//! find keypoints and compute it's response if _bNonMaxSupression is true
//! return count of detected keypoints
//return the # of keypoints, stored in _uCount;
//store Key point locations into _cvgmKeyPointLocation;
//store the corner strength into _cvgmScore;
int CFast::calcKeyPointsLocation(const cv::gpu::GpuMat& cvgmImg_, const cv::gpu::GpuMat& cvgmMask_)
{
//using namespace cv::gpu::device::fast;
CV_Assert(cvgmImg_.type() == CV_8UC1);
CV_Assert(cvgmMask_.empty() || (cvgmMask_.type() == CV_8UC1 && cvgmMask_.size() == cvgmImg_.size()));
if (!cv::gpu::TargetArchs::builtWith(cv::gpu::GLOBAL_ATOMICS) || !cv::gpu::DeviceInfo().supports(cv::gpu::GLOBAL_ATOMICS))
CV_Error(CV_StsNotImplemented, "The device doesn't support global atomics");
unsigned int uMaxKeypoints = static_cast<unsigned int>(_dKeyPointsRatio * cvgmImg_.size().area());
ensureSizeIsEnough(1, uMaxKeypoints, CV_16SC2, _cvgmKeyPointLocation);
if (_bNonMaxSupression)
{
ensureSizeIsEnough(cvgmImg_.size(), CV_32SC1, _cvgmScore);
_cvgmScore.setTo(cv::Scalar::all(0));
}
_uCount = btl::device::fast::cudaCalcKeypoints(cvgmImg_, cvgmMask_, uMaxKeypoints, _nThreshold, _cvgmKeyPointLocation.ptr<short2>(), _bNonMaxSupression ? &_cvgmScore : NULL);
_uCount = std::min(_uCount, uMaxKeypoints);
return _uCount;
}
void LukasKanadeOpticalFlow::drawMotionField_GPU(cv::gpu::GpuMat &imgU, cv::gpu::GpuMat &imgV, cv::gpu::GpuMat &imgMotion,
int xSpace, int ySpace, float minCutoff, float maxCutoff, float multiplier, CvScalar color)
{
cv::Mat uMat( imgU );
cv::Mat vMat( imgV );
cv::Mat drawMat(cv::Size( imgU.size().width, imgU.size().height), CV_8UC3 );
int x = 0, y = 0;
float *ptri;
float deltaX = 0.0, deltaY = 0.0, angle = 0.0, hyp = 0.0;
cv::Point p0, p1;
for( y = ySpace; y < uMat.rows; y += ySpace )
{
for(x = xSpace; x < uMat.cols; x += xSpace )
{
p0.x = x;
p0.y = y;
ptri = uMat.ptr<float>(y);
deltaX = ptri[x];
ptri = vMat.ptr<float>(y);
deltaY = ptri[x];
angle = atan2(deltaY, deltaX);
hyp = sqrt(deltaX*deltaX + deltaY*deltaY);
if( hyp > minCutoff && hyp < maxCutoff )
{
p1.x = p0.x + cvRound(multiplier*hyp*cos(angle));
p1.y = p0.y + cvRound(multiplier*hyp*sin(angle));
cv::line(drawMat,p0,p1,color,1,CV_AA,0);
/*
p0.x = p1.x + cvRound(2*cos(angle-M_PI + M_PI/4));
p0.y = p1.y + cvRound(2*sin(angle-M_PI + M_PI/4));
cv::line( imgMotion, p0, p1, color,1, CV_AA, 0);
p0.x = p1.x + cvRound(2*cos(angle-M_PI - M_PI/4));
p0.y = p1.y + cvRound(2*sin(angle-M_PI - M_PI/4));
cv::line( imgMotion, p0, p1, color,1, CV_AA, 0);
*/
}
}
}
imgMotion.upload( drawMat );
}
void sg::uncropFFTGPU( cv::gpu::GpuMat const & input, cv::gpu::GpuMat & output, std::vector<cv::gpu::GpuMat> & splitBuffer )
{
cv::Size originalSize = input.size();
cv::Size oldSize = output.size();
if( input.channels() > 1 )
{
cv::gpu::split( input, splitBuffer );
splitBuffer[0]( cv::Rect( 0, 0, oldSize.width, oldSize.height ) ).copyTo( output );
}
else
input( cv::Rect( 0, 0, oldSize.width, oldSize.height ) ).copyTo( output );
cv::gpu::multiply( output, ( 1.0 / ( originalSize.width * originalSize.height ) ), output );
}
void MotionSubtraction::subtractGlobalMotion(cv::gpu::GpuMat &in__flowVector3DAngle, cv::gpu::GpuMat &in__flowVector3DMagnitude, cv::gpu::GpuMat &in__globalMotionX, cv::gpu::GpuMat &in__globalMotionY, cv::gpu::GpuMat &out__subtractedAngle, cv::gpu::GpuMat &out__subtractedMagnitude) {
const int64 start = cv::getTickCount();
//----------------------------------------------------------------------------------------
// convert from magnitde/angle to x/y
//----------------------------------------------------------------------------------------
cv::gpu::GpuMat subtractedX(in__flowVector3DAngle.size(), CV_32FC1, cv::Scalar(0.0f));
cv::gpu::GpuMat subtractedY(in__flowVector3DAngle.size(), CV_32FC1, cv::Scalar(0.0f));
cv::gpu::GpuMat flowVector3DX, flowVector3DY;
cv::gpu::polarToCart(in__flowVector3DMagnitude, in__flowVector3DAngle, flowVector3DX, flowVector3DY, true);
//---------------------------------------------------------------------------------------------------------------------------------
// segmentation
//---------------------------------------------------------------------------------------------------------------------------------
kernel.subtractMotion(flowVector3DX, flowVector3DY, in__globalMotionX, in__globalMotionY, subtractedX, subtractedY);
//----------------------------------------------------------------------------------------
// convert from x/y to magnitde/angle
//----------------------------------------------------------------------------------------
cv::gpu::cartToPolar(subtractedX, subtractedY, out__subtractedMagnitude, out__subtractedAngle, true);
//---------------------------------------------------------------------------------------------------------------------------------
// display computation time
//---------------------------------------------------------------------------------------------------------------------------------
const double timeSec = (cv::getTickCount() - start) / cv::getTickFrequency();
std::cout << "Motion subtr : \t" << timeSec << " sec" << std::endl;
}
void cv::gpu::GMG_GPU::operator ()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat& fgmask, float newLearningRate, cv::gpu::Stream& stream)
{
using namespace cv::gpu::cudev::bgfg_gmg;
typedef void (*func_t)(PtrStepSzb frame, PtrStepb fgmask, PtrStepSzi colors, PtrStepf weights, PtrStepi nfeatures,
int frameNum, float learningRate, bool updateBackgroundModel, cudaStream_t stream);
static const func_t funcs[6][4] =
{
{update_gpu<uchar>, 0, update_gpu<uchar3>, update_gpu<uchar4>},
{0,0,0,0},
{update_gpu<ushort>, 0, update_gpu<ushort3>, update_gpu<ushort4>},
{0,0,0,0},
{0,0,0,0},
{update_gpu<float>, 0, update_gpu<float3>, update_gpu<float4>}
};
CV_Assert(frame.depth() == CV_8U || frame.depth() == CV_16U || frame.depth() == CV_32F);
CV_Assert(frame.channels() == 1 || frame.channels() == 3 || frame.channels() == 4);
if (newLearningRate != -1.0f)
{
CV_Assert(newLearningRate >= 0.0f && newLearningRate <= 1.0f);
learningRate = newLearningRate;
}
if (frame.size() != frameSize_)
initialize(frame.size(), 0.0f, frame.depth() == CV_8U ? 255.0f : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0f);
fgmask.create(frameSize_, CV_8UC1);
fgmask.setTo(cv::Scalar::all(0), stream);
funcs[frame.depth()][frame.channels() - 1](frame, fgmask, colors_, weights_, nfeatures_, frameNum_, learningRate, updateBackgroundModel, cv::gpu::StreamAccessor::getStream(stream));
// medianBlur
if (smoothingRadius > 0)
{
boxFilter_->apply(fgmask, buf_, stream);
int minCount = (smoothingRadius * smoothingRadius + 1) / 2;
double thresh = 255.0 * minCount / (smoothingRadius * smoothingRadius);
cv::gpu::threshold(buf_, fgmask, thresh, 255.0, cv::THRESH_BINARY, stream);
}
// keep track of how many frames we have processed
++frameNum_;
}
void cv::gpu::MOG_GPU::operator()(const cv::gpu::GpuMat& frame, cv::gpu::GpuMat& fgmask, float learningRate, Stream& stream)
{
using namespace cv::gpu::cudev::mog;
CV_Assert(frame.depth() == CV_8U);
int ch = frame.channels();
int work_ch = ch;
if (nframes_ == 0 || learningRate >= 1.0 || frame.size() != frameSize_ || work_ch != mean_.channels())
initialize(frame.size(), frame.type());
fgmask.create(frameSize_, CV_8UC1);
++nframes_;
learningRate = learningRate >= 0.0f && nframes_ > 1 ? learningRate : 1.0f / std::min(nframes_, history);
CV_Assert(learningRate >= 0.0f);
mog_gpu(frame, ch, fgmask, weight_, sortKey_, mean_, var_, nmixtures_,
varThreshold, learningRate, backgroundRatio, noiseSigma,
StreamAccessor::getStream(stream));
}