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opencv/modules/cudaimgproc/src/generalized_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::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_CUDAARITHM)
Ptr<GeneralizedHoughBallard> cv::cuda::createGeneralizedHoughBallard() { throw_no_cuda(); return Ptr<GeneralizedHoughBallard>(); }
Ptr<GeneralizedHoughGuil> cv::cuda::createGeneralizedHoughGuil() { throw_no_cuda(); return Ptr<GeneralizedHoughGuil>(); }
#else /* !defined (HAVE_CUDA) */
namespace cv { namespace cuda { namespace device
{
namespace ght
{
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 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 Ballard_Pos_findPosInHist_gpu(PtrStepSzi hist, float4* out, int3* votes, int maxSize, float dp, int threshold);
void Guil_Full_setTemplFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void Guil_Full_setImageFeatures(PtrStepb p1_pos, PtrStepb p1_theta, PtrStepb p2_pos, PtrStepb d12, PtrStepb r1, PtrStepb r2);
void 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 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 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 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 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 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);
}
}}}
// common
namespace
{
class GeneralizedHoughBase
{
protected:
GeneralizedHoughBase();
virtual ~GeneralizedHoughBase() {}
void setTemplateImpl(InputArray templ, Point templCenter);
void setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter);
void detectImpl(InputArray image, OutputArray positions, OutputArray votes);
void detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes);
void buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy);
virtual void processTempl() = 0;
virtual void processImage() = 0;
int cannyLowThresh_;
int cannyHighThresh_;
double minDist_;
double dp_;
int maxBufferSize_;
Size templSize_;
Point templCenter_;
GpuMat templEdges_;
GpuMat templDx_;
GpuMat templDy_;
Size imageSize_;
GpuMat imageEdges_;
GpuMat imageDx_;
GpuMat imageDy_;
GpuMat edgePointList_;
GpuMat outBuf_;
int posCount_;
private:
#ifdef HAVE_OPENCV_CUDAFILTERS
void calcEdges(InputArray src, GpuMat& edges, GpuMat& dx, GpuMat& dy);
#endif
void filterMinDist();
void convertTo(OutputArray positions, OutputArray votes);
#ifdef HAVE_OPENCV_CUDAFILTERS
Ptr<cuda::CannyEdgeDetector> canny_;
Ptr<cuda::Filter> filterDx_;
Ptr<cuda::Filter> filterDy_;
#endif
std::vector<float4> oldPosBuf_;
std::vector<int3> oldVoteBuf_;
std::vector<float4> newPosBuf_;
std::vector<int3> newVoteBuf_;
std::vector<int> indexies_;
};
GeneralizedHoughBase::GeneralizedHoughBase()
{
cannyLowThresh_ = 50;
cannyHighThresh_ = 100;
minDist_ = 1.0;
dp_ = 1.0;
maxBufferSize_ = 10000;
#ifdef HAVE_OPENCV_CUDAFILTERS
canny_ = cuda::createCannyEdgeDetector(cannyLowThresh_, cannyHighThresh_);
filterDx_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 1, 0);
filterDy_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 0, 1);
#endif
}
#ifdef HAVE_OPENCV_CUDAFILTERS
void GeneralizedHoughBase::calcEdges(InputArray _src, GpuMat& edges, GpuMat& dx, GpuMat& dy)
{
GpuMat src = _src.getGpuMat();
CV_Assert( src.type() == CV_8UC1 );
CV_Assert( cannyLowThresh_ > 0 && cannyLowThresh_ < cannyHighThresh_ );
ensureSizeIsEnough(src.size(), CV_32SC1, dx);
ensureSizeIsEnough(src.size(), CV_32SC1, dy);
filterDx_->apply(src, dx);
filterDy_->apply(src, dy);
ensureSizeIsEnough(src.size(), CV_8UC1, edges);
canny_->setLowThreshold(cannyLowThresh_);
canny_->setHighThreshold(cannyHighThresh_);
canny_->detect(dx, dy, edges);
}
#endif
void GeneralizedHoughBase::setTemplateImpl(InputArray templ, Point templCenter)
{
#ifndef HAVE_OPENCV_CUDAFILTERS
CV_UNUSED(templ);
CV_UNUSED(templCenter);
throw_no_cuda();
#else
calcEdges(templ, templEdges_, templDx_, templDy_);
if (templCenter == Point(-1, -1))
templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
templSize_ = templEdges_.size();
templCenter_ = templCenter;
processTempl();
#endif
}
void GeneralizedHoughBase::setTemplateImpl(InputArray edges, InputArray dx, InputArray dy, Point templCenter)
{
edges.getGpuMat().copyTo(templEdges_);
dx.getGpuMat().copyTo(templDx_);
dy.getGpuMat().copyTo(templDy_);
CV_Assert( templEdges_.type() == CV_8UC1 );
CV_Assert( templDx_.type() == CV_32FC1 && templDx_.size() == templEdges_.size() );
CV_Assert( templDy_.type() == templDx_.type() && templDy_.size() == templEdges_.size() );
if (templCenter == Point(-1, -1))
templCenter = Point(templEdges_.cols / 2, templEdges_.rows / 2);
templSize_ = templEdges_.size();
templCenter_ = templCenter;
processTempl();
}
void GeneralizedHoughBase::detectImpl(InputArray image, OutputArray positions, OutputArray votes)
{
#ifndef HAVE_OPENCV_CUDAFILTERS
CV_UNUSED(image);
CV_UNUSED(positions);
CV_UNUSED(votes);
throw_no_cuda();
#else
calcEdges(image, imageEdges_, imageDx_, imageDy_);
imageSize_ = imageEdges_.size();
posCount_ = 0;
processImage();
if (posCount_ == 0)
{
positions.release();
if (votes.needed())
votes.release();
}
else
{
if (minDist_ > 1)
filterMinDist();
convertTo(positions, votes);
}
#endif
}
void GeneralizedHoughBase::detectImpl(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes)
{
edges.getGpuMat().copyTo(imageEdges_);
dx.getGpuMat().copyTo(imageDx_);
dy.getGpuMat().copyTo(imageDy_);
CV_Assert( imageEdges_.type() == CV_8UC1 );
CV_Assert( imageDx_.type() == CV_32FC1 && imageDx_.size() == imageEdges_.size() );
CV_Assert( imageDy_.type() == imageDx_.type() && imageDy_.size() == imageEdges_.size() );
imageSize_ = imageEdges_.size();
posCount_ = 0;
processImage();
if (posCount_ == 0)
{
positions.release();
if (votes.needed())
votes.release();
}
else
{
if (minDist_ > 1)
filterMinDist();
convertTo(positions, votes);
}
}
void GeneralizedHoughBase::buildEdgePointList(const GpuMat& edges, const GpuMat& dx, const GpuMat& dy)
{
using namespace cv::cuda::device::ght;
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));
}
struct IndexCmp
{
const int3* aux;
explicit IndexCmp(const int3* _aux) : aux(_aux) {}
bool operator ()(int l1, int l2) const
{
return aux[l1].x > aux[l2].x;
}
};
void GeneralizedHoughBase::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;
std::sort(indexies_.begin(), indexies_.end(), IndexCmp(&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 GeneralizedHoughBase::convertTo(OutputArray positions, OutputArray votes)
{
ensureSizeIsEnough(1, posCount_, CV_32FC4, positions);
GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(0), outBuf_.step).copyTo(positions);
if (votes.needed())
{
ensureSizeIsEnough(1, posCount_, CV_32FC3, votes);
GpuMat(1, posCount_, CV_32FC4, outBuf_.ptr(1), outBuf_.step).copyTo(votes);
}
}
}
// GeneralizedHoughBallard
namespace
{
class GeneralizedHoughBallardImpl : public GeneralizedHoughBallard, private GeneralizedHoughBase
{
public:
GeneralizedHoughBallardImpl();
void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
int getCannyLowThresh() const { return cannyLowThresh_; }
void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
int getCannyHighThresh() const { return cannyHighThresh_; }
void setMinDist(double minDist) { minDist_ = minDist; }
double getMinDist() const { return minDist_; }
void setDp(double dp) { dp_ = dp; }
double getDp() const { return dp_; }
void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
int getMaxBufferSize() const { return maxBufferSize_; }
void setLevels(int levels) { levels_ = levels; }
int getLevels() const { return levels_; }
void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; }
int getVotesThreshold() const { return votesThreshold_; }
private:
void processTempl();
void processImage();
void calcHist();
void findPosInHist();
int levels_;
int votesThreshold_;
GpuMat r_table_;
GpuMat r_sizes_;
GpuMat hist_;
};
GeneralizedHoughBallardImpl::GeneralizedHoughBallardImpl()
{
levels_ = 360;
votesThreshold_ = 100;
}
void GeneralizedHoughBallardImpl::processTempl()
{
using namespace cv::cuda::device::ght;
CV_Assert( levels_ > 0 );
buildEdgePointList(templEdges_, templDx_, templDy_);
ensureSizeIsEnough(levels_ + 1, maxBufferSize_, 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_);
cuda::min(r_sizes_, maxBufferSize_, r_sizes_);
}
}
void GeneralizedHoughBallardImpl::processImage()
{
calcHist();
findPosInHist();
}
void GeneralizedHoughBallardImpl::calcHist()
{
using namespace cv::cuda::device::ght;
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)
{
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 GeneralizedHoughBallardImpl::findPosInHist()
{
using namespace cv::cuda::device::ght;
CV_Assert( votesThreshold_ > 0 );
ensureSizeIsEnough(2, maxBufferSize_, CV_32FC4, outBuf_);
posCount_ = Ballard_Pos_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1), maxBufferSize_, (float)dp_, votesThreshold_);
}
}
Ptr<GeneralizedHoughBallard> cv::cuda::createGeneralizedHoughBallard()
{
return makePtr<GeneralizedHoughBallardImpl>();
}
// GeneralizedHoughGuil
namespace
{
class GeneralizedHoughGuilImpl : public GeneralizedHoughGuil, private GeneralizedHoughBase
{
public:
GeneralizedHoughGuilImpl();
void setTemplate(InputArray templ, Point templCenter) { setTemplateImpl(templ, templCenter); }
void setTemplate(InputArray edges, InputArray dx, InputArray dy, Point templCenter) { setTemplateImpl(edges, dx, dy, templCenter); }
void detect(InputArray image, OutputArray positions, OutputArray votes) { detectImpl(image, positions, votes); }
void detect(InputArray edges, InputArray dx, InputArray dy, OutputArray positions, OutputArray votes) { detectImpl(edges, dx, dy, positions, votes); }
void setCannyLowThresh(int cannyLowThresh) { cannyLowThresh_ = cannyLowThresh; }
int getCannyLowThresh() const { return cannyLowThresh_; }
void setCannyHighThresh(int cannyHighThresh) { cannyHighThresh_ = cannyHighThresh; }
int getCannyHighThresh() const { return cannyHighThresh_; }
void setMinDist(double minDist) { minDist_ = minDist; }
double getMinDist() const { return minDist_; }
void setDp(double dp) { dp_ = dp; }
double getDp() const { return dp_; }
void setMaxBufferSize(int maxBufferSize) { maxBufferSize_ = maxBufferSize; }
int getMaxBufferSize() const { return maxBufferSize_; }
void setXi(double xi) { xi_ = xi; }
double getXi() const { return xi_; }
void setLevels(int levels) { levels_ = levels; }
int getLevels() const { return levels_; }
void setAngleEpsilon(double angleEpsilon) { angleEpsilon_ = angleEpsilon; }
double getAngleEpsilon() const { return angleEpsilon_; }
void setMinAngle(double minAngle) { minAngle_ = minAngle; }
double getMinAngle() const { return minAngle_; }
void setMaxAngle(double maxAngle) { maxAngle_ = maxAngle; }
double getMaxAngle() const { return maxAngle_; }
void setAngleStep(double angleStep) { angleStep_ = angleStep; }
double getAngleStep() const { return angleStep_; }
void setAngleThresh(int angleThresh) { angleThresh_ = angleThresh; }
int getAngleThresh() const { return angleThresh_; }
void setMinScale(double minScale) { minScale_ = minScale; }
double getMinScale() const { return minScale_; }
void setMaxScale(double maxScale) { maxScale_ = maxScale; }
double getMaxScale() const { return maxScale_; }
void setScaleStep(double scaleStep) { scaleStep_ = scaleStep; }
double getScaleStep() const { return scaleStep_; }
void setScaleThresh(int scaleThresh) { scaleThresh_ = scaleThresh; }
int getScaleThresh() const { return scaleThresh_; }
void setPosThresh(int posThresh) { posThresh_ = posThresh; }
int getPosThresh() const { return posThresh_; }
private:
void processTempl();
void processImage();
double xi_;
int levels_;
double angleEpsilon_;
double minAngle_;
double maxAngle_;
double angleStep_;
int angleThresh_;
double minScale_;
double maxScale_;
double scaleStep_;
int scaleThresh_;
int posThresh_;
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);
};
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);
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_;
};
GeneralizedHoughGuilImpl::GeneralizedHoughGuilImpl()
{
maxBufferSize_ = 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;
posThresh_ = 100;
}
void GeneralizedHoughGuilImpl::processTempl()
{
using namespace cv::cuda::device::ght;
buildFeatureList(templEdges_, templDx_, templDy_, templFeatures_,
Guil_Full_setTemplFeatures, 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 = *std::max_element(h_buf_.begin(), h_buf_.end());
}
void GeneralizedHoughGuilImpl::processImage()
{
using namespace cv::cuda::device::ght;
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, maxBufferSize_, CV_32FC4, outBuf_);
buildFeatureList(imageEdges_, imageDx_, imageDy_, imageFeatures_,
Guil_Full_setImageFeatures, 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 GeneralizedHoughGuilImpl::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 GeneralizedHoughGuilImpl::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_, maxBufferSize_, 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>(), maxBufferSize_, (float)xi_, (float)angleEpsilon_, levels_, make_float2((float)center.x, (float)center.y), (float)maxDist);
}
}
void GeneralizedHoughGuilImpl::calcOrientation()
{
using namespace cv::cuda::device::ght;
const double iAngleStep = 1.0 / angleStep_;
const int angleRange = cvCeil((maxAngle_ - minAngle_) * iAngleStep);
hist_.setTo(Scalar::all(0));
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 GeneralizedHoughGuilImpl::calcScale(double angle)
{
using namespace cv::cuda::device::ght;
const double iScaleStep = 1.0 / scaleStep_;
const int scaleRange = cvCeil((maxScale_ - minScale_) * iScaleStep);
hist_.setTo(Scalar::all(0));
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 GeneralizedHoughGuilImpl::calcPosition(double angle, int angleVotes, double scale, int scaleVotes)
{
using namespace cv::cuda::device::ght;
hist_.setTo(Scalar::all(0));
Guil_Full_calcPHist_gpu(templFeatures_.sizes.ptr<int>(), imageFeatures_.sizes.ptr<int>(0), hist_,
(float)angle, (float)angleEpsilon_, (float)scale, (float)dp_, levels_, templFeatures_.maxSize);
posCount_ = Guil_Full_findPosInHist_gpu(hist_, outBuf_.ptr<float4>(0), outBuf_.ptr<int3>(1),
posCount_, maxBufferSize_, (float)angle, angleVotes,
(float)scale, scaleVotes, (float)dp_, posThresh_);
}
}
Ptr<GeneralizedHoughGuil> cv::cuda::createGeneralizedHoughGuil()
{
return makePtr<GeneralizedHoughGuilImpl>();
}
#endif /* !defined (HAVE_CUDA) */