This source file includes following definitions.
- createHoughCirclesDetector
- setDp
- getDp
- setMinDist
- getMinDist
- setCannyThreshold
- getCannyThreshold
- setVotesThreshold
- getVotesThreshold
- setMinRadius
- getMinRadius
- setMaxRadius
- getMaxRadius
- setMaxCircles
- getMaxCircles
- write
- read
- centersCompare
- maxCircles_
- detect
- createHoughCirclesDetector
#include "precomp.hpp"
using namespace cv;
using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER) || !defined(HAVE_OPENCV_CUDAFILTERS)
Ptr<cuda::HoughCirclesDetector> cv::cuda::createHoughCirclesDetector(float, float, int, int, int, int, int) { throw_no_cuda(); return Ptr<HoughCirclesDetector>(); }
#else
namespace cv { namespace cuda { namespace device
{
namespace hough
{
int buildPointList_gpu(PtrStepSzb src, unsigned int* list);
}
namespace hough_circles
{
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);
}
}}}
namespace
{
class HoughCirclesDetectorImpl : public HoughCirclesDetector
{
public:
HoughCirclesDetectorImpl(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles);
void detect(InputArray src, OutputArray circles, Stream& stream);
void setDp(float dp) { dp_ = dp; }
float getDp() const { return dp_; }
void setMinDist(float minDist) { minDist_ = minDist; }
float getMinDist() const { return minDist_; }
void setCannyThreshold(int cannyThreshold) { cannyThreshold_ = cannyThreshold; }
int getCannyThreshold() const { return cannyThreshold_; }
void setVotesThreshold(int votesThreshold) { votesThreshold_ = votesThreshold; }
int getVotesThreshold() const { return votesThreshold_; }
void setMinRadius(int minRadius) { minRadius_ = minRadius; }
int getMinRadius() const { return minRadius_; }
void setMaxRadius(int maxRadius) { maxRadius_ = maxRadius; }
int getMaxRadius() const { return maxRadius_; }
void setMaxCircles(int maxCircles) { maxCircles_ = maxCircles; }
int getMaxCircles() const { return maxCircles_; }
void write(FileStorage& fs) const
{
fs << "name" << "HoughCirclesDetector_CUDA"
<< "dp" << dp_
<< "minDist" << minDist_
<< "cannyThreshold" << cannyThreshold_
<< "votesThreshold" << votesThreshold_
<< "minRadius" << minRadius_
<< "maxRadius" << maxRadius_
<< "maxCircles" << maxCircles_;
}
void read(const FileNode& fn)
{
CV_Assert( String(fn["name"]) == "HoughCirclesDetector_CUDA" );
dp_ = (float)fn["dp"];
minDist_ = (float)fn["minDist"];
cannyThreshold_ = (int)fn["cannyThreshold"];
votesThreshold_ = (int)fn["votesThreshold"];
minRadius_ = (int)fn["minRadius"];
maxRadius_ = (int)fn["maxRadius"];
maxCircles_ = (int)fn["maxCircles"];
}
private:
float dp_;
float minDist_;
int cannyThreshold_;
int votesThreshold_;
int minRadius_;
int maxRadius_;
int maxCircles_;
GpuMat dx_, dy_;
GpuMat edges_;
GpuMat accum_;
Mat tt;
GpuMat list_;
GpuMat result_;
Ptr<cuda::Filter> filterDx_;
Ptr<cuda::Filter> filterDy_;
Ptr<cuda::CannyEdgeDetector> canny_;
};
bool centersCompare(Vec3f a, Vec3f b) {return (a[2] > b[2]);}
HoughCirclesDetectorImpl::HoughCirclesDetectorImpl(float dp, float minDist, int cannyThreshold, int votesThreshold,
int minRadius, int maxRadius, int maxCircles) :
dp_(dp), minDist_(minDist), cannyThreshold_(cannyThreshold), votesThreshold_(votesThreshold),
minRadius_(minRadius), maxRadius_(maxRadius), maxCircles_(maxCircles)
{
canny_ = cuda::createCannyEdgeDetector(std::max(cannyThreshold_ / 2, 1), cannyThreshold_);
filterDx_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 1, 0);
filterDy_ = cuda::createSobelFilter(CV_8UC1, CV_32S, 0, 1);
}
void HoughCirclesDetectorImpl::detect(InputArray _src, OutputArray circles, Stream& stream)
{
(void) stream;
using namespace cv::cuda::device::hough;
using namespace cv::cuda::device::hough_circles;
GpuMat src = _src.getGpuMat();
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( 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_;
filterDx_->apply(src, dx_);
filterDy_->apply(src, dy_);
canny_->setLowThreshold(std::max(cannyThreshold_ / 2, 1));
canny_->setHighThreshold(cannyThreshold_);
canny_->detect(dx_, dy_, edges_);
ensureSizeIsEnough(2, src.size().area(), CV_32SC1, list_);
unsigned int* srcPoints = list_.ptr<unsigned int>(0);
unsigned int* centers = list_.ptr<unsigned int>(1);
const int pointsCount = buildPointList_gpu(edges_, srcPoints);
if (pointsCount == 0)
{
circles.release();
return;
}
ensureSizeIsEnough(cvCeil(src.rows * idp) + 2, cvCeil(src.cols * idp) + 2, CV_32SC1, accum_);
accum_.setTo(Scalar::all(0));
circlesAccumCenters_gpu(srcPoints, pointsCount, dx_, dy_, accum_, minRadius_, maxRadius_, idp);
accum_.download(tt);
int centersCount = buildCentersList_gpu(accum_, centers, votesThreshold_);
if (centersCount == 0)
{
circles.release();
return;
}
if (minDist_ > 1)
{
AutoBuffer<ushort2> oldBuf_(centersCount);
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_;
std::vector<Vec3f> sortBuf;
for(int i=0; i<centersCount; i++){
Vec3f temp;
temp[0] = oldBuf[i].x;
temp[1] = oldBuf[i].y;
temp[2] = tt.at<int>(temp[1]+1, temp[0]+1);
sortBuf.push_back(temp);
}
std::sort(sortBuf.begin(), sortBuf.end(), centersCompare);
for (int i = 0; i < centersCount; ++i)
{
ushort2 p;
p.x = sortBuf[i][0];
p.y = sortBuf[i][1];
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;
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, result_);
int circlesCount = circlesAccumRadius_gpu(centers, centersCount, srcPoints, pointsCount, result_.ptr<float3>(), maxCircles_,
dp_, minRadius_, maxRadius_, votesThreshold_, deviceSupports(FEATURE_SET_COMPUTE_20));
if (circlesCount == 0)
{
circles.release();
return;
}
result_.cols = circlesCount;
result_.copyTo(circles);
}
}
Ptr<HoughCirclesDetector> cv::cuda::createHoughCirclesDetector(float dp, float minDist, int cannyThreshold, int votesThreshold, int minRadius, int maxRadius, int maxCircles)
{
return makePtr<HoughCirclesDetectorImpl>(dp, minDist, cannyThreshold, votesThreshold, minRadius, maxRadius, maxCircles);
}
#endif