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opencv/modules/gpu/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.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage 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"
using namespace cv;
using namespace cv::gpu;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::HoughLines(const GpuMat&, GpuMat&, float, float, int, bool, int) { throw_nogpu(); }
void cv::gpu::HoughLines(const GpuMat&, GpuMat&, HoughLinesBuf&, float, float, int, bool, int) { throw_nogpu(); }
void cv::gpu::HoughLinesDownload(const GpuMat&, OutputArray, OutputArray) { throw_nogpu(); }
void cv::gpu::HoughLinesP(const GpuMat&, GpuMat&, HoughLinesBuf&, float, float, int, int, int) { throw_nogpu(); }
void cv::gpu::HoughCircles(const GpuMat&, GpuMat&, int, float, float, int, int, int, int, int) { throw_nogpu(); }
void cv::gpu::HoughCircles(const GpuMat&, GpuMat&, HoughCirclesBuf&, int, float, float, int, int, int, int, int) { throw_nogpu(); }
void cv::gpu::HoughCirclesDownload(const GpuMat&, OutputArray) { throw_nogpu(); }
Ptr<GeneralizedHough_GPU> cv::gpu::GeneralizedHough_GPU::create(int) { throw_nogpu(); return Ptr<GeneralizedHough_GPU>(); }
cv::gpu::GeneralizedHough_GPU::~GeneralizedHough_GPU() {}
void cv::gpu::GeneralizedHough_GPU::setTemplate(const GpuMat&, int, Point) { throw_nogpu(); }
void cv::gpu::GeneralizedHough_GPU::setTemplate(const GpuMat&, const GpuMat&, const GpuMat&, Point) { throw_nogpu(); }
void cv::gpu::GeneralizedHough_GPU::detect(const GpuMat&, GpuMat&, int) { throw_nogpu(); }
void cv::gpu::GeneralizedHough_GPU::detect(const GpuMat&, const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
void cv::gpu::GeneralizedHough_GPU::download(const GpuMat&, OutputArray, OutputArray) { throw_nogpu(); }
void cv::gpu::GeneralizedHough_GPU::release() {}
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace gpu { namespace device
{
namespace hough
{
int buildPointList_gpu(PtrStepSzb src, unsigned int* list);
}
}}}
//////////////////////////////////////////////////////////
// HoughLines
namespace cv { namespace gpu { namespace device
{
namespace hough
{
void linesAccum_gpu(const unsigned int* list, int count, PtrStepSzi accum, float rho, float theta, size_t sharedMemPerBlock, bool has20);
int linesGetResult_gpu(PtrStepSzi accum, float2* out, int* votes, int maxSize, float rho, float theta, int threshold, bool doSort);
}
}}}
void cv::gpu::HoughLines(const GpuMat& src, GpuMat& lines, float rho, float theta, int threshold, bool doSort, int maxLines)
{
HoughLinesBuf buf;
HoughLines(src, lines, buf, rho, theta, threshold, doSort, maxLines);
}
void cv::gpu::HoughLines(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int threshold, bool doSort, int maxLines)
{
using namespace cv::gpu::device::hough;
CV_Assert(src.type() == CV_8UC1);
CV_Assert(src.cols < std::numeric_limits<unsigned short>::max());
CV_Assert(src.rows < std::numeric_limits<unsigned short>::max());
ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf.list);
unsigned int* srcPoints = buf.list.ptr<unsigned int>();
const int pointsCount = buildPointList_gpu(src, srcPoints);
if (pointsCount == 0)
{
lines.release();
return;
}
const int numangle = cvRound(CV_PI / theta);
const int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho);
CV_Assert(numangle > 0 && numrho > 0);
ensureSizeIsEnough(numangle + 2, numrho + 2, CV_32SC1, buf.accum);
buf.accum.setTo(Scalar::all(0));
DeviceInfo devInfo;
linesAccum_gpu(srcPoints, pointsCount, buf.accum, rho, theta, devInfo.sharedMemPerBlock(), devInfo.supports(FEATURE_SET_COMPUTE_20));
ensureSizeIsEnough(2, maxLines, CV_32FC2, lines);
int linesCount = linesGetResult_gpu(buf.accum, lines.ptr<float2>(0), lines.ptr<int>(1), maxLines, rho, theta, threshold, doSort);
if (linesCount > 0)
lines.cols = linesCount;
else
lines.release();
}
void cv::gpu::HoughLinesDownload(const GpuMat& d_lines, OutputArray h_lines_, OutputArray h_votes_)
{
if (d_lines.empty())
{
h_lines_.release();
if (h_votes_.needed())
h_votes_.release();
return;
}
CV_Assert(d_lines.rows == 2 && d_lines.type() == CV_32FC2);
h_lines_.create(1, d_lines.cols, CV_32FC2);
Mat h_lines = h_lines_.getMat();
d_lines.row(0).download(h_lines);
if (h_votes_.needed())
{
h_votes_.create(1, d_lines.cols, CV_32SC1);
Mat h_votes = h_votes_.getMat();
GpuMat d_votes(1, d_lines.cols, CV_32SC1, const_cast<int*>(d_lines.ptr<int>(1)));
d_votes.download(h_votes);
}
}
//////////////////////////////////////////////////////////
// HoughLinesP
namespace cv { namespace gpu { namespace device
{
namespace hough
{
int houghLinesProbabilistic_gpu(PtrStepSzb mask, PtrStepSzi accum, int4* out, int maxSize, float rho, float theta, int lineGap, int lineLength);
}
}}}
void cv::gpu::HoughLinesP(const GpuMat& src, GpuMat& lines, HoughLinesBuf& buf, float rho, float theta, int minLineLength, int maxLineGap, int maxLines)
{
using namespace cv::gpu::device::hough;
CV_Assert( src.type() == CV_8UC1 );
CV_Assert( src.cols < std::numeric_limits<unsigned short>::max() );
CV_Assert( src.rows < std::numeric_limits<unsigned short>::max() );
ensureSizeIsEnough(1, src.size().area(), CV_32SC1, buf.list);
unsigned int* srcPoints = buf.list.ptr<unsigned int>();
const int pointsCount = buildPointList_gpu(src, srcPoints);
if (pointsCount == 0)
{
lines.release();
return;
}
const int numangle = cvRound(CV_PI / theta);
const int numrho = cvRound(((src.cols + src.rows) * 2 + 1) / rho);
CV_Assert( numangle > 0 && numrho > 0 );
ensureSizeIsEnough(numangle + 2, numrho + 2, CV_32SC1, buf.accum);
buf.accum.setTo(Scalar::all(0));
DeviceInfo devInfo;
linesAccum_gpu(srcPoints, pointsCount, buf.accum, rho, theta, devInfo.sharedMemPerBlock(), devInfo.supports(FEATURE_SET_COMPUTE_20));
ensureSizeIsEnough(1, maxLines, CV_32SC4, lines);
int linesCount = houghLinesProbabilistic_gpu(src, buf.accum, lines.ptr<int4>(), maxLines, rho, theta, maxLineGap, minLineLength);
if (linesCount > 0)
lines.cols = linesCount;
else
lines.release();
}
//////////////////////////////////////////////////////////
// HoughCircles
namespace cv { namespace gpu { namespace device
{
namespace hough
{
void circlesAccumCenters_gpu(const unsigned int* list, int count, PtrStepi dx, PtrStepi dy, PtrStepSzi accum, int minRadius, int maxRadius, float idp);
int buildCentersList_gpu(PtrStepSzi accum, unsigned int* centers, int threshold);
int circlesAccumRadius_gpu(const unsigned int* centers, int centersCount, const unsigned int* list, int count,
float3* circles, int maxCircles, float dp, int minRadius, int maxRadius, int threshold, bool has20);
}
}}}
void cv::gpu::HoughCircles(const GpuMat& src, GpuMat& circles, int method, float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
{
HoughCirclesBuf buf;
HoughCircles(src, circles, buf, method, dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, maxCircles);
}
void cv::gpu::HoughCircles(const GpuMat& src, GpuMat& circles, HoughCirclesBuf& buf, int method,
float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
{
using namespace cv::gpu::device::hough;
CV_Assert(src.type() == CV_8UC1);
CV_Assert(src.cols < std::numeric_limits<unsigned short>::max());
CV_Assert(src.rows < std::numeric_limits<unsigned short>::max());
CV_Assert(method == CV_HOUGH_GRADIENT);
CV_Assert(dp > 0);
CV_Assert(minRadius > 0 && maxRadius > minRadius);
CV_Assert(cannyThreshold > 0);
CV_Assert(votesThreshold > 0);
CV_Assert(maxCircles > 0);
const float idp = 1.0f / dp;
cv::gpu::Canny(src, buf.cannyBuf, buf.edges, std::max(cannyThreshold / 2, 1), cannyThreshold);
ensureSizeIsEnough(2, src.size().area(), CV_32SC1, buf.list);
unsigned int* srcPoints = buf.list.ptr<unsigned int>(0);
unsigned int* centers = buf.list.ptr<unsigned int>(1);
const int pointsCount = buildPointList_gpu(buf.edges, srcPoints);
if (pointsCount == 0)
{
circles.release();
return;
}
ensureSizeIsEnough(cvCeil(src.rows * idp) + 2, cvCeil(src.cols * idp) + 2, CV_32SC1, buf.accum);
buf.accum.setTo(Scalar::all(0));
circlesAccumCenters_gpu(srcPoints, pointsCount, buf.cannyBuf.dx, buf.cannyBuf.dy, buf.accum, minRadius, maxRadius, idp);
int centersCount = buildCentersList_gpu(buf.accum, centers, votesThreshold);
if (centersCount == 0)
{
circles.release();
return;
}
if (minDist > 1)
{
cv::AutoBuffer<ushort2> oldBuf_(centersCount);
cv::AutoBuffer<ushort2> newBuf_(centersCount);
int newCount = 0;
ushort2* oldBuf = oldBuf_;
ushort2* newBuf = newBuf_;
cudaSafeCall( cudaMemcpy(oldBuf, centers, centersCount * sizeof(ushort2), cudaMemcpyDeviceToHost) );
const int cellSize = cvRound(minDist);
const int gridWidth = (src.cols + cellSize - 1) / cellSize;
const int gridHeight = (src.rows + cellSize - 1) / cellSize;
std::vector< std::vector<ushort2> > grid(gridWidth * gridHeight);
const float minDist2 = minDist * minDist;
for (int i = 0; i < centersCount; ++i)
{
ushort2 p = oldBuf[i];
bool good = true;
int xCell = static_cast<int>(p.x / cellSize);
int yCell = static_cast<int>(p.y / cellSize);
int x1 = xCell - 1;
int y1 = yCell - 1;
int x2 = xCell + 1;
int y2 = yCell + 1;
// boundary check
x1 = std::max(0, x1);
y1 = std::max(0, y1);
x2 = std::min(gridWidth - 1, x2);
y2 = std::min(gridHeight - 1, y2);
for (int yy = y1; yy <= y2; ++yy)
{
for (int xx = x1; xx <= x2; ++xx)
{
std::vector<ushort2>& m = grid[yy * gridWidth + xx];
for(size_t j = 0; j < m.size(); ++j)
{
float dx = (float)(p.x - m[j].x);
float dy = (float)(p.y - m[j].y);
if (dx * dx + dy * dy < minDist2)
{
good = false;
goto break_out;
}
}
}
}
break_out:
if(good)
{
grid[yCell * gridWidth + xCell].push_back(p);
newBuf[newCount++] = p;
}
}
cudaSafeCall( cudaMemcpy(centers, newBuf, newCount * sizeof(unsigned int), cudaMemcpyHostToDevice) );
centersCount = newCount;
}
ensureSizeIsEnough(1, maxCircles, CV_32FC3, circles);
const int circlesCount = circlesAccumRadius_gpu(centers, centersCount, srcPoints, pointsCount, circles.ptr<float3>(), maxCircles,
dp, minRadius, maxRadius, votesThreshold, deviceSupports(FEATURE_SET_COMPUTE_20));
if (circlesCount > 0)
circles.cols = circlesCount;
else
circles.release();
}
void cv::gpu::HoughCirclesDownload(const GpuMat& d_circles, cv::OutputArray h_circles_)
{
if (d_circles.empty())
{
h_circles_.release();
return;
}
CV_Assert(d_circles.rows == 1 && d_circles.type() == CV_32FC3);
h_circles_.create(1, d_circles.cols, CV_32FC3);
Mat h_circles = h_circles_.getMat();
d_circles.download(h_circles);
}
//////////////////////////////////////////////////////////
// GeneralizedHough
namespace cv { namespace gpu { namespace device
{
namespace hough
{
template <typename T>
int buildEdgePointList_gpu(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
void buildRTable_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, int* r_sizes,
short2 templCenter, int levels);
void GHT_Ballard_Pos_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepSzi hist,
float dp, int levels);
int GHT_Ballard_Pos_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int maxSize, float dp, int threshold);
void GHT_Ballard_PosScale_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepi hist, int rows, int cols,
float minScale, float scaleStep, int scaleRange,
float dp, int levels);
int GHT_Ballard_PosScale_findPosInHist_gpu(PtrStepi hist, int rows, int cols, int scaleRange, float4* out, int3* votes, int maxSize,
float minScale, float scaleStep, float dp, int threshold);
void GHT_Ballard_PosRotation_calcHist_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
PtrStepSz<short2> r_table, const int* r_sizes,
PtrStepi hist, int rows, int cols,
float minAngle, float angleStep, int angleRange,
float dp, int levels);
int GHT_Ballard_PosRotation_findPosInHist_gpu(PtrStepi hist, int rows, int cols, int angleRange, float4* out, int3* votes, int maxSize,
float minAngle, float angleStep, float dp, int threshold);
void GHT_Guil_Full_setTemplFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void GHT_Guil_Full_setImageFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void GHT_Guil_Full_buildTemplFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist);
void GHT_Guil_Full_buildImageFeatureList_gpu(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist);
void GHT_Guil_Full_calcOHist_gpu(const int* templSizes, const int* imageSizes, int* OHist,
float minAngle, float maxAngle, float angleStep, int angleRange,
int levels, int tMaxSize);
void GHT_Guil_Full_calcSHist_gpu(const int* templSizes, const int* imageSizes, int* SHist,
float angle, float angleEpsilon,
float minScale, float maxScale, float iScaleStep, int scaleRange,
int levels, int tMaxSize);
void GHT_Guil_Full_calcPHist_gpu(const int* templSizes, const int* imageSizes, PtrStepSzi PHist,
float angle, float angleEpsilon, float scale,
float dp,
int levels, int tMaxSize);
int GHT_Guil_Full_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int curSize, int maxSize,
float angle, int angleVotes, float scale, int scaleVotes,
float dp, int threshold);
}
}}}
namespace
{
/////////////////////////////////////
// Common
template <typename T, class A> void releaseVector(std::vector<T, A>& v)
{
std::vector<T, A> empty;
empty.swap(v);
}
class GHT_Pos : public GeneralizedHough_GPU
{
public:
GHT_Pos();
protected:
void setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter);
void detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions);
void releaseImpl();
virtual void processTempl() = 0;
virtual void processImage() = 0;
void buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy);
void filterMinDist();
void convertTo(GpuMat& positions);
int maxSize;
double minDist;
Size templSize;
Point templCenter;
GpuMat templEdges;
GpuMat templDx;
GpuMat templDy;
Size imageSize;
GpuMat imageEdges;
GpuMat imageDx;
GpuMat imageDy;
GpuMat edgePointList;
GpuMat outBuf;
int posCount;
std::vector<float4> oldPosBuf;
std::vector<int3> oldVoteBuf;
std::vector<float4> newPosBuf;
std::vector<int3> newVoteBuf;
std::vector<int> indexies;
};
GHT_Pos::GHT_Pos()
{
maxSize = 10000;
minDist = 1.0;
}
void GHT_Pos::setTemplateImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter_)
{
templSize = edges.size();
templCenter = templCenter_;
ensureSizeIsEnough(templSize, edges.type(), templEdges);
ensureSizeIsEnough(templSize, dx.type(), templDx);
ensureSizeIsEnough(templSize, dy.type(), templDy);
edges.copyTo(templEdges);
dx.copyTo(templDx);
dy.copyTo(templDy);
processTempl();
}
void GHT_Pos::detectImpl(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions)
{
imageSize = edges.size();
ensureSizeIsEnough(imageSize, edges.type(), imageEdges);
ensureSizeIsEnough(imageSize, dx.type(), imageDx);
ensureSizeIsEnough(imageSize, dy.type(), imageDy);
edges.copyTo(imageEdges);
dx.copyTo(imageDx);
dy.copyTo(imageDy);
posCount = 0;
processImage();
if (posCount == 0)
positions.release();
else
{
if (minDist > 1)
filterMinDist();
convertTo(positions);
}
}
void GHT_Pos::releaseImpl()
{
templSize = Size();
templCenter = Point(-1, -1);
templEdges.release();
templDx.release();
templDy.release();
imageSize = Size();
imageEdges.release();
imageDx.release();
imageDy.release();
edgePointList.release();
outBuf.release();
posCount = 0;
releaseVector(oldPosBuf);
releaseVector(oldVoteBuf);
releaseVector(newPosBuf);
releaseVector(newVoteBuf);
releaseVector(indexies);
}
void GHT_Pos::buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy)
{
using namespace cv::gpu::device::hough;
typedef int (*func_t)(PtrStepSzb edges, PtrStepSzb dx, PtrStepSzb dy, unsigned int* coordList, float* thetaList);
static const func_t funcs[] =
{
0,
0,
0,
buildEdgePointList_gpu<short>,
buildEdgePointList_gpu<int>,
buildEdgePointList_gpu<float>,
0
};
CV_Assert(edges.type() == CV_8UC1);
CV_Assert(dx.size() == edges.size());
CV_Assert(dy.type() == dx.type() && dy.size() == edges.size());
const func_t func = funcs[dx.depth()];
CV_Assert(func != 0);
edgePointList.cols = (int) (edgePointList.step / sizeof(int));
ensureSizeIsEnough(2, edges.size().area(), CV_32SC1, edgePointList);
edgePointList.cols = func(edges, dx, dy, edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1));
}
#define votes_cmp_gt(l1, l2) (aux[l1].x > aux[l2].x)
static CV_IMPLEMENT_QSORT_EX( sortIndexies, int, votes_cmp_gt, const int3* )
void GHT_Pos::filterMinDist()
{
oldPosBuf.resize(posCount);
oldVoteBuf.resize(posCount);
cudaSafeCall( cudaMemcpy(&oldPosBuf[0], outBuf.ptr(0), posCount * sizeof(float4), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaMemcpy(&oldVoteBuf[0], outBuf.ptr(1), posCount * sizeof(int3), cudaMemcpyDeviceToHost) );
indexies.resize(posCount);
for (int i = 0; i < posCount; ++i)
indexies[i] = i;
sortIndexies(&indexies[0], posCount, &oldVoteBuf[0]);
newPosBuf.clear();
newVoteBuf.clear();
newPosBuf.reserve(posCount);
newVoteBuf.reserve(posCount);
const int cellSize = cvRound(minDist);
const int gridWidth = (imageSize.width + cellSize - 1) / cellSize;
const int gridHeight = (imageSize.height + cellSize - 1) / cellSize;
std::vector< std::vector<Point2f> > grid(gridWidth * gridHeight);
const double minDist2 = minDist * minDist;
for (int i = 0; i < posCount; ++i)
{
const int ind = indexies[i];
Point2f p(oldPosBuf[ind].x, oldPosBuf[ind].y);
bool good = true;
const int xCell = static_cast<int>(p.x / cellSize);
const int yCell = static_cast<int>(p.y / cellSize);
int x1 = xCell - 1;
int y1 = yCell - 1;
int x2 = xCell + 1;
int y2 = yCell + 1;
// boundary check
x1 = std::max(0, x1);
y1 = std::max(0, y1);
x2 = std::min(gridWidth - 1, x2);
y2 = std::min(gridHeight - 1, y2);
for (int yy = y1; yy <= y2; ++yy)
{
for (int xx = x1; xx <= x2; ++xx)
{
const std::vector<Point2f>& m = grid[yy * gridWidth + xx];
for(size_t j = 0; j < m.size(); ++j)
{
const Point2f d = p - m[j];
if (d.ddot(d) < minDist2)
{
good = false;
goto break_out;
}
}
}
}
break_out:
if(good)
{
grid[yCell * gridWidth + xCell].push_back(p);
newPosBuf.push_back(oldPosBuf[ind]);
newVoteBuf.push_back(oldVoteBuf[ind]);
}
}
posCount = static_cast<int>(newPosBuf.size());
cudaSafeCall( cudaMemcpy(outBuf.ptr(0), &newPosBuf[0], posCount * sizeof(float4), cudaMemcpyHostToDevice) );
cudaSafeCall( cudaMemcpy(outBuf.ptr(1), &newVoteBuf[0], posCount * sizeof(int3), cudaMemcpyHostToDevice) );
}
void GHT_Pos::convertTo(GpuMat& positions)
{
ensureSizeIsEnough(2, posCount, CV_32FC4, positions);
GpuMat(2, posCount, CV_32FC4, outBuf.data, outBuf.step).copyTo(positions);
}
/////////////////////////////////////
// POSITION Ballard
class GHT_Ballard_Pos : public GHT_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_Pos();
protected:
void releaseImpl();
void processTempl();
void processImage();
virtual void calcHist();
virtual void findPosInHist();
int levels;
int votesThreshold;
double dp;
GpuMat r_table;
GpuMat r_sizes;
GpuMat hist;
};
CV_INIT_ALGORITHM(GHT_Ballard_Pos, "GeneralizedHough_GPU.POSITION",
obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
"Maximal size of inner buffers.");
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution."));
GHT_Ballard_Pos::GHT_Ballard_Pos()
{
levels = 360;
votesThreshold = 100;
dp = 1.0;
}
void GHT_Ballard_Pos::releaseImpl()
{
GHT_Pos::releaseImpl();
r_table.release();
r_sizes.release();
hist.release();
}
void GHT_Ballard_Pos::processTempl()
{
using namespace cv::gpu::device::hough;
CV_Assert(levels > 0);
buildEdgePointList(templEdges, templDx, templDy);
ensureSizeIsEnough(levels + 1, maxSize, CV_16SC2, r_table);
ensureSizeIsEnough(1, levels + 1, CV_32SC1, r_sizes);
r_sizes.setTo(Scalar::all(0));
if (edgePointList.cols > 0)
{
buildRTable_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
r_table, r_sizes.ptr<int>(), make_short2(templCenter.x, templCenter.y), levels);
min(r_sizes, maxSize, r_sizes);
}
}
void GHT_Ballard_Pos::processImage()
{
calcHist();
findPosInHist();
}
void GHT_Ballard_Pos::calcHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
CV_Assert(dp > 0.0);
const double idp = 1.0 / dp;
buildEdgePointList(imageEdges, imageDx, imageDy);
ensureSizeIsEnough(cvCeil(imageSize.height * idp) + 2, cvCeil(imageSize.width * idp) + 2, CV_32SC1, hist);
hist.setTo(Scalar::all(0));
if (edgePointList.cols > 0)
{
GHT_Ballard_Pos_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
r_table, r_sizes.ptr<int>(),
hist,
(float)dp, levels);
}
}
void GHT_Ballard_Pos::findPosInHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(votesThreshold > 0);
ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
posCount = GHT_Ballard_Pos_findPosInHist_gpu(hist, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)dp, votesThreshold);
}
/////////////////////////////////////
// POSITION & SCALE
class GHT_Ballard_PosScale : public GHT_Ballard_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_PosScale();
protected:
void calcHist();
void findPosInHist();
double minScale;
double maxScale;
double scaleStep;
};
CV_INIT_ALGORITHM(GHT_Ballard_PosScale, "GeneralizedHough_GPU.POSITION_SCALE",
obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
"Maximal size of inner buffers.");
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
"Minimal scale to detect.");
obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
"Maximal scale to detect.");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Scale step."));
GHT_Ballard_PosScale::GHT_Ballard_PosScale()
{
minScale = 0.5;
maxScale = 2.0;
scaleStep = 0.05;
}
void GHT_Ballard_PosScale::calcHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
CV_Assert(dp > 0.0);
CV_Assert(minScale > 0.0 && minScale < maxScale);
CV_Assert(scaleStep > 0.0);
const double idp = 1.0 / dp;
const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
const int rows = cvCeil(imageSize.height * idp);
const int cols = cvCeil(imageSize.width * idp);
buildEdgePointList(imageEdges, imageDx, imageDy);
ensureSizeIsEnough((scaleRange + 2) * (rows + 2), cols + 2, CV_32SC1, hist);
hist.setTo(Scalar::all(0));
if (edgePointList.cols > 0)
{
GHT_Ballard_PosScale_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
r_table, r_sizes.ptr<int>(),
hist, rows, cols,
(float)minScale, (float)scaleStep, scaleRange, (float)dp, levels);
}
}
void GHT_Ballard_PosScale::findPosInHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(votesThreshold > 0);
const double idp = 1.0 / dp;
const int scaleRange = cvCeil((maxScale - minScale) / scaleStep);
const int rows = cvCeil(imageSize.height * idp);
const int cols = cvCeil(imageSize.width * idp);
ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
posCount = GHT_Ballard_PosScale_findPosInHist_gpu(hist, rows, cols, scaleRange, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)minScale, (float)scaleStep, (float)dp, votesThreshold);
}
/////////////////////////////////////
// POSITION & Rotation
class GHT_Ballard_PosRotation : public GHT_Ballard_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Ballard_PosRotation();
protected:
void calcHist();
void findPosInHist();
double minAngle;
double maxAngle;
double angleStep;
};
CV_INIT_ALGORITHM(GHT_Ballard_PosRotation, "GeneralizedHough_GPU.POSITION_ROTATION",
obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
"Maximal size of inner buffers.");
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"R-Table levels.");
obj.info()->addParam(obj, "votesThreshold", obj.votesThreshold, false, 0, 0,
"The accumulator threshold for the template centers at the detection stage. The smaller it is, the more false positions may be detected.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
"Minimal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
"Maximal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
"Angle step in degrees."));
GHT_Ballard_PosRotation::GHT_Ballard_PosRotation()
{
minAngle = 0.0;
maxAngle = 360.0;
angleStep = 1.0;
}
void GHT_Ballard_PosRotation::calcHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(levels > 0 && r_table.rows == (levels + 1) && r_sizes.cols == (levels + 1));
CV_Assert(dp > 0.0);
CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
CV_Assert(angleStep > 0.0 && angleStep < 360.0);
const double idp = 1.0 / dp;
const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
const int rows = cvCeil(imageSize.height * idp);
const int cols = cvCeil(imageSize.width * idp);
buildEdgePointList(imageEdges, imageDx, imageDy);
ensureSizeIsEnough((angleRange + 2) * (rows + 2), cols + 2, CV_32SC1, hist);
hist.setTo(Scalar::all(0));
if (edgePointList.cols > 0)
{
GHT_Ballard_PosRotation_calcHist_gpu(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
r_table, r_sizes.ptr<int>(),
hist, rows, cols,
(float)minAngle, (float)angleStep, angleRange, (float)dp, levels);
}
}
void GHT_Ballard_PosRotation::findPosInHist()
{
using namespace cv::gpu::device::hough;
CV_Assert(votesThreshold > 0);
const double idp = 1.0 / dp;
const int angleRange = cvCeil((maxAngle - minAngle) / angleStep);
const int rows = cvCeil(imageSize.height * idp);
const int cols = cvCeil(imageSize.width * idp);
ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
posCount = GHT_Ballard_PosRotation_findPosInHist_gpu(hist, rows, cols, angleRange, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1), maxSize, (float)minAngle, (float)angleStep, (float)dp, votesThreshold);
}
/////////////////////////////////////////
// POSITION & SCALE & ROTATION
double toRad(double a)
{
return a * CV_PI / 180.0;
}
double clampAngle(double a)
{
double res = a;
while (res > 360.0)
res -= 360.0;
while (res < 0)
res += 360.0;
return res;
}
bool angleEq(double a, double b, double eps = 1.0)
{
return (fabs(clampAngle(a - b)) <= eps);
}
class GHT_Guil_Full : public GHT_Pos
{
public:
AlgorithmInfo* info() const;
GHT_Guil_Full();
protected:
void releaseImpl();
void processTempl();
void processImage();
struct Feature
{
GpuMat p1_pos;
GpuMat p1_theta;
GpuMat p2_pos;
GpuMat d12;
GpuMat r1;
GpuMat r2;
GpuMat sizes;
int maxSize;
void create(int levels, int maxCapacity, bool isTempl);
void release();
};
typedef void (*set_func_t)(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
typedef void (*build_func_t)(const unsigned int* coordList, const float* thetaList, int pointsCount,
int* sizes, int maxSize,
float xi, float angleEpsilon, int levels,
float2 center, float maxDist);
void buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center = Point2d());
void calcOrientation();
void calcScale(double angle);
void calcPosition(double angle, int angleVotes, double scale, int scaleVotes);
double xi;
int levels;
double angleEpsilon;
double minAngle;
double maxAngle;
double angleStep;
int angleThresh;
double minScale;
double maxScale;
double scaleStep;
int scaleThresh;
double dp;
int posThresh;
Feature templFeatures;
Feature imageFeatures;
std::vector< std::pair<double, int> > angles;
std::vector< std::pair<double, int> > scales;
GpuMat hist;
std::vector<int> h_buf;
};
CV_INIT_ALGORITHM(GHT_Guil_Full, "GeneralizedHough_GPU.POSITION_SCALE_ROTATION",
obj.info()->addParam(obj, "minDist", obj.minDist, false, 0, 0,
"Minimum distance between the centers of the detected objects.");
obj.info()->addParam(obj, "maxSize", obj.maxSize, false, 0, 0,
"Maximal size of inner buffers.");
obj.info()->addParam(obj, "xi", obj.xi, false, 0, 0,
"Angle difference in degrees between two points in feature.");
obj.info()->addParam(obj, "levels", obj.levels, false, 0, 0,
"Feature table levels.");
obj.info()->addParam(obj, "angleEpsilon", obj.angleEpsilon, false, 0, 0,
"Maximal difference between angles that treated as equal.");
obj.info()->addParam(obj, "minAngle", obj.minAngle, false, 0, 0,
"Minimal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "maxAngle", obj.maxAngle, false, 0, 0,
"Maximal rotation angle to detect in degrees.");
obj.info()->addParam(obj, "angleStep", obj.angleStep, false, 0, 0,
"Angle step in degrees.");
obj.info()->addParam(obj, "angleThresh", obj.angleThresh, false, 0, 0,
"Angle threshold.");
obj.info()->addParam(obj, "minScale", obj.minScale, false, 0, 0,
"Minimal scale to detect.");
obj.info()->addParam(obj, "maxScale", obj.maxScale, false, 0, 0,
"Maximal scale to detect.");
obj.info()->addParam(obj, "scaleStep", obj.scaleStep, false, 0, 0,
"Scale step.");
obj.info()->addParam(obj, "scaleThresh", obj.scaleThresh, false, 0, 0,
"Scale threshold.");
obj.info()->addParam(obj, "dp", obj.dp, false, 0, 0,
"Inverse ratio of the accumulator resolution to the image resolution.");
obj.info()->addParam(obj, "posThresh", obj.posThresh, false, 0, 0,
"Position threshold."));
GHT_Guil_Full::GHT_Guil_Full()
{
maxSize = 1000;
xi = 90.0;
levels = 360;
angleEpsilon = 1.0;
minAngle = 0.0;
maxAngle = 360.0;
angleStep = 1.0;
angleThresh = 15000;
minScale = 0.5;
maxScale = 2.0;
scaleStep = 0.05;
scaleThresh = 1000;
dp = 1.0;
posThresh = 100;
}
void GHT_Guil_Full::releaseImpl()
{
GHT_Pos::releaseImpl();
templFeatures.release();
imageFeatures.release();
releaseVector(angles);
releaseVector(scales);
hist.release();
releaseVector(h_buf);
}
void GHT_Guil_Full::processTempl()
{
using namespace cv::gpu::device::hough;
buildFeatureList(templEdges, templDx, templDy, templFeatures,
GHT_Guil_Full_setTemplFeatures, GHT_Guil_Full_buildTemplFeatureList_gpu,
true, templCenter);
h_buf.resize(templFeatures.sizes.cols);
cudaSafeCall( cudaMemcpy(&h_buf[0], templFeatures.sizes.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
templFeatures.maxSize = *max_element(h_buf.begin(), h_buf.end());
}
void GHT_Guil_Full::processImage()
{
using namespace cv::gpu::device::hough;
CV_Assert(levels > 0);
CV_Assert(templFeatures.sizes.cols == levels + 1);
CV_Assert(minAngle >= 0.0 && minAngle < maxAngle && maxAngle <= 360.0);
CV_Assert(angleStep > 0.0 && angleStep < 360.0);
CV_Assert(angleThresh > 0);
CV_Assert(minScale > 0.0 && minScale < maxScale);
CV_Assert(scaleStep > 0.0);
CV_Assert(scaleThresh > 0);
CV_Assert(dp > 0.0);
CV_Assert(posThresh > 0);
const double iAngleStep = 1.0 / angleStep;
const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
const double iScaleStep = 1.0 / scaleStep;
const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
const double idp = 1.0 / dp;
const int histRows = cvCeil(imageSize.height * idp);
const int histCols = cvCeil(imageSize.width * idp);
ensureSizeIsEnough(histRows + 2, std::max(angleRange + 1, std::max(scaleRange + 1, histCols + 2)), CV_32SC1, hist);
h_buf.resize(std::max(angleRange + 1, scaleRange + 1));
ensureSizeIsEnough(2, maxSize, CV_32FC4, outBuf);
buildFeatureList(imageEdges, imageDx, imageDy, imageFeatures,
GHT_Guil_Full_setImageFeatures, GHT_Guil_Full_buildImageFeatureList_gpu,
false);
calcOrientation();
for (size_t i = 0; i < angles.size(); ++i)
{
const double angle = angles[i].first;
const int angleVotes = angles[i].second;
calcScale(angle);
for (size_t j = 0; j < scales.size(); ++j)
{
const double scale = scales[j].first;
const int scaleVotes = scales[j].second;
calcPosition(angle, angleVotes, scale, scaleVotes);
}
}
}
void GHT_Guil_Full::Feature::create(int levels, int maxCapacity, bool isTempl)
{
if (!isTempl)
{
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, p1_pos);
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, p2_pos);
}
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC1, p1_theta);
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC1, d12);
if (isTempl)
{
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, r1);
ensureSizeIsEnough(levels + 1, maxCapacity, CV_32FC2, r2);
}
ensureSizeIsEnough(1, levels + 1, CV_32SC1, sizes);
sizes.setTo(Scalar::all(0));
maxSize = 0;
}
void GHT_Guil_Full::Feature::release()
{
p1_pos.release();
p1_theta.release();
p2_pos.release();
d12.release();
r1.release();
r2.release();
sizes.release();
maxSize = 0;
}
void GHT_Guil_Full::buildFeatureList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Feature& features,
set_func_t set_func, build_func_t build_func, bool isTempl, Point2d center)
{
CV_Assert(levels > 0);
const double maxDist = sqrt((double) templSize.width * templSize.width + templSize.height * templSize.height) * maxScale;
features.create(levels, maxSize, isTempl);
set_func(features.p1_pos, features.p1_theta, features.p2_pos, features.d12, features.r1, features.r2);
buildEdgePointList(edges, dx, dy);
if (edgePointList.cols > 0)
{
build_func(edgePointList.ptr<unsigned int>(0), edgePointList.ptr<float>(1), edgePointList.cols,
features.sizes.ptr<int>(), maxSize, (float)xi, (float)angleEpsilon, levels, make_float2((float)center.x, (float)center.y), (float)maxDist);
}
}
void GHT_Guil_Full::calcOrientation()
{
using namespace cv::gpu::device::hough;
const double iAngleStep = 1.0 / angleStep;
const int angleRange = cvCeil((maxAngle - minAngle) * iAngleStep);
hist.setTo(Scalar::all(0));
GHT_Guil_Full_calcOHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0),
hist.ptr<int>(), (float)minAngle, (float)maxAngle, (float)angleStep, angleRange, levels, templFeatures.maxSize);
cudaSafeCall( cudaMemcpy(&h_buf[0], hist.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
angles.clear();
for (int n = 0; n < angleRange; ++n)
{
if (h_buf[n] >= angleThresh)
{
const double angle = minAngle + n * angleStep;
angles.push_back(std::make_pair(angle, h_buf[n]));
}
}
}
void GHT_Guil_Full::calcScale(double angle)
{
using namespace cv::gpu::device::hough;
const double iScaleStep = 1.0 / scaleStep;
const int scaleRange = cvCeil((maxScale - minScale) * iScaleStep);
hist.setTo(Scalar::all(0));
GHT_Guil_Full_calcSHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0),
hist.ptr<int>(), (float)angle, (float)angleEpsilon, (float)minScale, (float)maxScale, (float)iScaleStep, scaleRange, levels, templFeatures.maxSize);
cudaSafeCall( cudaMemcpy(&h_buf[0], hist.data, h_buf.size() * sizeof(int), cudaMemcpyDeviceToHost) );
scales.clear();
for (int s = 0; s < scaleRange; ++s)
{
if (h_buf[s] >= scaleThresh)
{
const double scale = minScale + s * scaleStep;
scales.push_back(std::make_pair(scale, h_buf[s]));
}
}
}
void GHT_Guil_Full::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
{
using namespace cv::gpu::device::hough;
hist.setTo(Scalar::all(0));
GHT_Guil_Full_calcPHist_gpu(templFeatures.sizes.ptr<int>(), imageFeatures.sizes.ptr<int>(0),
hist,(float) (float)angle, (float)angleEpsilon, (float)scale, (float)dp, levels, templFeatures.maxSize);
posCount = GHT_Guil_Full_findPosInHist_gpu(hist, outBuf.ptr<float4>(0), outBuf.ptr<int3>(1),
posCount, maxSize, (float)angle, angleVotes, (float)scale, scaleVotes, (float)dp, posThresh);
}
}
Ptr<GeneralizedHough_GPU> cv::gpu::GeneralizedHough_GPU::create(int method)
{
switch (method)
{
case GHT_POSITION:
CV_Assert( !GHT_Ballard_Pos_info_auto.name().empty() );
return new GHT_Ballard_Pos();
case (GHT_POSITION | GHT_SCALE):
CV_Assert( !GHT_Ballard_PosScale_info_auto.name().empty() );
return new GHT_Ballard_PosScale();
case (GHT_POSITION | GHT_ROTATION):
CV_Assert( !GHT_Ballard_PosRotation_info_auto.name().empty() );
return new GHT_Ballard_PosRotation();
case (GHT_POSITION | GHT_SCALE | GHT_ROTATION):
CV_Assert( !GHT_Guil_Full_info_auto.name().empty() );
return new GHT_Guil_Full();
}
CV_Error(CV_StsBadArg, "Unsupported method");
return Ptr<GeneralizedHough_GPU>();
}
cv::gpu::GeneralizedHough_GPU::~GeneralizedHough_GPU()
{
}
void cv::gpu::GeneralizedHough_GPU::setTemplate(const GpuMat& templ, int cannyThreshold, Point templCenter)
{
CV_Assert(templ.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
ensureSizeIsEnough(templ.size(), CV_8UC1, edges_);
Canny(templ, cannyBuf_, edges_, cannyThreshold / 2, cannyThreshold);
if (templCenter == Point(-1, -1))
templCenter = Point(templ.cols / 2, templ.rows / 2);
setTemplateImpl(edges_, cannyBuf_.dx, cannyBuf_.dy, templCenter);
}
void cv::gpu::GeneralizedHough_GPU::setTemplate(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, Point templCenter)
{
if (templCenter == Point(-1, -1))
templCenter = Point(edges.cols / 2, edges.rows / 2);
setTemplateImpl(edges, dx, dy, templCenter);
}
void cv::gpu::GeneralizedHough_GPU::detect(const GpuMat& image, GpuMat& positions, int cannyThreshold)
{
CV_Assert(image.type() == CV_8UC1);
CV_Assert(cannyThreshold > 0);
ensureSizeIsEnough(image.size(), CV_8UC1, edges_);
Canny(image, cannyBuf_, edges_, cannyThreshold / 2, cannyThreshold);
detectImpl(edges_, cannyBuf_.dx, cannyBuf_.dy, positions);
}
void cv::gpu::GeneralizedHough_GPU::detect(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy, GpuMat& positions)
{
detectImpl(edges, dx, dy, positions);
}
void cv::gpu::GeneralizedHough_GPU::download(const GpuMat& d_positions, OutputArray h_positions_, OutputArray h_votes_)
{
if (d_positions.empty())
{
h_positions_.release();
if (h_votes_.needed())
h_votes_.release();
return;
}
CV_Assert(d_positions.rows == 2 && d_positions.type() == CV_32FC4);
h_positions_.create(1, d_positions.cols, CV_32FC4);
Mat h_positions = h_positions_.getMat();
d_positions.row(0).download(h_positions);
if (h_votes_.needed())
{
h_votes_.create(1, d_positions.cols, CV_32SC3);
Mat h_votes = h_votes_.getMat();
GpuMat d_votes(1, d_positions.cols, CV_32SC3, const_cast<int3*>(d_positions.ptr<int3>(1)));
d_votes.download(h_votes);
}
}
void cv::gpu::GeneralizedHough_GPU::release()
{
edges_.release();
cannyBuf_.release();
releaseImpl();
}
#endif /* !defined (HAVE_CUDA) */