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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// 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
//
// Copyright (C) 2000, Intel Corporation, 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:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * 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.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* This class implements an algorithm described in "Visual Tracking of Human Visitors under
* Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
* A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
*
* Prepared and integrated by Andrew B. Godbehere.
*/
#include "precomp.hpp"
cv::BackgroundSubtractorGMG::BackgroundSubtractorGMG()
{
/*
* Default Parameter Values. Override with algorithm "set" method.
*/
maxFeatures = 64;
learningRate = 0.025;
numInitializationFrames = 120;
quantizationLevels = 16;
backgroundPrior = 0.8;
decisionThreshold = 0.8;
smoothingRadius = 7;
updateBackgroundModel = true;
}
cv::BackgroundSubtractorGMG::~BackgroundSubtractorGMG()
{
}
void cv::BackgroundSubtractorGMG::initialize(cv::Size frameSize, double min, double max)
{
CV_Assert(min < max);
CV_Assert(maxFeatures > 0);
CV_Assert(learningRate >= 0.0 && learningRate <= 1.0);
CV_Assert(numInitializationFrames >= 1);
CV_Assert(quantizationLevels >= 1 && quantizationLevels <= 255);
CV_Assert(backgroundPrior >= 0.0 && backgroundPrior <= 1.0);
minVal_ = min;
maxVal_ = max;
frameSize_ = frameSize;
frameNum_ = 0;
nfeatures_.create(frameSize_);
colors_.create(frameSize_.area(), maxFeatures);
weights_.create(frameSize_.area(), maxFeatures);
nfeatures_.setTo(cv::Scalar::all(0));
}
namespace
{
float findFeature(unsigned int color, const unsigned int* colors, const float* weights, int nfeatures)
{
for (int i = 0; i < nfeatures; ++i)
{
if (color == colors[i])
return weights[i];
}
// not in histogram, so return 0.
return 0.0f;
}
void normalizeHistogram(float* weights, int nfeatures)
{
float total = 0.0f;
for (int i = 0; i < nfeatures; ++i)
total += weights[i];
if (total != 0.0f)
{
for (int i = 0; i < nfeatures; ++i)
weights[i] /= total;
}
}
bool insertFeature(unsigned int color, float weight, unsigned int* colors, float* weights, int& nfeatures, int maxFeatures)
{
int idx = -1;
for (int i = 0; i < nfeatures; ++i)
{
if (color == colors[i])
{
// feature in histogram
weight += weights[i];
idx = i;
break;
}
}
if (idx >= 0)
{
// move feature to beginning of list
::memmove(colors + 1, colors, idx * sizeof(unsigned int));
::memmove(weights + 1, weights, idx * sizeof(float));
colors[0] = color;
weights[0] = weight;
}
else if (nfeatures == maxFeatures)
{
// discard oldest feature
::memmove(colors + 1, colors, (nfeatures - 1) * sizeof(unsigned int));
::memmove(weights + 1, weights, (nfeatures - 1) * sizeof(float));
colors[0] = color;
weights[0] = weight;
}
else
{
colors[nfeatures] = color;
weights[nfeatures] = weight;
++nfeatures;
return true;
}
return false;
}
}
namespace
{
template <typename T> struct Quantization
{
static unsigned int apply(const void* src_, int x, int cn, double minVal, double maxVal, int quantizationLevels)
{
const T* src = static_cast<const T*>(src_);
src += x * cn;
unsigned int res = 0;
for (int i = 0, shift = 0; i < cn; ++i, ++src, shift += 8)
res |= static_cast<int>((*src - minVal) * quantizationLevels / (maxVal - minVal)) << shift;
return res;
}
};
class GMG_LoopBody : public cv::ParallelLoopBody
{
public:
GMG_LoopBody(const cv::Mat& frame, const cv::Mat& fgmask, const cv::Mat_<int>& nfeatures, const cv::Mat_<unsigned int>& colors, const cv::Mat_<float>& weights,
int maxFeatures, double learningRate, int numInitializationFrames, int quantizationLevels, double backgroundPrior, double decisionThreshold,
double maxVal, double minVal, int frameNum, bool updateBackgroundModel) :
frame_(frame), fgmask_(fgmask), nfeatures_(nfeatures), colors_(colors), weights_(weights),
maxFeatures_(maxFeatures), learningRate_(learningRate), numInitializationFrames_(numInitializationFrames), quantizationLevels_(quantizationLevels),
backgroundPrior_(backgroundPrior), decisionThreshold_(decisionThreshold), updateBackgroundModel_(updateBackgroundModel),
maxVal_(maxVal), minVal_(minVal), frameNum_(frameNum)
{
}
void operator() (const cv::Range& range) const;
private:
cv::Mat frame_;
mutable cv::Mat_<uchar> fgmask_;
mutable cv::Mat_<int> nfeatures_;
mutable cv::Mat_<unsigned int> colors_;
mutable cv::Mat_<float> weights_;
int maxFeatures_;
double learningRate_;
int numInitializationFrames_;
int quantizationLevels_;
double backgroundPrior_;
double decisionThreshold_;
bool updateBackgroundModel_;
double maxVal_;
double minVal_;
int frameNum_;
};
void GMG_LoopBody::operator() (const cv::Range& range) const
{
typedef unsigned int (*func_t)(const void* src_, int x, int cn, double minVal, double maxVal, int quantizationLevels);
static const func_t funcs[] =
{
Quantization<uchar>::apply,
Quantization<schar>::apply,
Quantization<ushort>::apply,
Quantization<short>::apply,
Quantization<int>::apply,
Quantization<float>::apply,
Quantization<double>::apply
};
const func_t func = funcs[frame_.depth()];
CV_Assert(func != 0);
const int cn = frame_.channels();
for (int y = range.start, featureIdx = y * frame_.cols; y < range.end; ++y)
{
const uchar* frame_row = frame_.ptr(y);
int* nfeatures_row = nfeatures_[y];
uchar* fgmask_row = fgmask_[y];
for (int x = 0; x < frame_.cols; ++x, ++featureIdx)
{
int nfeatures = nfeatures_row[x];
unsigned int* colors = colors_[featureIdx];
float* weights = weights_[featureIdx];
unsigned int newFeatureColor = func(frame_row, x, cn, minVal_, maxVal_, quantizationLevels_);
bool isForeground = false;
if (frameNum_ >= numInitializationFrames_)
{
// typical operation
const double weight = findFeature(newFeatureColor, colors, weights, nfeatures);
// see Godbehere, Matsukawa, Goldberg (2012) for reasoning behind this implementation of Bayes rule
const double posterior = (weight * backgroundPrior_) / (weight * backgroundPrior_ + (1.0 - weight) * (1.0 - backgroundPrior_));
isForeground = ((1.0 - posterior) > decisionThreshold_);
// update histogram.
if (updateBackgroundModel_)
{
for (int i = 0; i < nfeatures; ++i)
weights[i] *= (float)(1.0f - learningRate_);
bool inserted = insertFeature(newFeatureColor, (float)learningRate_, colors, weights, nfeatures, maxFeatures_);
if (inserted)
{
normalizeHistogram(weights, nfeatures);
nfeatures_row[x] = nfeatures;
}
}
}
else
{
// training-mode update
insertFeature(newFeatureColor, 1.0f, colors, weights, nfeatures, maxFeatures_);
if (frameNum_ == numInitializationFrames_ - 1)
normalizeHistogram(weights, nfeatures);
}
fgmask_row[x] = (uchar)(-(schar)isForeground);
}
}
}
}
void cv::BackgroundSubtractorGMG::operator ()(InputArray _frame, OutputArray _fgmask, double newLearningRate)
{
Mat frame = _frame.getMat();
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 != -1.0)
{
CV_Assert(newLearningRate >= 0.0 && newLearningRate <= 1.0);
learningRate = newLearningRate;
}
if (frame.size() != frameSize_)
initialize(frame.size(), 0.0, frame.depth() == CV_8U ? 255.0 : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0);
_fgmask.create(frameSize_, CV_8UC1);
Mat fgmask = _fgmask.getMat();
GMG_LoopBody body(frame, fgmask, nfeatures_, colors_, weights_,
maxFeatures, learningRate, numInitializationFrames, quantizationLevels, backgroundPrior, decisionThreshold,
maxVal_, minVal_, frameNum_, updateBackgroundModel);
parallel_for_(Range(0, frame.rows), body, frame.total()/(double)(1<<16));
if (smoothingRadius > 0)
{
medianBlur(fgmask, buf_, smoothingRadius);
cv::swap(fgmask, buf_);
}
// keep track of how many frames we have processed
++frameNum_;
}
void cv::BackgroundSubtractorGMG::release()
{
frameSize_ = cv::Size();
nfeatures_.release();
colors_.release();
weights_.release();
buf_.release();
}