|
}
|
|
else
|
|
{
|
|
Eimg[(j + centery) * win.width + k + centerx] = energy =
|
|
src[(pt[i].y + j) * srcStep + pt[i].x + k];
|
|
}
|
|
|
|
maxEimg = MAX( maxEimg, energy );
|
|
minEimg = MIN( minEimg, energy );
|
|
}
|
|
}
|
|
|
|
tmp = (maxEimg - minEimg);
|
|
tmp = (tmp == 0) ? 0 : (1 / tmp);
|
|
|
|
for( k = 0; k < neighbors; k++ )
|
|
{
|
|
Eimg[k] = (minEimg - Eimg[k]) * tmp;
|
|
}
|
|
|
|
/* locate coefficients */
|
|
if( coeffUsage == CV_VALUE)
|
|
{
|
|
_alpha = *alpha;
|
|
_beta = *beta;
|
|
_gamma = *gamma;
|
|
}
|
|
else
|
|
{
|
|
_alpha = alpha[i];
|
|
_beta = beta[i];
|
|
_gamma = gamma[i];
|
|
}
|
|
|
|
/* Find Minimize point in the neighbors */
|
|
for( k = 0; k < neighbors; k++ )
|
|
{
|
|
E[k] = _alpha * Econt[k] + _beta * Ecurv[k] + _gamma * Eimg[k];
|
|
}
|
|
Emin = _CV_SNAKE_BIG;
|
|
for( j = -upper; j <= bottom; j++ )
|
|
{
|
|
for( k = -left; k <= right; k++ )
|
|
{
|
|
|
|
if( E[(j + centery) * win.width + k + centerx] < Emin )
|
|
{
|
|
Emin = E[(j + centery) * win.width + k + centerx];
|
|
offsetx = k;
|
|
offsety = j;
|
|
}
|
|
}
|
|
}
|
|
|
|
if( offsetx || offsety )
|
|
{
|
|
pt[i].x += offsetx;
|
|
pt[i].y += offsety;
|
|
moved++;
|
|
}
|
|
}
|
|
converged = (moved == 0);
|
|
if( (criteria.type & CV_TERMCRIT_ITER) && (iteration >= criteria.max_iter) )
|
|
converged = 1;
|
|
if( (criteria.type & CV_TERMCRIT_EPS) && (moved <= criteria.epsilon) )
|
|
converged = 1;
|
|
}
|
|
|
|
cvFree( &Econt );
|
|
cvFree( &Ecurv );
|
|
cvFree( &Eimg );
|
|
cvFree( &E );
|
|
|
|
if( scheme == _CV_SNAKE_GRAD )
|
|
{
|
|
cvFree( &gradient );
|
|
cvFree( &map );
|
|
}
|
|
return CV_OK;
|
|
}
|
|
|
|
|
|
CV_IMPL void
|
|
cvSnakeImage( const IplImage* src, CvPoint* points,
|
|
int length, float *alpha,
|
|
float *beta, float *gamma,
|
|
int coeffUsage, CvSize win,
|
|
CvTermCriteria criteria, int calcGradient )
|
|
{
|
|
uchar *data;
|
|
CvSize size;
|
|
int step;
|
|
|
|
if( src->nChannels != 1 )
|
|
CV_Error( CV_BadNumChannels, "input image has more than one channel" );
|
|
|
|
if( src->depth != IPL_DEPTH_8U )
|
|
CV_Error( CV_BadDepth, cvUnsupportedFormat );
|
|
|
|
cvGetRawData( src, &data, &step, &size );
|
|
|
|
IPPI_CALL( icvSnake8uC1R( data, step, size, points, length,
|
|
alpha, beta, gamma, coeffUsage, win, criteria,
|
|
calcGradient ? _CV_SNAKE_GRAD : _CV_SNAKE_IMAGE ));
|
|
}
|
|
|
|
/* end of file */
|