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 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 meanStdDev(const cv::gpu::GpuMat& mtx,cv::Scalar& mean, cv::Scalar& stddev){ assert(1==mtx.channels()); size_t elem_num = mtx.rows * mtx.cols; cv::gpu::GpuMat buf; mean = cv::gpu::sum( mtx, buf); stddev = cv::gpu::sqrSum( mtx, buf); for(int i=0;i<mean.cols;i++){ mean[i] /= elem_num; } stddev = cv::gpu::sqrSum( mtx, buf); for(int i=0;i<stddev.cols;i++){ stddev[i] /= elem_num; stddev[i] = std::sqrt(stddev[i] - std::pow(mean[i],2)); } }
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)); }