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hough.cpp
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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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
// Copyright (C) 2014, Itseez, Inc, 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 the copyright holders 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 "opencl_kernels_imgproc.hpp"
#include "opencv2/core/hal/intrin.hpp"
#include <algorithm>
#include <iterator>
namespace cv
{
// Classical Hough Transform
struct LinePolar
{
float rho;
float angle;
};
struct hough_cmp_gt
{
hough_cmp_gt(const int* _aux) : aux(_aux) {}
inline bool operator()(int l1, int l2) const
{
return aux[l1] > aux[l2] || (aux[l1] == aux[l2] && l1 < l2);
}
const int* aux;
};
static void
createTrigTable( int numangle, double min_theta, double theta_step,
float irho, float *tabSin, float *tabCos )
{
float ang = static_cast<float>(min_theta);
for(int n = 0; n < numangle; ang += (float)theta_step, n++ )
{
tabSin[n] = (float)(sin((double)ang) * irho);
tabCos[n] = (float)(cos((double)ang) * irho);
}
}
static void
findLocalMaximums( int numrho, int numangle, int threshold,
const int *accum, std::vector<int>& sort_buf )
{
for(int r = 0; r < numrho; r++ )
for(int 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.push_back(base);
}
}
/*
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
HoughLinesStandard( const Mat& img, float rho, float theta,
int threshold, std::vector<Vec2f>& lines, int linesMax,
double min_theta, double max_theta )
{
int i, j;
float irho = 1 / rho;
CV_Assert( img.type() == CV_8UC1 );
const uchar* image = img.ptr();
int step = (int)img.step;
int width = img.cols;
int height = img.rows;
if (max_theta < min_theta ) {
CV_Error( CV_StsBadArg, "max_theta must be greater than min_theta" );
}
int numangle = cvRound((max_theta - min_theta) / theta);
int numrho = cvRound(((width + height) * 2 + 1) / rho);
#if defined HAVE_IPP && IPP_VERSION_X100 >= 810 && !IPP_DISABLE_HOUGH
CV_IPP_CHECK()
{
IppiSize srcSize = { width, height };
IppPointPolar delta = { rho, theta };
IppPointPolar dstRoi[2] = {{(Ipp32f) -(width + height), (Ipp32f) min_theta},{(Ipp32f) (width + height), (Ipp32f) max_theta}};
int bufferSize;
int nz = countNonZero(img);
int ipp_linesMax = std::min(linesMax, nz*numangle/threshold);
int linesCount = 0;
lines.resize(ipp_linesMax);
IppStatus ok = ippiHoughLineGetSize_8u_C1R(srcSize, delta, ipp_linesMax, &bufferSize);
Ipp8u* buffer = ippsMalloc_8u_L(bufferSize);
if (ok >= 0) {ok = CV_INSTRUMENT_FUN_IPP(ippiHoughLine_Region_8u32f_C1R, image, step, srcSize, (IppPointPolar*) &lines[0], dstRoi, ipp_linesMax, &linesCount, delta, threshold, buffer);};
ippsFree(buffer);
if (ok >= 0)
{
lines.resize(linesCount);
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
lines.clear();
setIppErrorStatus();
}
#endif
Mat _accum = Mat::zeros( (numangle+2), (numrho+2), CV_32SC1 );
std::vector<int> _sort_buf;
AutoBuffer<float> _tabSin(numangle);
AutoBuffer<float> _tabCos(numangle);
int *accum = _accum.ptr<int>();
float *tabSin = _tabSin, *tabCos = _tabCos;
// create sin and cos table
createTrigTable( numangle, min_theta, theta,
irho, tabSin, tabCos );
// stage 1. fill accumulator
for( i = 0; i < height; i++ )
for( j = 0; j < width; j++ )
{
if( image[i * step + j] != 0 )
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( j * tabCos[n] + i * tabSin[n] );
r += (numrho - 1) / 2;
accum[(n+1) * (numrho+2) + r+1]++;
}
}
// stage 2. find local maximums
findLocalMaximums( numrho, numangle, threshold, accum, _sort_buf );
// stage 3. sort the detected lines by accumulator value
std::sort(_sort_buf.begin(), _sort_buf.end(), hough_cmp_gt(accum));
// stage 4. store the first min(total,linesMax) lines to the output buffer
linesMax = std::min(linesMax, (int)_sort_buf.size());
double scale = 1./(numrho+2);
for( i = 0; i < linesMax; i++ )
{
LinePolar 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 = static_cast<float>(min_theta) + n * theta;
lines.push_back(Vec2f(line.rho, line.angle));
}
}
// Multi-Scale variant of Classical Hough Transform
struct hough_index
{
hough_index() : value(0), rho(0.f), theta(0.f) {}
hough_index(int _val, float _rho, float _theta)
: value(_val), rho(_rho), theta(_theta) {}
int value;
float rho, theta;
};
static void
HoughLinesSDiv( const Mat& img,
float rho, float theta, int threshold,
int srn, int stn,
std::vector<Vec2f>& lines, int linesMax,
double min_theta, double max_theta )
{
#define _POINT(row, column)\
(image_src[(row)*step+(column)])
int index, i;
int ri, ti, ti1, ti0;
int row, col;
float r, t; /* Current rho and theta */
float rv; /* Some temporary rho value */
int fn = 0;
float xc, yc;
const float d2r = (float)(CV_PI / 180);
int sfn = srn * stn;
int fi;
int count;
int cmax = 0;
std::vector<hough_index> lst;
CV_Assert( img.type() == CV_8UC1 );
CV_Assert( linesMax > 0 );
threshold = MIN( threshold, 255 );
const uchar* image_src = img.ptr();
int step = (int)img.step;
int w = img.cols;
int h = img.rows;
float irho = 1 / rho;
float itheta = 1 / theta;
float srho = rho / srn;
float stheta = theta / stn;
float isrho = 1 / srho;
float istheta = 1 / stheta;
int rn = cvFloor( std::sqrt( (double)w * w + (double)h * h ) * irho );
int tn = cvFloor( 2 * CV_PI * itheta );
lst.push_back(hough_index(threshold, -1.f, 0.f));
// Precalculate sin table
std::vector<float> _sinTable( 5 * tn * stn );
float* sinTable = &_sinTable[0];
for( index = 0; index < 5 * tn * stn; index++ )
sinTable[index] = (float)cos( stheta * index * 0.2f );
std::vector<uchar> _caccum(rn * tn, (uchar)0);
uchar* caccum = &_caccum[0];
// Counting all feature pixels
for( row = 0; row < h; row++ )
for( col = 0; col < w; col++ )
fn += _POINT( row, col ) != 0;
std::vector<int> _x(fn), _y(fn);
int* 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) std::sqrt( (double)xc * xc + (double)yc * yc );
r0 = r * irho;
ti0 = cvFloor( (t + CV_PI*0.5) * itheta );
caccum[ti0]++;
theta_it = rho / r;
theta_it = theta_it < theta ? theta_it : theta;
scale_factor = theta_it * itheta;
halftn = cvFloor( CV_PI / theta_it );
for( ti1 = 1, phi = theta_it - (float)(CV_PI*0.5), phi1 = (theta_it + t) * itheta;
ti1 < halftn; ti1++, phi += theta_it, phi1 += scale_factor )
{
rv = r0 * std::cos( phi );
i = (int)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 )
{
HoughLinesStandard( img, rho, theta, threshold, lines, linesMax, min_theta, max_theta );
return;
}
std::vector<uchar> _buffer(srn * stn + 2);
uchar* buffer = &_buffer[0];
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) std::sqrt( (double)xc * xc + (double)yc * yc ) * isrho;
ti0 = cvFloor( (t + CV_PI * 0.5) * istheta );
ti2 = (ti * stn - ti0) * 5;
r0 = (float) ri *srn;
for( ti1 = 0; ti1 < stn; ti1++, ti2 += 5 )
{
rv = r * sinTable[(int) (std::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;
int pos = (int)(lst.size() - 1);
if( pos < 0 || lst[pos].value < mcaccum[index] )
{
hough_index vi(mcaccum[index],
index / stn * srho + ri * rho,
index % stn * stheta + ti * theta - (float)(CV_PI*0.5));
lst.push_back(vi);
for( ; pos >= 0; pos-- )
{
if( lst[pos].value > vi.value )
break;
lst[pos+1] = lst[pos];
}
lst[pos+1] = vi;
if( (int)lst.size() > linesMax )
lst.pop_back();
}
}
}
}
}
for( size_t idx = 0; idx < lst.size(); idx++ )
{
if( lst[idx].rho < 0 )
continue;
lines.push_back(Vec2f(lst[idx].rho, lst[idx].theta));
}
}
/****************************************************************************************\
* Probabilistic Hough Transform *
\****************************************************************************************/
static void
HoughLinesProbabilistic( Mat& image,
float rho, float theta, int threshold,
int lineLength, int lineGap,
std::vector<Vec4i>& lines, int linesMax )
{
Point pt;
float irho = 1 / rho;
RNG rng((uint64)-1);
CV_Assert( image.type() == CV_8UC1 );
int width = image.cols;
int height = image.rows;
int numangle = cvRound(CV_PI / theta);
int numrho = cvRound(((width + height) * 2 + 1) / rho);
#if defined HAVE_IPP && IPP_VERSION_X100 >= 810 && !IPP_DISABLE_HOUGH
CV_IPP_CHECK()
{
IppiSize srcSize = { width, height };
IppPointPolar delta = { rho, theta };
IppiHoughProbSpec* pSpec;
int bufferSize, specSize;
int ipp_linesMax = std::min(linesMax, numangle*numrho);
int linesCount = 0;
lines.resize(ipp_linesMax);
IppStatus ok = ippiHoughProbLineGetSize_8u_C1R(srcSize, delta, &specSize, &bufferSize);
Ipp8u* buffer = ippsMalloc_8u_L(bufferSize);
pSpec = (IppiHoughProbSpec*) ippsMalloc_8u_L(specSize);
if (ok >= 0) ok = ippiHoughProbLineInit_8u32f_C1R(srcSize, delta, ippAlgHintNone, pSpec);
if (ok >= 0) {ok = CV_INSTRUMENT_FUN_IPP(ippiHoughProbLine_8u32f_C1R, image.data, (int)image.step, srcSize, threshold, lineLength, lineGap, (IppiPoint*) &lines[0], ipp_linesMax, &linesCount, buffer, pSpec);};
ippsFree(pSpec);
ippsFree(buffer);
if (ok >= 0)
{
lines.resize(linesCount);
CV_IMPL_ADD(CV_IMPL_IPP);
return;
}
lines.clear();
setIppErrorStatus();
}
#endif
Mat accum = Mat::zeros( numangle, numrho, CV_32SC1 );
Mat mask( height, width, CV_8UC1 );
std::vector<float> trigtab(numangle*2);
for( int n = 0; n < numangle; n++ )
{
trigtab[n*2] = (float)(cos((double)n*theta) * irho);
trigtab[n*2+1] = (float)(sin((double)n*theta) * irho);
}
const float* ttab = &trigtab[0];
uchar* mdata0 = mask.ptr();
std::vector<Point> nzloc;
// stage 1. collect non-zero image points
for( pt.y = 0; pt.y < height; pt.y++ )
{
const uchar* data = image.ptr(pt.y);
uchar* mdata = mask.ptr(pt.y);
for( pt.x = 0; pt.x < width; pt.x++ )
{
if( data[pt.x] )
{
mdata[pt.x] = (uchar)1;
nzloc.push_back(pt);
}
else
mdata[pt.x] = 0;
}
}
int count = (int)nzloc.size();
// stage 2. process all the points in random order
for( ; count > 0; count-- )
{
// choose random point out of the remaining ones
int idx = rng.uniform(0, count);
int max_val = threshold-1, max_n = 0;
Point point = nzloc[idx];
Point line_end[2];
float a, b;
int* adata = accum.ptr<int>();
int i = point.y, j = point.x, k, x0, y0, dx0, dy0, xflag;
int good_line;
const int shift = 16;
// "remove" it by overriding it with the last element
nzloc[idx] = nzloc[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( int n = 0; n < numangle; n++, adata += numrho )
{
int 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 = std::abs(line_end[1].x - line_end[0].x) >= lineLength ||
std::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 = accum.ptr<int>();
for( int n = 0; n < numangle; n++, adata += numrho )
{
int 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 )
{
Vec4i lr(line_end[0].x, line_end[0].y, line_end[1].x, line_end[1].y);
lines.push_back(lr);
if( (int)lines.size() >= linesMax )
return;
}
}
}
#ifdef HAVE_OPENCL
#define OCL_MAX_LINES 4096
static bool ocl_makePointsList(InputArray _src, OutputArray _pointsList, InputOutputArray _counters)
{
UMat src = _src.getUMat();
_pointsList.create(1, (int) src.total(), CV_32SC1);
UMat pointsList = _pointsList.getUMat();
UMat counters = _counters.getUMat();
ocl::Device dev = ocl::Device::getDefault();
const int pixPerWI = 16;
int workgroup_size = min((int) dev.maxWorkGroupSize(), (src.cols + pixPerWI - 1)/pixPerWI);
ocl::Kernel pointListKernel("make_point_list", ocl::imgproc::hough_lines_oclsrc,
format("-D MAKE_POINTS_LIST -D GROUP_SIZE=%d -D LOCAL_SIZE=%d", workgroup_size, src.cols));
if (pointListKernel.empty())
return false;
pointListKernel.args(ocl::KernelArg::ReadOnly(src), ocl::KernelArg::WriteOnlyNoSize(pointsList),
ocl::KernelArg::PtrWriteOnly(counters));
size_t localThreads[2] = { (size_t)workgroup_size, 1 };
size_t globalThreads[2] = { (size_t)workgroup_size, (size_t)src.rows };
return pointListKernel.run(2, globalThreads, localThreads, false);
}
static bool ocl_fillAccum(InputArray _pointsList, OutputArray _accum, int total_points, double rho, double theta, int numrho, int numangle)
{
UMat pointsList = _pointsList.getUMat();
_accum.create(numangle + 2, numrho + 2, CV_32SC1);
UMat accum = _accum.getUMat();
ocl::Device dev = ocl::Device::getDefault();
float irho = (float) (1 / rho);
int workgroup_size = min((int) dev.maxWorkGroupSize(), total_points);
ocl::Kernel fillAccumKernel;
size_t localThreads[2];
size_t globalThreads[2];
size_t local_memory_needed = (numrho + 2)*sizeof(int);
if (local_memory_needed > dev.localMemSize())
{
accum.setTo(Scalar::all(0));
fillAccumKernel.create("fill_accum_global", ocl::imgproc::hough_lines_oclsrc,
format("-D FILL_ACCUM_GLOBAL"));
if (fillAccumKernel.empty())
return false;
globalThreads[0] = workgroup_size; globalThreads[1] = numangle;
fillAccumKernel.args(ocl::KernelArg::ReadOnlyNoSize(pointsList), ocl::KernelArg::WriteOnlyNoSize(accum),
total_points, irho, (float) theta, numrho, numangle);
return fillAccumKernel.run(2, globalThreads, NULL, false);
}
else
{
fillAccumKernel.create("fill_accum_local", ocl::imgproc::hough_lines_oclsrc,
format("-D FILL_ACCUM_LOCAL -D LOCAL_SIZE=%d -D BUFFER_SIZE=%d", workgroup_size, numrho + 2));
if (fillAccumKernel.empty())
return false;
localThreads[0] = workgroup_size; localThreads[1] = 1;
globalThreads[0] = workgroup_size; globalThreads[1] = numangle+2;
fillAccumKernel.args(ocl::KernelArg::ReadOnlyNoSize(pointsList), ocl::KernelArg::WriteOnlyNoSize(accum),
total_points, irho, (float) theta, numrho, numangle);
return fillAccumKernel.run(2, globalThreads, localThreads, false);
}
}
static bool ocl_HoughLines(InputArray _src, OutputArray _lines, double rho, double theta, int threshold,
double min_theta, double max_theta)
{
CV_Assert(_src.type() == CV_8UC1);
if (max_theta < 0 || max_theta > CV_PI ) {
CV_Error( Error::StsBadArg, "max_theta must fall between 0 and pi" );
}
if (min_theta < 0 || min_theta > max_theta ) {
CV_Error( Error::StsBadArg, "min_theta must fall between 0 and max_theta" );
}
if (!(rho > 0 && theta > 0)) {
CV_Error( Error::StsBadArg, "rho and theta must be greater 0" );
}
UMat src = _src.getUMat();
int numangle = cvRound((max_theta - min_theta) / theta);
int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho);
UMat pointsList;
UMat counters(1, 2, CV_32SC1, Scalar::all(0));
if (!ocl_makePointsList(src, pointsList, counters))
return false;
int total_points = counters.getMat(ACCESS_READ).at<int>(0, 0);
if (total_points <= 0)
{
_lines.assign(UMat(0,0,CV_32FC2));
return true;
}
UMat accum;
if (!ocl_fillAccum(pointsList, accum, total_points, rho, theta, numrho, numangle))
return false;
const int pixPerWI = 8;
ocl::Kernel getLinesKernel("get_lines", ocl::imgproc::hough_lines_oclsrc,
format("-D GET_LINES"));
if (getLinesKernel.empty())
return false;
int linesMax = threshold > 0 ? min(total_points*numangle/threshold, OCL_MAX_LINES) : OCL_MAX_LINES;
UMat lines(linesMax, 1, CV_32FC2);
getLinesKernel.args(ocl::KernelArg::ReadOnly(accum), ocl::KernelArg::WriteOnlyNoSize(lines),
ocl::KernelArg::PtrWriteOnly(counters), linesMax, threshold, (float) rho, (float) theta);
size_t globalThreads[2] = { ((size_t)numrho + pixPerWI - 1)/pixPerWI, (size_t)numangle };
if (!getLinesKernel.run(2, globalThreads, NULL, false))
return false;
int total_lines = min(counters.getMat(ACCESS_READ).at<int>(0, 1), linesMax);
if (total_lines > 0)
_lines.assign(lines.rowRange(Range(0, total_lines)));
else
_lines.assign(UMat(0,0,CV_32FC2));
return true;
}
static bool ocl_HoughLinesP(InputArray _src, OutputArray _lines, double rho, double theta, int threshold,
double minLineLength, double maxGap)
{
CV_Assert(_src.type() == CV_8UC1);
if (!(rho > 0 && theta > 0)) {
CV_Error( Error::StsBadArg, "rho and theta must be greater 0" );
}
UMat src = _src.getUMat();
int numangle = cvRound(CV_PI / theta);
int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho);
UMat pointsList;
UMat counters(1, 2, CV_32SC1, Scalar::all(0));
if (!ocl_makePointsList(src, pointsList, counters))
return false;
int total_points = counters.getMat(ACCESS_READ).at<int>(0, 0);
if (total_points <= 0)
{
_lines.assign(UMat(0,0,CV_32SC4));
return true;
}
UMat accum;
if (!ocl_fillAccum(pointsList, accum, total_points, rho, theta, numrho, numangle))
return false;
ocl::Kernel getLinesKernel("get_lines", ocl::imgproc::hough_lines_oclsrc,
format("-D GET_LINES_PROBABOLISTIC"));
if (getLinesKernel.empty())
return false;
int linesMax = threshold > 0 ? min(total_points*numangle/threshold, OCL_MAX_LINES) : OCL_MAX_LINES;
UMat lines(linesMax, 1, CV_32SC4);
getLinesKernel.args(ocl::KernelArg::ReadOnly(accum), ocl::KernelArg::ReadOnly(src),
ocl::KernelArg::WriteOnlyNoSize(lines), ocl::KernelArg::PtrWriteOnly(counters),
linesMax, threshold, (int) minLineLength, (int) maxGap, (float) rho, (float) theta);
size_t globalThreads[2] = { (size_t)numrho, (size_t)numangle };
if (!getLinesKernel.run(2, globalThreads, NULL, false))
return false;
int total_lines = min(counters.getMat(ACCESS_READ).at<int>(0, 1), linesMax);
if (total_lines > 0)
_lines.assign(lines.rowRange(Range(0, total_lines)));
else
_lines.assign(UMat(0,0,CV_32SC4));
return true;
}
#endif /* HAVE_OPENCL */
void HoughLines( InputArray _image, OutputArray _lines,
double rho, double theta, int threshold,
double srn, double stn, double min_theta, double max_theta )
{
CV_INSTRUMENT_REGION()
CV_OCL_RUN(srn == 0 && stn == 0 && _image.isUMat() && _lines.isUMat(),
ocl_HoughLines(_image, _lines, rho, theta, threshold, min_theta, max_theta));
Mat image = _image.getMat();
std::vector<Vec2f> lines;
if( srn == 0 && stn == 0 )
HoughLinesStandard(image, (float)rho, (float)theta, threshold, lines, INT_MAX, min_theta, max_theta );
else
HoughLinesSDiv(image, (float)rho, (float)theta, threshold, cvRound(srn), cvRound(stn), lines, INT_MAX, min_theta, max_theta);
Mat(lines).copyTo(_lines);
}
void HoughLinesP(InputArray _image, OutputArray _lines,
double rho, double theta, int threshold,
double minLineLength, double maxGap )
{
CV_INSTRUMENT_REGION()
CV_OCL_RUN(_image.isUMat() && _lines.isUMat(),
ocl_HoughLinesP(_image, _lines, rho, theta, threshold, minLineLength, maxGap));
Mat image = _image.getMat();
std::vector<Vec4i> lines;
HoughLinesProbabilistic(image, (float)rho, (float)theta, threshold, cvRound(minLineLength), cvRound(maxGap), lines, INT_MAX);
Mat(lines).copyTo(_lines);
}
void HoughLinesPointSet( InputArray _point, OutputArray _lines, int lines_max, int threshold,
double min_rho, double max_rho, double rho_step,
double min_theta, double max_theta, double theta_step )
{
std::vector<Vec3d> lines;
std::vector<Point2f> point;
_point.copyTo(point);
CV_Assert( _point.type() == CV_32FC2 || _point.type() == CV_32SC2 );
if( lines_max <= 0 ) {
CV_Error( Error::StsBadArg, "lines_max must be greater than 0" );
}
if( threshold < 0) {
CV_Error( Error::StsBadArg, "threshold must be greater than 0" );
}
if( ((max_rho - min_rho) <= 0) || ((max_theta - min_theta) <= 0) ) {
CV_Error( Error::StsBadArg, "max must be greater than min" );
}
if( ((rho_step <= 0)) || ((theta_step <= 0)) ) {
CV_Error( Error::StsBadArg, "step must be greater than 0" );
}
int i;
float irho = 1 / (float)rho_step;
float irho_min = ((float)min_rho * irho);
int numangle = cvRound((max_theta - min_theta) / theta_step);
int numrho = cvRound((max_rho - min_rho + 1) / rho_step);
Mat _accum = Mat::zeros( (numangle+2), (numrho+2), CV_32SC1 );
std::vector<int> _sort_buf;
AutoBuffer<float> _tabSin(numangle);
AutoBuffer<float> _tabCos(numangle);
int *accum = _accum.ptr<int>();
float *tabSin = _tabSin, *tabCos = _tabCos;
// create sin and cos table
createTrigTable( numangle, min_theta, theta_step,
irho, tabSin, tabCos );
// stage 1. fill accumlator
for( i = 0; i < (int)point.size(); i++ )
for(int n = 0; n < numangle; n++ )
{
int r = cvRound( point.at(i).x * tabCos[n] + point.at(i).y * tabSin[n] - irho_min);
accum[(n+1) * (numrho+2) + r+1]++;
}
// stage 2. find local maximums
findLocalMaximums( numrho, numangle, threshold, accum, _sort_buf );
// stage 3. sort the detected lines by accumulator value
std::sort(_sort_buf.begin(), _sort_buf.end(), hough_cmp_gt(accum));
// stage 4. store the first min(total,linesMax) lines to the output buffer
lines_max = std::min(lines_max, (int)_sort_buf.size());
double scale = 1./(numrho+2);
for( i = 0; i < lines_max; i++ )
{
LinePolar line;
int idx = _sort_buf[i];
int n = cvFloor(idx*scale) - 1;
int r = idx - (n+1)*(numrho+2) - 1;
line.rho = static_cast<float>(min_rho) + r * (float)rho_step;
line.angle = static_cast<float>(min_theta) + n * (float)theta_step;
lines.push_back(Vec3d((double)accum[idx], (double)line.rho, (double)line.angle));
}
Mat(lines).copyTo(_lines);
}
/****************************************************************************************\
* Circle Detection *
\****************************************************************************************/
struct EstimatedCircle
{
EstimatedCircle(Vec3f _c, int _accum) :
c(_c), accum(_accum) {}
Vec3f c;
int accum;
};
static bool cmpAccum(const EstimatedCircle& left, const EstimatedCircle& right)
{
// Compare everything so the order is completely deterministic
// Larger accum first
if (left.accum > right.accum)
return true;
else if (left.accum < right.accum)
return false;
// Larger radius first
else if (left.c[2] > right.c[2])
return true;
else if (left.c[2] < right.c[2])
return false;
// Smaller X
else if (left.c[0] < right.c[0])
return true;
else if (left.c[0] > right.c[0])
return false;
// Smaller Y