void sg::DoGCenterSurround::operator()( cv::gpu::GpuMat const & input, cv::gpu::GpuMat & output )
{
if( !itsIsValid )
return;
// Zero the output
output.setTo( 0.0f );
for( auto & filter : itsFilters )
{
cv::gpu::mulSpectrums( input, filter, itsBuffer, cv::DFT_COMPLEX_OUTPUT );
addFC2Wrapper( output, itsBuffer, output );
}
mulValueFC2Wrapper( output, 1.0f / itsFilters.size(), output );
}
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 detectKeypoints(cv::gpu::GpuMat& keypoints, int scales)
{
ensureSizeIsEnough(SIFT_GPU::ROWS_COUNT, MAXEXTREMAS, CV_32FC1, keypoints);
keypoints.setTo(cv::Scalar::all(0));
for (int octave = 0; octave < 1; ++octave)
{
const int scaleCols = cols >> octave;
const int scaleRows = rows >> octave;
createDoGSpace(inImage.data, &deviceDoGData, scales, scaleRows, scaleCols);
findExtremas(deviceDoGData, &sift_.extremaBuffer, &maxCounter, octave, scales, scaleRows, scaleCols);
localization(deviceDoGData, scaleRows, scaleCols, scales, octave, sift_.nOctaves, sift_.extremaBuffer, maxCounter,
keypoints.ptr<float>(SIFT_GPU::X_ROW), keypoints.ptr<float>(SIFT_GPU::Y_ROW),
keypoints.ptr<float>(SIFT_GPU::OCTAVE_ROW), keypoints.ptr<float>(SIFT_GPU::SIZE_ROW),
keypoints.ptr<float>(SIFT_GPU::ANGLE_ROW), keypoints.ptr<float>(SIFT_GPU::RESPONSE_ROW));
}
std::cout << "Number of keypoints: " << maxCounter[0] << std::endl;
}
void sg::LogGabor::getEdgeResponses( cv::gpu::GpuMat const & fftImage, cv::gpu::GpuMat & edges,
std::vector<cv::gpu::GpuMat> & splitBuffer, DoGCenterSurround & dog )
{
if( !itsValid )
{
std::cerr << "This LogGabor bank is not initialized!\n";
return;
}
edges.setTo( 0.0f );
// outer loop for orientations
for( size_t o = 0; o < itsFilters.size(); ++o )
{
// inner loop over scales
for( size_t s = 0; s < itsGaborScales.size(); ++s )
{
// see note in addFilters about why this is a normal multiplication
// and not a spectrum multiply
mulFC2Wrapper( fftImage, itsFilters[o][s], itsFFTBuffer[0] );
cv::gpu::dft( itsFFTBuffer[0], itsFFTBuffer[1], itsFFTBuffer[0].size(), cv::DFT_INVERSE );
// Get magnitude
cv::gpu::magnitude( itsFFTBuffer[1], splitBuffer[0] );
// Compute edge response
// the edge response is a DoG applied to the magnitude
// so we'll pad the magnitude with zero valued complex
// take the DFT and then pass it through the DoG processing chain
splitBuffer[1].setTo( 0.0f );
cv::gpu::GpuMat merge[] = { splitBuffer[0], splitBuffer[1] };
cv::gpu::merge( merge, 2, itsFFTBuffer[0] );
cv::gpu::dft( itsFFTBuffer[0], itsFFTBuffer[0], itsFFTBuffer[0].size() );
dog( itsFFTBuffer[0], itsFFTBuffer[2] );
// accumulate result into edges
addFC2Wrapper( edges, itsFFTBuffer[2], edges );
} // end scales
} // end orientations
}
