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gpu version of GMG Background Subtractor

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Vladislav Vinogradov
2012-08-09 11:31:08 +04:00
parent 0ceb9b6a00
commit 9ec96597cd
3 changed files with 464 additions and 0 deletions
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modules/gpu/src/bgfg_gmg.cpp Normal file
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
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// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
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// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
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//M*/
#include "precomp.hpp"
#ifndef HAVE_CUDA
cv::gpu::GMG_GPU::GMG_GPU() { throw_nogpu(); }
void cv::gpu::GMG_GPU::initialize(cv::Size, float, float) { throw_nogpu(); }
void cv::gpu::GMG_GPU::operator ()(const cv::gpu::GpuMat&, cv::gpu::GpuMat&, float, cv::gpu::Stream&) { throw_nogpu(); }
#else
namespace cv { namespace gpu { namespace device {
namespace bgfg_gmg
{
void loadConstants(int width, int height, float minVal, float maxVal, int quantizationLevels, float backgroundPrior,
float decisionThreshold, int maxFeatures, int numInitializationFrames);
template <typename SrcT>
void update_gpu(DevMem2Db frame, PtrStepb fgmask, DevMem2Di colors, PtrStepf weights, PtrStepi nfeatures, int frameNum, float learningRate, cudaStream_t stream);
}
}}}
cv::gpu::GMG_GPU::GMG_GPU()
{
maxFeatures = 64;
learningRate = 0.025f;
numInitializationFrames = 120;
quantizationLevels = 16;
backgroundPrior = 0.8f;
decisionThreshold = 0.8f;
smoothingRadius = 7;
}
void cv::gpu::GMG_GPU::initialize(cv::Size frameSize, float min, float max)
{
using namespace cv::gpu::device::bgfg_gmg;
CV_Assert(min < max);
CV_Assert(maxFeatures > 0);
CV_Assert(learningRate >= 0.0f && learningRate <= 1.0f);
CV_Assert(numInitializationFrames >= 1);
CV_Assert(quantizationLevels >= 1 && quantizationLevels <= 255);
CV_Assert(backgroundPrior >= 0.0f && backgroundPrior <= 1.0f);
minVal_ = min;
maxVal_ = max;
frameSize_ = frameSize;
frameNum_ = 0;
nfeatures_.create(frameSize_, CV_32SC1);
colors_.create(maxFeatures * frameSize_.height, frameSize_.width, CV_32SC1);
weights_.create(maxFeatures * frameSize_.height, frameSize_.width, CV_32FC1);
nfeatures_.setTo(cv::Scalar::all(0));
boxFilter_ = cv::gpu::createBoxFilter_GPU(CV_8UC1, CV_8UC1, cv::Size(smoothingRadius, smoothingRadius));
loadConstants(frameSize_.width, frameSize_.height, minVal_, maxVal_, quantizationLevels, backgroundPrior, decisionThreshold, maxFeatures, numInitializationFrames);
}
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::device::bgfg_gmg;
typedef void (*func_t)(DevMem2Db frame, PtrStepb fgmask, DevMem2Di colors, PtrStepf weights, PtrStepi nfeatures,
int frameNum, float learningRate, 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);
funcs[frame.depth()][frame.channels() - 1](frame, fgmask, colors_, weights_, nfeatures_, frameNum_, learningRate, cv::gpu::StreamAccessor::getStream(stream));
// medianBlur
boxFilter_->apply(fgmask, buf_, cv::Rect(0,0,-1,-1), 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_;
}
#endif