www.pudn.com > OpenCV-Intel.zip > cvhough.cpp
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#include "_cv.h"
#include "_cvlist.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 CvStatus
icvHoughLines_8uC1R( uchar* image, int step, CvSize size,
float rho, float theta, int threshold,
CvSeq *lines, int linesMax )
{
int width, height;
int numangle, numrho;
int *accum = 0;
int *sort_buf=0;
int total = 0;
float *tabSin = 0;
float *tabCos = 0;
float ang;
int r, n;
int i, j;
float irho = 1 / rho;
double scale;
width = size.width;
height = size.height;
if( image == NULL )
return CV_NULLPTR_ERR;
if( width < 0 || height < 0 )
return CV_BADSIZE_ERR;
numangle = (int) (Pi / theta);
numrho = (int) (((width + height) * 2 + 1) / rho);
accum = (int*)cvAlloc( sizeof(accum[0]) * (numangle+2) * (numrho+2) );
sort_buf = (int*)cvAlloc( sizeof(accum[0]) * numangle * numrho );
tabSin = (float*)cvAlloc( sizeof(tabSin[0]) * numangle );
tabCos = (float*)cvAlloc( sizeof(tabCos[0]) * numangle );
memset( accum, 0, sizeof(accum[0]) * (numangle+2) * (numrho+2) );
if( tabSin == 0 || tabCos == 0 || accum == 0 )
goto func_exit;
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( j = 0; j < height; j++ )
for( i = 0; i < width; i++ )
{
if( image[j * step + i] != 0 )
for( n = 0; n < numangle; n++ )
{
r = cvRound( i * tabCos[n] + j * 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 );
}
func_exit:
cvFree( (void**)&tabSin );
cvFree( (void**)&tabCos );
cvFree( (void**)&accum );
return CV_OK;
}
/****************************************************************************************\
* 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 CvStatus icvHoughLinesSDiv_8uC1R( uchar * image_src, int step, CvSize size,
float rho, float theta, int threshold,
int srn, int stn,
CvSeq* lines, int linesMax )
{
#define _POINT(row, column)\
(image_src[(row)*step+(column)])
int rn, tn; /* number of rho and theta discrete values */
uchar *mcaccum = 0;
uchar *caccum = 0;
uchar *buffer = 0;
float *sinTable = 0;
int *x = 0;
int *y = 0;
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;
int w = size.width;
int h = size.height;
int fn = 0;
float xc, yc;
const float d2r = (float)(Pi / 180);
int sfn = srn * stn;
int fi;
int count;
int cmax = 0;
_CVLIST *list;
CVPOS pos;
_index *pindex;
_index vi;
if( image_src == NULL )
return CV_NULLPTR_ERR;
if( size.width < 0 || size.height < 0 )
return CV_BADSIZE_ERR;
if( linesMax == 0 || rho <= 0 || theta <= 0 )
return CV_BADFACTOR_ERR;
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 = (float*)cvAlloc( 5 * tn * stn * sizeof( float ));
for( index = 0; index < 5 * tn * stn; index++ )
{
sinTable[index] = (float)cos( stheta * index * 0.2f );
}
/* Allocating memory for the accumulator ad initializing it */
if( threshold > 255 )
goto func_exit;
caccum = (uchar*)cvAlloc( rn * tn * sizeof( 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 = (int*)cvAlloc( fn * sizeof(x[0]));
y = (int*)cvAlloc( fn * sizeof(y[0]));
/* Full Hough Transform (it's accumulator update part) */
fi = 0;
if( threshold < 256 )
{
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] = (unsigned char) (caccum[i] + ((i ^ iprev) != 0));
iprev = i;
if( cmax < caccum[i] )
cmax = caccum[i];
}
}
}
}
}
else
{
cvClearSeq( lines );
goto func_exit;
}
/* 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 )
{
icvHoughLines_8uC1R( image_src, step, size, rho, theta,
threshold, lines, linesMax );
goto func_exit;
}
buffer = (uchar *) cvAlloc( (srn * stn + 2) * sizeof( uchar ));
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 )
goto func_exit;
h_get_next__index( &pos );
}
}
func_exit:
h_destroy_list__index( list );
cvFree( (void**)&sinTable );
cvFree( (void**)&x );
cvFree( (void**)&y );
cvFree( (void**)&caccum );
cvFree( (void**)&buffer );
return CV_OK;
}
/****************************************************************************************\
* Probabilistic Hough Transform *
\****************************************************************************************/
#define _PHOUGH_SIN_TABLE
static CvStatus icvHoughLinesP_8uC1R( uchar * image_src, int step, CvSize size,
float rho, float theta, int threshold,
int lineLength, int lineGap,
CvSeq *lines, int linesMax )
{
#define _POINT(row, column)\
(image_src[(row)*step+(column)])
int *map = 0;
int rn, tn; /* number of rho and theta discrete values */
#define ROUNDR(x) cvFloor(x)
#define ROUNDT(x) cvFloor(x)
#ifdef _PHOUGH_SIN_TABLE
#define SIN(a) sinTable[a]
#else
#define SIN(a) sin((a)*theta)
#endif
int *iaccum = 0;
uchar *caccum = 0;
int imaccum;
uchar cmaccum;
int *x = 0;
int *y = 0;
int index, i;
int ri, ri1, ti, ti1, ti0;
int halftn;
int row, col;
float r, t; /* Current rho and theta */
float rv; /* Some temporary rho value */
float irho;
float itheta;
int w = size.width;
int h = size.height;
int fn = 0;
float xc, yc;
const float d2r = (float)(Pi / 180);
int fpn = 0;
float *sinTable = 0;
CvRNG state = CvRNG(0xffffffff);
if( linesMax <= 0 )
return CV_BADSIZE_ERR;
if( rho <= 0 || theta <= 0 )
return CV_BADARG_ERR;
irho = 1 / rho;
itheta = 1 / theta;
rn = cvFloor( sqrt( (double)w * w + (double)h * h ) * irho );
tn = cvFloor( 2 * Pi * itheta );
halftn = cvFloor( Pi * itheta );
/* Allocating memory for the accumulator ad initializing it */
if( threshold > 255 )
{
iaccum = (int *) cvAlloc( rn * tn * sizeof( int ));
memset( iaccum, 0, rn * tn * sizeof( int ));
}
else
{
caccum = (uchar *) cvAlloc( rn * tn * sizeof( uchar ));
memset( caccum, 0, rn * tn * sizeof( uchar ));
}
/* Counting all feature pixels */
for( row = 0; row < h; row++ )
{
for( col = 0; col < w; col++ )
{
fn += !!_POINT( row, col );
}
}
x = (int *) cvAlloc( fn * sizeof( int ));
y = (int *) cvAlloc( fn * sizeof( int ));
map = (int *) cvAlloc( w * h * sizeof( int ));
memset( map, -1, w * h * sizeof( int ));
#ifdef _PHOUGH_SIN_TABLE
sinTable = (float *) cvAlloc( tn * sizeof( float ));
for( ti = 0; ti < tn; ti++ )
{
sinTable[ti] = (float)sin( (double)(ti * theta) );
}
#endif
index = 0;
for( row = 0; row < h; row++ )
{
for( col = 0; col < w; col++ )
{
if( _POINT( row, col ))
{
x[index] = col;
y[index] = row;
map[row * w + col] = index;
index++;
}
}
}
/* Starting Hough Transform */
while( fn != 0 )
{
int temp;
int index0;
int cl; /* Counter of length of lines of feature pixels */
int cg; /* Counter of gaps length in lines of feature pixels */
float dx = 1.0f, dy = 0.0f, ax, ay;
float msx = 0, msy = 0, mex = 0, mey = 0, mdx = 1.0f, mdy = 0.0f;
int ml;
/* The x, y and length of a line (remember the maximum length) */
float curx = 0, cury = 0;
int ox, oy; /* Rounded ax and ay */
int ex, ey;
#define _EXCHANGE(x1, x2) temp = x1;x1 = x2;x2 = temp
/* Select a pixel randomly */
index0 = cvRandInt(&state) % fn;
/* Remove the pixel from the feature points set */
if( index0 != fn - 1 )
{
/* Exchange the point with the last one */
_EXCHANGE( x[index0], x[fn - 1] );
_EXCHANGE( y[index0], y[fn - 1] );
_EXCHANGE( map[y[index0] * w + x[index0]], map[y[fn - 1] * w + x[fn - 1]] );
}
fn--;
fpn++;
yc = (float) y[fn] + 0.5f;
xc = (float) x[fn] + 0.5f;
/* Update the accumulator */
t = (float) fabs( cvFastArctan( yc, xc ) * d2r );
r = (float) sqrt( (double)xc * xc + (double)yc * yc );
ti0 = ROUNDT( t * itheta );
/* ti1 = 0 */
if( threshold > 255 )
{
rv = 0.0f;
ri1 = 0;
i = ti0;
iaccum[i]++;
imaccum = iaccum[i];
ri = ri1;
ti = ti0;
for( ti1 = 1; ti1 < halftn; ti1++ )
{
rv = r * SIN( ti1 );
ri1 = ROUNDR( rv * irho );
i = ri1 * tn + ti1 + ti0;
iaccum[i]++;
if( imaccum < iaccum[i] )
{
imaccum = iaccum[i];
ri = ri1;
ti = ti1 + ti0;
}
}
r = ri * rho + rho / 2;
t = ti * theta + theta / 2;
if( iaccum[ri * tn + ti] < threshold )
{
continue;
}
/* Unvote all the pixels from the detected line */
iaccum[ri * tn + ti] = 0;
}
else
{
rv = 0.0f;
ri1 = 0;
i = ti0;
caccum[i]++;
cmaccum = caccum[i];
ri = ri1;
ti = ti0;
for( ti1 = 1; ti1 < halftn; ti1++ )
{
rv = r * SIN( ti1 );
ri1 = ROUNDR( rv * irho );
i = ri1 * tn + ti1 + ti0;
caccum[i]++;
if( cmaccum < caccum[i] )
{
cmaccum = caccum[i];
ri = ri1;
ti = ti1 + ti0;
}
}
r = ri * rho + rho / 2;
t = ti * theta + theta / 2;
if( caccum[ri * tn + ti] < threshold )
{
continue;
}
/* Unvote all the pixels from the detected line */
caccum[ri * tn + ti] = 0;
}
/* Find a longest segment representing the line */
/* Use an algorithm like Bresenheim one */
ml = 0;
for( i = 0; i < 7; i++ )
{
switch (i)
{
case 0:
break;
case 1:
r = ri * rho;
t = ti * theta - halfPi + 0.1f * theta;
break;
case 2:
r = ri * rho;
t = (ti + 1) * theta - halfPi - 0.1f * theta;
break;
case 3:
r = (ri + 1) * rho - 0.1f * rho;
t = ti * theta - halfPi + 0.1f * theta;
break;
case 4:
r = (ri + 1) * rho - 0.1f * rho;
t = (ti + 1) * theta - halfPi - 0.1f * theta;
break;
case 5:
r = ri * rho + 0.1f * rho;
t = ti * theta - halfPi + 0.5f * theta;
break;
case 6:
r = (ri + 1) * rho - 0.1f * rho;
t = ti * theta - halfPi + 0.5f * theta;
break;
}
if( t > Pi )
{
t = t - 2 * Pi;
}
if( t >= 0 )
{
if( t <= Pi / 2 )
{
dx = -(float) sin( t );
dy = (float) cos( t );
if( r < (w - 1) * fabs( dy ))
{
ax = (float) cvFloor( r / dy ) + 0.5f;
ay = 0.5f;
}
else
{
ax = (float) w - 0.5f;
ay = (float) cvFloor( (r - (w - 1) * dy) / (float) fabs( dx )) + 0.5f;
}
}
else
{
/* Pi/2 < t < Pi */
dx = (float) sin( t );
dy = -(float) cos( t );
ax = 0.5f;
ay = (float) cvFloor( r / dx ) + 0.5f;
}
}
else
{
/* -Pi/2 < t < 0 */
dx = -(float) sin( t );
dy = (float) cos( t );
ax = (float) cvFloor( r / dy ) + 0.5f;
ay = 0.5f;
}
cl = 0;
cg = 0;
ox = cvFloor( ax );
oy = cvFloor( ay );
while( ox >= 0 && ox < w && oy >= 0 && oy < h )
{
if( _POINT( oy, ox ))
{
if( cl == 0 )
{
/* A line has started */
curx = ax;
cury = ay;
}
cl++;
cg = 0; /* No gaps so far */
}
else if( cl )
{
if( ++cg > lineGap )
{
/* This is not a gap, the line has finished */
/* Let us remember it's parameters */
if( ml < cl )
{
msx = curx;
msy = cury;
mex = ax;
mey = ay;
mdx = dx;
mdy = dy;
ml = cl;
}
cl = 0;
cg = 0;
}
}
ax += dx;
ay += dy;
ox = cvFloor( ax );
oy = cvFloor( ay );
}
/* The last line if there was any... */
if( ml < cl )
{
msx = curx;
msy = cury;
mex = ax;
mey = ay;
mdx = dx;
mdy = dy;
ml = cl;
}
}
if( ml == 0 )
{
// no line...
continue;
}
/* Now let's remove all the pixels in the segment from the input image */
cl = 0;
cg = 0;
ax = msx;
ay = msy;
ox = cvFloor( msx );
oy = cvFloor( msy );
ex = cvFloor( mex );
ey = cvFloor( mey );
while( (ox != ex || oy != ey) && fn > 0 )
{
if( (unsigned)ox >= (unsigned)w || (unsigned)oy >= (unsigned)h )
break;
{
image_src[oy * step + ox] = 0;
index0 = map[oy * w + ox];
if( index0 != -1 )
{
if( index0 != fn - 1 )
{
/* Exchange the point with the last one */
_EXCHANGE( x[index0], x[fn - 1] );
_EXCHANGE( y[index0], y[fn - 1] );
_EXCHANGE( map[y[index0] * w + x[index0]],
map[y[fn - 1] * w + x[fn - 1]] );
}
fn--;
}
}
ax += mdx;
ay += mdy;
ox = cvFloor( ax );
oy = cvFloor( ay );
}
if( ml >= lineLength )
{
CvRect line;
line.x = cvFloor( msx );
line.y = cvFloor( msy );
line.width = cvFloor( mex );
line.height = cvFloor( mey );
cvSeqPush( lines, &line );
if( lines->total >= linesMax || fn == 0 )
goto func_exit;
}
}
func_exit:
cvFree( (void**)&x );
cvFree( (void**)&y );
cvFree( (void**)&map );
cvFree( (void**)&sinTable );
cvFree( (void**)&iaccum );
cvFree( (void**)&caccum );
return CV_OK;
}
/* 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;
CV_FUNCNAME( "cvHoughLines" );
__BEGIN__;
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;
CvSize size;
int iparam1, iparam2;
CV_CALL( 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 ))
{
CV_CALL( 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" );
CV_CALL( lines = cvMakeSeqHeaderForArray( lineType, sizeof(CvSeq), elemSize, mat->data.ptr,
mat->rows + mat->cols - 1, &lines_header, &lines_block ));
linesMax = lines->total;
CV_CALL( cvClearSeq( lines ));
}
else
{
CV_ERROR( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
}
size = cvGetMatSize(img);
iparam1 = cvRound(param1);
iparam2 = cvRound(param2);
switch( method )
{
case CV_HOUGH_STANDARD:
IPPI_CALL( icvHoughLines_8uC1R( img->data.ptr, img->step, size, (float)rho,
(float)theta, threshold, lines, linesMax ));
break;
case CV_HOUGH_MULTI_SCALE:
IPPI_CALL( icvHoughLinesSDiv_8uC1R( img->data.ptr, img->step, size, (float)rho,
(float)theta, threshold, iparam1,
iparam2, lines, linesMax ));
break;
case CV_HOUGH_PROBABILISTIC:
IPPI_CALL( icvHoughLinesP_8uC1R( img->data.ptr, img->step, size, (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;
}
__END__;
return result;
}
/****************************************************************************************\
* Circle Detection *
\****************************************************************************************/
static void
icvHoughCirclesGradient( CvMat* img, float dp, float min_dist,
int canny_threshold, int acc_threshold,
CvSeq* circles, int circles_max )
{
const int SHIFT = 10, ONE = 1 << SHIFT, R_THRESH = 30;
CvMat *dx = 0, *dy = 0;
CvMat *edges = 0;
CvMat *accum = 0;
int* sort_buf = 0;
CvMat* dist_buf = 0;
CvMemStorage* storage = 0;
CV_FUNCNAME( "icvHoughCirclesGradient" );
__BEGIN__;
int x, y, i, j, center_count, nz_count;
int rows, cols, arows, acols;
int astep, *adata;
float* ddata;
CvSize asize;
CvSeq *nz, *centers;
float idp, dr;
CvSeqReader reader;
CV_CALL( edges = cvCreateMat( img->rows, img->cols, CV_8UC1 ));
CV_CALL( cvCanny( img, edges, MAX(canny_threshold/2,1), canny_threshold, 3 ));
CV_CALL( dx = cvCreateMat( img->rows, img->cols, CV_16SC1 ));
CV_CALL( dy = cvCreateMat( img->rows, img->cols, CV_16SC1 ));
CV_CALL( cvSobel( img, dx, 1, 0, 3 ));
CV_CALL( cvSobel( img, dy, 0, 1, 3 ));
if( dp < 1.f )
dp = 1.f;
idp = 1.f/dp;
CV_CALL( accum = cvCreateMat( cvCeil(img->rows*idp)+2, cvCeil(img->cols*idp)+2, CV_32SC1 ));
CV_CALL( cvZero(accum));
asize = cvGetMatSize(accum);
CV_CALL( storage = cvCreateMemStorage() );
CV_CALL( nz = cvCreateSeq( CV_32SC2, sizeof(CvSeq), sizeof(CvPoint), storage ));
CV_CALL( 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;
CvPoint pt;
vx = dx_row[x];
vy = dy_row[x];
if( !edges_row[x] || vx == 0 && vy == 0 )
continue;
if( fabs(vx) < fabs(vy) )
{
sx = cvRound(vx*ONE/fabs(vy));
sy = vy < 0 ? -ONE : ONE;
}
else
{
assert( vx != 0 );
sy = cvRound(vy*ONE/fabs(vx));
sx = vx < 0 ? -ONE : ONE;
}
x0 = cvRound((x*idp)*ONE) + ONE + (ONE/2);
y0 = cvRound((y*idp)*ONE) + ONE + (ONE/2);
for( x1 = x0, y1 = y0;; x1 += sx, y1 += sy )
{
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;
for( x1 = x0 + sx, y1 = y0 + sy;; x1 += sx, y1 += sy )
{
int x2 = x1 >> SHIFT, y2 = y1 >> SHIFT;
if( (unsigned)x2 >= (unsigned)acols ||
(unsigned)y2 >= (unsigned)arows )
break;
adata[y2*astep + x2]++;
}
pt.x = x; pt.y = y;
cvSeqPush( nz, &pt );
}
}
nz_count = nz->total;
if( !nz_count )
EXIT;
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 )
EXIT;
CV_CALL( sort_buf = (int*)cvAlloc( MAX(center_count,nz_count)*sizeof(sort_buf[0]) ));
cvCvtSeqToArray( centers, sort_buf );
icvHoughSortDescent32s( sort_buf, center_count, adata );
cvClearSeq( centers );
cvSeqPushMulti( centers, sort_buf, center_count );
CV_CALL( 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, prev_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, nz_count, (int*)ddata );
dist_sum = prev_dist = start_dist = ddata[sort_buf[nz_count-1]];
for( j = nz_count - 2; j >= 0; j-- )
{
float d = ddata[sort_buf[j]];
if( d - start_dist > dr )
{
if( start_idx - j >= max_count )
{
r_best = ddata[sort_buf[(j + start_idx)/2]];
max_count = start_idx - j;
}
start_dist = d;
start_idx = j;
dist_sum = 0;
}
dist_sum += d;
prev_dist = 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 )
EXIT;
}
}
__END__;
cvReleaseMat( &dist_buf );
cvFree( (void**)&sort_buf );
cvReleaseMemStorage( &storage );
cvReleaseMat( &edges );
cvReleaseMat( &dx );
cvReleaseMat( &dy );
cvReleaseMat( &accum );
}
CV_IMPL CvSeq*
cvHoughCircles( CvArr* src_image, void* circle_storage,
int method, double dp, double min_dist,
double param1, double param2 )
{
CvSeq* result = 0;
CV_FUNCNAME( "cvHoughCircles" );
__BEGIN__;
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);
CvSize size;
CV_CALL( 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" );
if( CV_IS_STORAGE( circle_storage ))
{
CV_CALL( 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" );
CV_CALL( 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;
CV_CALL( cvClearSeq( circles ));
}
else
{
CV_ERROR( CV_StsBadArg, "Destination is not CvMemStorage* nor CvMat*" );
}
size = cvGetMatSize(img);
switch( method )
{
case CV_HOUGH_GRADIENT:
CV_CALL( icvHoughCirclesGradient( img, (float)dp, (float)min_dist,
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;
__END__;
return result;
}
/* End of file. */