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Open Source Computer Vision Library https://opencv.org/
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opencv/modules/legacy/src/snakes.cpp

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/*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 */