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opencv/modules/imgproc/src/hough.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"
#include "_list.h"
#define halfPi ((float)(CV_PI*0.5))
#define Pi ((float)CV_PI)
#define a0 0 /*-4.172325e-7f*/ /*(-(float)0x7)/((float)0x1000000); */
#define a1 1.000025f /*((float)0x1922253)/((float)0x1000000)*2/Pi; */
#define a2 -2.652905e-4f /*(-(float)0x2ae6)/((float)0x1000000)*4/(Pi*Pi); */
#define a3 -0.165624f /*(-(float)0xa45511)/((float)0x1000000)*8/(Pi*Pi*Pi); */
#define a4 -1.964532e-3f /*(-(float)0x30fd3)/((float)0x1000000)*16/(Pi*Pi*Pi*Pi); */
#define a5 1.02575e-2f /*((float)0x191cac)/((float)0x1000000)*32/(Pi*Pi*Pi*Pi*Pi); */
#define a6 -9.580378e-4f /*(-(float)0x3af27)/((float)0x1000000)*64/(Pi*Pi*Pi*Pi*Pi*Pi); */
#define _sin(x) ((((((a6*(x) + a5)*(x) + a4)*(x) + a3)*(x) + a2)*(x) + a1)*(x) + a0)
#define _cos(x) _sin(halfPi - (x))
/****************************************************************************************\
* Classical Hough Transform *
\****************************************************************************************/
typedef struct CvLinePolar
{
float rho;
float angle;
}
CvLinePolar;
/*=====================================================================================*/
#define hough_cmp_gt(l1,l2) (aux[l1] > aux[l2])
static CV_IMPLEMENT_QSORT_EX( icvHoughSortDescent32s, int, hough_cmp_gt, const int* )
/*
Here image is an input raster;
step is it's step; size characterizes it's ROI;
rho and theta are discretization steps (in pixels and radians correspondingly).
threshold is the minimum number of pixels in the feature for it
to be a candidate for line. lines is the output
array of (rho, theta) pairs. linesMax is the buffer size (number of pairs).
Functions return the actual number of found lines.
*/
static void
icvHoughLinesStandard( const CvMat* img, float rho, float theta,
int threshold, CvSeq *lines, int linesMax )
{
cv::AutoBuffer<int> _accum, _sort_buf;
cv::AutoBuffer<float> _tabSin, _tabCos;
const uchar* image;
int step, width, height;
int numangle, numrho;
int total = 0;
float ang;
int r, n;
int i, j;
float irho = 1 / rho;
double scale;
CV_Assert( CV_IS_MAT(img) && CV_MAT_TYPE(img->type) == CV_8UC1 );
image = img->data.ptr;
step = img->step;
width = img->cols;
height = img->rows;
numangle = cvRound(CV_PI / theta);
numrho = cvRound(((width + height) * 2 + 1) / rho);
_accum.allocate((numangle+2) * (numrho+2));
_sort_buf.allocate(numangle * numrho);
_tabSin.allocate(numangle);
_tabCos.allocate(numangle);
int *accum = _accum, *sort_buf = _sort_buf;
float *tabSin = _tabSin, *tabCos = _tabCos;
memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) );
for( ang = 0, n = 0; n < numangle; ang += theta, n++ )
{
tabSin[n] = (float)(sin(ang) * irho);
tabCos[n] = (float)(cos(ang) * irho);
}
// stage 1. fill accumulator
for( i = 0; i < height; i++ )
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for( n = 0; n < numangle; n++ )
{
r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n+1) * (numrho+2) + r+1]++;
}
}
// stage 2. find local maximums
for( r = 0; r < numrho; r++ )
for( n = 0; n < numangle; n++ )
{
int base = (n+1) * (numrho+2) + r+1;
if( accum[base] > threshold &&
accum[base] > accum[base - 1] && accum[base] >= accum[base + 1] &&
accum[base] > accum[base - numrho - 2] && accum[base] >= accum[base + numrho + 2] )
sort_buf[total++] = base;
}
// stage 3. sort the detected lines by accumulator value
icvHoughSortDescent32s( sort_buf, total, accum );
// stage 4. store the first min(total,linesMax) lines to the output buffer
linesMax = MIN(linesMax, total);
scale = 1./(numrho+2);
for( i = 0; i < linesMax; i++ )
{
CvLinePolar line;
int idx = sort_buf[i];
int n = cvFloor(idx*scale) - 1;
int r = idx - (n+1)*(numrho+2) - 1;
line.rho = (r - (numrho - 1)*0.5f) * rho;
line.angle = n * theta;
cvSeqPush( lines, &line );
}
}
/****************************************************************************************\
* Multi-Scale variant of Classical Hough Transform *
\****************************************************************************************/
#if defined _MSC_VER && _MSC_VER >= 1200
#pragma warning( disable: 4714 )
#endif
//DECLARE_AND_IMPLEMENT_LIST( _index, h_ );
IMPLEMENT_LIST( _index, h_ )
static void
icvHoughLinesSDiv( const CvMat* img,
float rho, float theta, int threshold,
int srn, int stn,
CvSeq* lines, int linesMax )
{
std::vector<uchar> _caccum, _buffer;
std::vector<float> _sinTable;
std::vector<int> _x, _y;
float* sinTable;
int *x, *y;
uchar *caccum, *buffer;
_CVLIST* list = 0;
#define _POINT(row, column)\
(image_src[(row)*step+(column)])
uchar *mcaccum = 0;
int rn, tn; /* number of rho and theta discrete values */
int index, i;
int ri, ti, ti1, ti0;
int row, col;
float r, t; /* Current rho and theta */
float rv; /* Some temporary rho value */
float irho;
float itheta;
float srho, stheta;
float isrho, istheta;
const uchar* image_src;
int w, h, step;
int fn = 0;
float xc, yc;
const float d2r = (float)(Pi / 180);
int sfn = srn * stn;
int fi;
int count;
int cmax = 0;
CVPOS pos;
_index *pindex;
_index vi;
CV_Assert( CV_IS_MAT(img) && CV_MAT_TYPE(img->type) == CV_8UC1 );
CV_Assert( linesMax > 0 && rho > 0 && theta > 0 );
threshold = MIN( threshold, 255 );
image_src = img->data.ptr;
step = img->step;
w = img->cols;
h = img->rows;
irho = 1 / rho;
itheta = 1 / theta;
srho = rho / srn;
stheta = theta / stn;
isrho = 1 / srho;
istheta = 1 / stheta;
rn = cvFloor( sqrt( (double)w * w + (double)h * h ) * irho );
tn = cvFloor( 2 * Pi * itheta );
list = h_create_list__index( linesMax < 1000 ? linesMax : 1000 );
vi.value = threshold;
vi.rho = -1;
h_add_head__index( list, &vi );
/* Precalculating sin */
_sinTable.resize( 5 * tn * stn );
sinTable = &_sinTable[0];
for( index = 0; index < 5 * tn * stn; index++ )
sinTable[index] = (float)cos( stheta * index * 0.2f );
_caccum.resize(rn * tn);
caccum = &_caccum[0];
memset( caccum, 0, rn * tn * sizeof( caccum[0] ));
/* Counting all feature pixels */
for( row = 0; row < h; row++ )
for( col = 0; col < w; col++ )
fn += _POINT( row, col ) != 0;
_x.resize(fn);
_y.resize(fn);
x = &_x[0];
y = &_y[0];
/* Full Hough Transform (it's accumulator update part) */
fi = 0;
for( row = 0; row < h; row++ )
{
for( col = 0; col < w; col++ )
{
if( _POINT( row, col ))
{
int halftn;
float r0;
float scale_factor;
int iprev = -1;
float phi, phi1;
float theta_it; /* Value of theta for iterating */
/* Remember the feature point */
x[fi] = col;
y[fi] = row;
fi++;
yc = (float) row + 0.5f;
xc = (float) col + 0.5f;
/* Update the accumulator */
t = (float) fabs( cvFastArctan( yc, xc ) * d2r );
r = (float) sqrt( (double)xc * xc + (double)yc * yc );
r0 = r * irho;
ti0 = cvFloor( (t + Pi / 2) * itheta );
caccum[ti0]++;
theta_it = rho / r;
theta_it = theta_it < theta ? theta_it : theta;
scale_factor = theta_it * itheta;
halftn = cvFloor( Pi / theta_it );
for( ti1 = 1, phi = theta_it - halfPi, phi1 = (theta_it + t) * itheta;
ti1 < halftn; ti1++, phi += theta_it, phi1 += scale_factor )
{
rv = r0 * _cos( phi );
i = cvFloor( rv ) * tn;
i += cvFloor( phi1 );
assert( i >= 0 );
assert( i < rn * tn );
caccum[i] = (uchar) (caccum[i] + ((i ^ iprev) != 0));
iprev = i;
if( cmax < caccum[i] )
cmax = caccum[i];
}
}
}
}
/* Starting additional analysis */
count = 0;
for( ri = 0; ri < rn; ri++ )
{
for( ti = 0; ti < tn; ti++ )
{
if( caccum[ri * tn + ti > threshold] )
{
count++;
}
}
}
if( count * 100 > rn * tn )
{
icvHoughLinesStandard( img, rho, theta, threshold, lines, linesMax );
return;
}
_buffer.resize(srn * stn + 2);
buffer = &_buffer[0];
mcaccum = buffer + 1;
count = 0;
for( ri = 0; ri < rn; ri++ )
{
for( ti = 0; ti < tn; ti++ )
{
if( caccum[ri * tn + ti] > threshold )
{
count++;
memset( mcaccum, 0, sfn * sizeof( uchar ));
for( index = 0; index < fn; index++ )
{
int ti2;
float r0;
yc = (float) y[index] + 0.5f;
xc = (float) x[index] + 0.5f;
/* Update the accumulator */
t = (float) fabs( cvFastArctan( yc, xc ) * d2r );
r = (float) sqrt( (double)xc * xc + (double)yc * yc ) * isrho;
ti0 = cvFloor( (t + Pi * 0.5f) * istheta );
ti2 = (ti * stn - ti0) * 5;
r0 = (float) ri *srn;
for( ti1 = 0 /*, phi = ti*theta - Pi/2 - t */ ; ti1 < stn; ti1++, ti2 += 5
/*phi += stheta */ )
{
/*rv = r*_cos(phi) - r0; */
rv = r * sinTable[(int) (abs( ti2 ))] - r0;
i = cvFloor( rv ) * stn + ti1;
i = CV_IMAX( i, -1 );
i = CV_IMIN( i, sfn );
mcaccum[i]++;
assert( i >= -1 );
assert( i <= sfn );
}
}
/* Find peaks in maccum... */
for( index = 0; index < sfn; index++ )
{
i = 0;
pos = h_get_tail_pos__index( list );
if( h_get_prev__index( &pos )->value < mcaccum[index] )
{
vi.value = mcaccum[index];
vi.rho = index / stn * srho + ri * rho;
vi.theta = index % stn * stheta + ti * theta - halfPi;
while( h_is_pos__index( pos ))
{
if( h_get__index( pos )->value > mcaccum[index] )
{
h_insert_after__index( list, pos, &vi );
if( h_get_count__index( list ) > linesMax )
{
h_remove_tail__index( list );
}
break;
}
h_get_prev__index( &pos );
}
if( !h_is_pos__index( pos ))
{
h_add_head__index( list, &vi );
if( h_get_count__index( list ) > linesMax )
{
h_remove_tail__index( list );
}
}
}
}
}
}
}
pos = h_get_head_pos__index( list );
if( h_get_count__index( list ) == 1 )
{
if( h_get__index( pos )->rho < 0 )
{
h_clear_list__index( list );
}
}
else
{
while( h_is_pos__index( pos ))
{
CvLinePolar line;
pindex = h_get__index( pos );
if( pindex->rho < 0 )
{
/* This should be the last element... */
h_get_next__index( &pos );
assert( !h_is_pos__index( pos ));
break;
}
line.rho = pindex->rho;
line.angle = pindex->theta;
cvSeqPush( lines, &line );
if( lines->total >= linesMax )
break;
h_get_next__index( &pos );
}
}
h_destroy_list__index(list);
}
/****************************************************************************************\
* Probabilistic Hough Transform *
\****************************************************************************************/
static void
icvHoughLinesProbabalistic( CvMat* image,
float rho, float theta, int threshold,
int lineLength, int lineGap,
CvSeq *lines, int linesMax )
{
cv::Mat accum, mask;
cv::vector<float> trigtab;
cv::MemStorage storage(cvCreateMemStorage(0));
CvSeq* seq;
CvSeqWriter writer;
int width, height;
int numangle, numrho;
float ang;
int r, n, count;
CvPoint pt;
float irho = 1 / rho;
CvRNG rng = cvRNG(-1);
const float* ttab;
uchar* mdata0;
CV_Assert( CV_IS_MAT(image) && CV_MAT_TYPE(image->type) == CV_8UC1 );
width = image->cols;
height = image->rows;
numangle = cvRound(CV_PI / theta);
numrho = cvRound(((width + height) * 2 + 1) / rho);
accum.create( numangle, numrho, CV_32SC1 );
mask.create( height, width, CV_8UC1 );
trigtab.resize(numangle*2);
accum = cv::Scalar(0);
for( ang = 0, n = 0; n < numangle; ang += theta, n++ )
{
trigtab[n*2] = (float)(cos(ang) * irho);
trigtab[n*2+1] = (float)(sin(ang) * irho);
}
ttab = &trigtab[0];
mdata0 = mask.data;
cvStartWriteSeq( CV_32SC2, sizeof(CvSeq), sizeof(CvPoint), storage, &writer );
// stage 1. collect non-zero image points
for( pt.y = 0, count = 0; pt.y < height; pt.y++ )
{
const uchar* data = image->data.ptr + pt.y*image->step;
uchar* mdata = mdata0 + pt.y*width;
for( pt.x = 0; pt.x < width; pt.x++ )
{
if( data[pt.x] )
{
mdata[pt.x] = (uchar)1;
CV_WRITE_SEQ_ELEM( pt, writer );
}
else
mdata[pt.x] = 0;
}
}
seq = cvEndWriteSeq( &writer );
count = seq->total;
// stage 2. process all the points in random order
for( ; count > 0; count-- )
{
// choose random point out of the remaining ones
int idx = cvRandInt(&rng) % count;
int max_val = threshold-1, max_n = 0;
CvPoint* pt = (CvPoint*)cvGetSeqElem( seq, idx );
CvPoint line_end[2] = {{0,0}, {0,0}};
float a, b;
int* adata = (int*)accum.data;
int i, j, k, x0, y0, dx0, dy0, xflag;
int good_line;
const int shift = 16;
i = pt->y;
j = pt->x;
// "remove" it by overriding it with the last element
*pt = *(CvPoint*)cvGetSeqElem( seq, count-1 );
// check if it has been excluded already (i.e. belongs to some other line)
if( !mdata0[i*width + j] )
continue;
// update accumulator, find the most probable line
for( n = 0; n < numangle; n++, adata += numrho )
{
r = cvRound( j * ttab[n*2] + i * ttab[n*2+1] );
r += (numrho - 1) / 2;
int val = ++adata[r];
if( max_val < val )
{
max_val = val;
max_n = n;
}
}
// if it is too "weak" candidate, continue with another point
if( max_val < threshold )
continue;
// from the current point walk in each direction
// along the found line and extract the line segment
a = -ttab[max_n*2+1];
b = ttab[max_n*2];
x0 = j;
y0 = i;
if( fabs(a) > fabs(b) )
{
xflag = 1;
dx0 = a > 0 ? 1 : -1;
dy0 = cvRound( b*(1 << shift)/fabs(a) );
y0 = (y0 << shift) + (1 << (shift-1));
}
else
{
xflag = 0;
dy0 = b > 0 ? 1 : -1;
dx0 = cvRound( a*(1 << shift)/fabs(b) );
x0 = (x0 << shift) + (1 << (shift-1));
}
for( k = 0; k < 2; k++ )
{
int gap = 0, x = x0, y = y0, dx = dx0, dy = dy0;
if( k > 0 )
dx = -dx, dy = -dy;
// walk along the line using fixed-point arithmetics,
// stop at the image border or in case of too big gap
for( ;; x += dx, y += dy )
{
uchar* mdata;
int i1, j1;
if( xflag )
{
j1 = x;
i1 = y >> shift;
}
else
{
j1 = x >> shift;
i1 = y;
}
if( j1 < 0 || j1 >= width || i1 < 0 || i1 >= height )
break;
mdata = mdata0 + i1*width + j1;
// for each non-zero point:
// update line end,
// clear the mask element
// reset the gap
if( *mdata )
{
gap = 0;
line_end[k].y = i1;
line_end[k].x = j1;
}
else if( ++gap > lineGap )
break;
}
}
good_line = abs(line_end[1].x - line_end[0].x) >= lineLength ||
abs(line_end[1].y - line_end[0].y) >= lineLength;
for( k = 0; k < 2; k++ )
{
int x = x0, y = y0, dx = dx0, dy = dy0;
if( k > 0 )
dx = -dx, dy = -dy;
// walk along the line using fixed-point arithmetics,
// stop at the image border or in case of too big gap
for( ;; x += dx, y += dy )
{
uchar* mdata;
int i1, j1;
if( xflag )
{
j1 = x;
i1 = y >> shift;
}
else
{
j1 = x >> shift;
i1 = y;
}
mdata = mdata0 + i1*width + j1;
// for each non-zero point:
// update line end,
// clear the mask element
// reset the gap
if( *mdata )
{
if( good_line )
{
adata = (int*)accum.data;
for( n = 0; n < numangle; n++, adata += numrho )
{
r = cvRound( j1 * ttab[n*2] + i1 * ttab[n*2+1] );
r += (numrho - 1) / 2;
adata[r]--;
}
}
*mdata = 0;
}
if( i1 == line_end[k].y && j1 == line_end[k].x )
break;
}
}
if( good_line )
{
CvRect lr = { line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y };
cvSeqPush( lines, &lr );
if( lines->total >= linesMax )
return;
}
}
}
/* Wrapper function for standard hough transform */
CV_IMPL CvSeq*
cvHoughLines2( CvArr* src_image, void* lineStorage, int method,
double rho, double theta, int threshold,
double param1, double param2 )
{
CvSeq* result = 0;
CvMat stub, *img = (CvMat*)src_image;
CvMat* mat = 0;
CvSeq* lines = 0;
CvSeq lines_header;
CvSeqBlock lines_block;
int lineType, elemSize;
int linesMax = INT_MAX;
int iparam1, iparam2;
img = cvGetMat( img, &stub );
if( !CV_IS_MASK_ARR(img))
CV_Error( CV_StsBadArg, "The source image must be 8-bit, single-channel" );
if( !lineStorage )
CV_Error( CV_StsNullPtr, "NULL destination" );
if( rho <= 0 || theta <= 0 || threshold <= 0 )
CV_Error( CV_StsOutOfRange, "rho, theta and threshold must be positive" );
if( method != CV_HOUGH_PROBABILISTIC )
{
lineType = CV_32FC2;
elemSize = sizeof(float)*2;
}
else
{
lineType = CV_32SC4;
elemSize = sizeof(int)*4;
}
if( CV_IS_STORAGE( lineStorage ))
{
lines = cvCreateSeq( lineType, sizeof(CvSeq), elemSize, (CvMemStorage*)lineStorage );
}
else if( CV_IS_MAT( lineStorage ))
{
mat = (CvMat*)lineStorage;
if( !CV_IS_MAT_CONT( mat->type ) || (mat->rows != 1 && mat->cols != 1) )
CV_Error( CV_StsBadArg,
"The destination matrix should be continuous and have a single row or a single column" );
if( CV_MAT_TYPE( mat->type ) != lineType )
CV_Error( CV_StsBadArg,
"The destination matrix data type is inappropriate, see the manual" );
lines = cvMakeSeqHeaderForArray( lineType, sizeof(CvSeq), elemSize, mat->data.ptr,
mat->rows + mat->cols - 1, &lines_header, &lines_block );
linesMax = lines->total;
cvClearSeq( lines );
}
else
CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
iparam1 = cvRound(param1);
iparam2 = cvRound(param2);
switch( method )
{
case CV_HOUGH_STANDARD:
icvHoughLinesStandard( img, (float)rho,
(float)theta, threshold, lines, linesMax );
break;
case CV_HOUGH_MULTI_SCALE:
icvHoughLinesSDiv( img, (float)rho, (float)theta,
threshold, iparam1, iparam2, lines, linesMax );
break;
case CV_HOUGH_PROBABILISTIC:
icvHoughLinesProbabalistic( img, (float)rho, (float)theta,
threshold, iparam1, iparam2, lines, linesMax );
break;
default:
CV_Error( CV_StsBadArg, "Unrecognized method id" );
}
if( mat )
{
if( mat->cols > mat->rows )
mat->cols = lines->total;
else
mat->rows = lines->total;
}
else
result = lines;
return result;
}
/****************************************************************************************\
* Circle Detection *
\****************************************************************************************/
static void
icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
int min_radius, int max_radius,
int canny_threshold, int acc_threshold,
CvSeq* circles, int circles_max )
{
const int SHIFT = 10, ONE = 1 << SHIFT, R_THRESH = 30;
cv::Ptr<CvMat> dx, dy;
cv::Ptr<CvMat> edges, accum, dist_buf;
std::vector<int> sort_buf;
cv::Ptr<CvMemStorage> storage;
int x, y, i, j, center_count, nz_count;
int rows, cols, arows, acols;
int astep, *adata;
float* ddata;
CvSeq *nz, *centers;
float idp, dr;
CvSeqReader reader;
edges = cvCreateMat( img->rows, img->cols, CV_8UC1 );
cvCanny( img, edges, MAX(canny_threshold/2,1), canny_threshold, 3 );
dx = cvCreateMat( img->rows, img->cols, CV_16SC1 );
dy = cvCreateMat( img->rows, img->cols, CV_16SC1 );
cvSobel( img, dx, 1, 0, 3 );
cvSobel( img, dy, 0, 1, 3 );
if( dp < 1.f )
dp = 1.f;
idp = 1.f/dp;
accum = cvCreateMat( cvCeil(img->rows*idp)+2, cvCeil(img->cols*idp)+2, CV_32SC1 );
cvZero(accum);
storage = cvCreateMemStorage();
nz = cvCreateSeq( CV_32SC2, sizeof(CvSeq), sizeof(CvPoint), storage );
centers = cvCreateSeq( CV_32SC1, sizeof(CvSeq), sizeof(int), storage );
rows = img->rows;
cols = img->cols;
arows = accum->rows - 2;
acols = accum->cols - 2;
adata = accum->data.i;
astep = accum->step/sizeof(adata[0]);
for( y = 0; y < rows; y++ )
{
const uchar* edges_row = edges->data.ptr + y*edges->step;
const short* dx_row = (const short*)(dx->data.ptr + y*dx->step);
const short* dy_row = (const short*)(dy->data.ptr + y*dy->step);
for( x = 0; x < cols; x++ )
{
float vx, vy;
int sx, sy, x0, y0, x1, y1, r, k;
CvPoint pt;
vx = dx_row[x];
vy = dy_row[x];
if( !edges_row[x] || (vx == 0 && vy == 0) )
continue;
float mag = sqrt(vx*vx+vy*vy);
assert( mag >= 1 );
sx = cvRound((vx*idp)*ONE/mag);
sy = cvRound((vy*idp)*ONE/mag);
x0 = cvRound((x*idp)*ONE);
y0 = cvRound((y*idp)*ONE);
for( k = 0; k < 2; k++ )
{
x1 = x0 + min_radius * sx;
y1 = y0 + min_radius * sy;
for( r = min_radius; r <= max_radius; x1 += sx, y1 += sy, r++ )
{
int x2 = x1 >> SHIFT, y2 = y1 >> SHIFT;
if( (unsigned)x2 >= (unsigned)acols ||
(unsigned)y2 >= (unsigned)arows )
break;
adata[y2*astep + x2]++;
}
sx = -sx; sy = -sy;
}
pt.x = x; pt.y = y;
cvSeqPush( nz, &pt );
}
}
nz_count = nz->total;
if( !nz_count )
return;
for( y = 1; y < arows - 1; y++ )
{
for( x = 1; x < acols - 1; x++ )
{
int base = y*(acols+2) + x;
if( adata[base] > acc_threshold &&
adata[base] > adata[base-1] && adata[base] > adata[base+1] &&
adata[base] > adata[base-acols-2] && adata[base] > adata[base+acols+2] )
cvSeqPush(centers, &base);
}
}
center_count = centers->total;
if( !center_count )
return;
sort_buf.resize( MAX(center_count,nz_count) );
cvCvtSeqToArray( centers, &sort_buf[0] );
icvHoughSortDescent32s( &sort_buf[0], center_count, adata );
cvClearSeq( centers );
cvSeqPushMulti( centers, &sort_buf[0], center_count );
dist_buf = cvCreateMat( 1, nz_count, CV_32FC1 );
ddata = dist_buf->data.fl;
dr = dp;
min_dist = MAX( min_dist, dp );
min_dist *= min_dist;
for( i = 0; i < centers->total; i++ )
{
int ofs = *(int*)cvGetSeqElem( centers, i );
y = ofs/(acols+2) - 1;
x = ofs - (y+1)*(acols+2) - 1;
float cx = (float)(x*dp), cy = (float)(y*dp);
int start_idx = nz_count - 1;
float start_dist, dist_sum;
float r_best = 0, c[3];
int max_count = R_THRESH;
for( j = 0; j < circles->total; j++ )
{
float* c = (float*)cvGetSeqElem( circles, j );
if( (c[0] - cx)*(c[0] - cx) + (c[1] - cy)*(c[1] - cy) < min_dist )
break;
}
if( j < circles->total )
continue;
cvStartReadSeq( nz, &reader );
for( j = 0; j < nz_count; j++ )
{
CvPoint pt;
float _dx, _dy;
CV_READ_SEQ_ELEM( pt, reader );
_dx = cx - pt.x; _dy = cy - pt.y;
ddata[j] = _dx*_dx + _dy*_dy;
sort_buf[j] = j;
}
cvPow( dist_buf, dist_buf, 0.5 );
icvHoughSortDescent32s( &sort_buf[0], nz_count, (int*)ddata );
dist_sum = start_dist = ddata[sort_buf[nz_count-1]];
for( j = nz_count - 2; j >= 0; j-- )
{
float d = ddata[sort_buf[j]];
if( d > max_radius )
break;
if( d - start_dist > dr )
{
float r_cur = ddata[sort_buf[(j + start_idx)/2]];
if( (start_idx - j)*r_best >= max_count*r_cur ||
(r_best < FLT_EPSILON && start_idx - j >= max_count) )
{
r_best = r_cur;
max_count = start_idx - j;
}
start_dist = d;
start_idx = j;
dist_sum = 0;
}
dist_sum += d;
}
if( max_count > R_THRESH )
{
c[0] = cx;
c[1] = cy;
c[2] = (float)r_best;
cvSeqPush( circles, c );
if( circles->total > circles_max )
return;
}
}
}
CV_IMPL CvSeq*
cvHoughCircles( CvArr* src_image, void* circle_storage,
int method, double dp, double min_dist,
double param1, double param2,
int min_radius, int max_radius )
{
CvSeq* result = 0;
CvMat stub, *img = (CvMat*)src_image;
CvMat* mat = 0;
CvSeq* circles = 0;
CvSeq circles_header;
CvSeqBlock circles_block;
int circles_max = INT_MAX;
int canny_threshold = cvRound(param1);
int acc_threshold = cvRound(param2);
img = cvGetMat( img, &stub );
if( !CV_IS_MASK_ARR(img))
CV_Error( CV_StsBadArg, "The source image must be 8-bit, single-channel" );
if( !circle_storage )
CV_Error( CV_StsNullPtr, "NULL destination" );
if( dp <= 0 || min_dist <= 0 || canny_threshold <= 0 || acc_threshold <= 0 )
CV_Error( CV_StsOutOfRange, "dp, min_dist, canny_threshold and acc_threshold must be all positive numbers" );
min_radius = MAX( min_radius, 0 );
if( max_radius <= 0 )
max_radius = MAX( img->rows, img->cols );
else if( max_radius <= min_radius )
max_radius = min_radius + 2;
if( CV_IS_STORAGE( circle_storage ))
{
circles = cvCreateSeq( CV_32FC3, sizeof(CvSeq),
sizeof(float)*3, (CvMemStorage*)circle_storage );
}
else if( CV_IS_MAT( circle_storage ))
{
mat = (CvMat*)circle_storage;
if( !CV_IS_MAT_CONT( mat->type ) || (mat->rows != 1 && mat->cols != 1) ||
CV_MAT_TYPE(mat->type) != CV_32FC3 )
CV_Error( CV_StsBadArg,
"The destination matrix should be continuous and have a single row or a single column" );
circles = cvMakeSeqHeaderForArray( CV_32FC3, sizeof(CvSeq), sizeof(float)*3,
mat->data.ptr, mat->rows + mat->cols - 1, &circles_header, &circles_block );
circles_max = circles->total;
cvClearSeq( circles );
}
else
CV_Error( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
switch( method )
{
case CV_HOUGH_GRADIENT:
icvHoughCirclesGradient( img, (float)dp, (float)min_dist,
min_radius, max_radius, canny_threshold,
acc_threshold, circles, circles_max );
break;
default:
CV_Error( CV_StsBadArg, "Unrecognized method id" );
}
if( mat )
{
if( mat->cols > mat->rows )
mat->cols = circles->total;
else
mat->rows = circles->total;
}
else
result = circles;
return result;
}
namespace cv
{
const int STORAGE_SIZE = 1 << 12;
void HoughLines( const Mat& image, vector<Vec2f>& lines,
double rho, double theta, int threshold,
double srn, double stn )
{
CvMemStorage* storage = cvCreateMemStorage(STORAGE_SIZE);
CvMat _image = image;
CvSeq* seq = cvHoughLines2( &_image, storage, srn == 0 && stn == 0 ?
CV_HOUGH_STANDARD : CV_HOUGH_MULTI_SCALE,
rho, theta, threshold, srn, stn );
Seq<Vec2f>(seq).copyTo(lines);
cvReleaseMemStorage(&storage);
}
void HoughLinesP( Mat& image, vector<Vec4i>& lines,
double rho, double theta, int threshold,
double minLineLength, double maxGap )
{
CvMemStorage* storage = cvCreateMemStorage(STORAGE_SIZE);
CvMat _image = image;
CvSeq* seq = cvHoughLines2( &_image, storage, CV_HOUGH_PROBABILISTIC,
rho, theta, threshold, minLineLength, maxGap );
Seq<Vec4i>(seq).copyTo(lines);
cvReleaseMemStorage(&storage);
}
void HoughCircles( const Mat& image, vector<Vec3f>& circles,
int method, double dp, double min_dist,
double param1, double param2,
int minRadius, int maxRadius )
{
CvMemStorage* storage = cvCreateMemStorage(STORAGE_SIZE);
CvMat _image = image;
CvSeq* seq = cvHoughCircles( &_image, storage, method,
dp, min_dist, param1, param2, minRadius, maxRadius );
Seq<Vec3f>(seq).copyTo(circles);
cvReleaseMemStorage(&storage);
}
}
/* End of file. */