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 }