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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
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Copyright (C) 2013, OpenCV Foundation, 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 the copyright holders 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 a particular BackgroundSubtraction 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"
#include "opencv2/core/utility.hpp"
#include <limits>
namespace cv
{
namespace bgsegm
{
class BackgroundSubtractorGMGImpl CV_FINAL : public BackgroundSubtractorGMG
{
public:
BackgroundSubtractorGMGImpl()
{
/*
* 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;
minVal_ = maxVal_ = 0;
name_ = "BackgroundSubtractor.GMG";
}
~BackgroundSubtractorGMGImpl()
{
}
/**
* Validate parameters and set up data structures for appropriate image size.
* Must call before running on data.
* @param frameSize input frame size
* @param min minimum value taken on by pixels in image sequence. Usually 0
* @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
*/
void initialize(Size frameSize, double minVal, double maxVal);
/**
* Performs single-frame background subtraction and builds up a statistical background image
* model.
* @param image Input image
* @param fgmask Output mask image representing foreground and background pixels
*/
virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1.0) CV_OVERRIDE;
/**
* Releases all inner buffers.
*/
void release();
virtual int getMaxFeatures() const CV_OVERRIDE { return maxFeatures; }
virtual void setMaxFeatures(int _maxFeatures) CV_OVERRIDE { maxFeatures = _maxFeatures; }
virtual double getDefaultLearningRate() const CV_OVERRIDE { return learningRate; }
virtual void setDefaultLearningRate(double lr) CV_OVERRIDE { learningRate = lr; }
virtual int getNumFrames() const CV_OVERRIDE { return numInitializationFrames; }
virtual void setNumFrames(int nframes) CV_OVERRIDE { numInitializationFrames = nframes; }
virtual int getQuantizationLevels() const CV_OVERRIDE { return quantizationLevels; }
virtual void setQuantizationLevels(int nlevels) CV_OVERRIDE { quantizationLevels = nlevels; }
virtual double getBackgroundPrior() const CV_OVERRIDE { return backgroundPrior; }
virtual void setBackgroundPrior(double bgprior) CV_OVERRIDE { backgroundPrior = bgprior; }
virtual int getSmoothingRadius() const CV_OVERRIDE { return smoothingRadius; }
virtual void setSmoothingRadius(int radius) CV_OVERRIDE { smoothingRadius = radius; }
virtual double getDecisionThreshold() const CV_OVERRIDE { return decisionThreshold; }
virtual void setDecisionThreshold(double thresh) CV_OVERRIDE { decisionThreshold = thresh; }
virtual bool getUpdateBackgroundModel() const CV_OVERRIDE { return updateBackgroundModel; }
virtual void setUpdateBackgroundModel(bool update) CV_OVERRIDE { updateBackgroundModel = update; }
virtual double getMinVal() const CV_OVERRIDE { return minVal_; }
virtual void setMinVal(double val) CV_OVERRIDE { minVal_ = val; }
virtual double getMaxVal() const CV_OVERRIDE { return maxVal_; }
virtual void setMaxVal(double val) CV_OVERRIDE { maxVal_ = val; }
virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE
{
backgroundImage.release();
}
virtual void write(FileStorage& fs) const CV_OVERRIDE
{
fs << "name" << name_
<< "maxFeatures" << maxFeatures
<< "defaultLearningRate" << learningRate
<< "numFrames" << numInitializationFrames
<< "quantizationLevels" << quantizationLevels
<< "backgroundPrior" << backgroundPrior
<< "decisionThreshold" << decisionThreshold
<< "smoothingRadius" << smoothingRadius
<< "updateBackgroundModel" << (int)updateBackgroundModel;
// we do not save minVal_ & maxVal_, since they depend on the image type.
}
virtual void read(const FileNode& fn) CV_OVERRIDE
{
CV_Assert( (String)fn["name"] == name_ );
maxFeatures = (int)fn["maxFeatures"];
learningRate = (double)fn["defaultLearningRate"];
numInitializationFrames = (int)fn["numFrames"];
quantizationLevels = (int)fn["quantizationLevels"];
backgroundPrior = (double)fn["backgroundPrior"];
smoothingRadius = (int)fn["smoothingRadius"];
decisionThreshold = (double)fn["decisionThreshold"];
updateBackgroundModel = (int)fn["updateBackgroundModel"] != 0;
minVal_ = maxVal_ = 0;
frameSize_ = Size();
}
//! Total number of distinct colors to maintain in histogram.
int maxFeatures;
//! Set between 0.0 and 1.0, determines how quickly features are "forgotten" from histograms.
double learningRate;
//! Number of frames of video to use to initialize histograms.
int numInitializationFrames;
//! Number of discrete levels in each channel to be used in histograms.
int quantizationLevels;
//! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
double backgroundPrior;
//! Value above which pixel is determined to be FG.
double decisionThreshold;
//! Smoothing radius, in pixels, for cleaning up FG image.
int smoothingRadius;
//! Perform background model update
bool updateBackgroundModel;
private:
double maxVal_;
double minVal_;
Size frameSize_;
int frameNum_;
String name_;
Mat_<int> nfeatures_;
Mat_<int> colors_;
Mat_<float> weights_;
};
void BackgroundSubtractorGMGImpl::initialize(Size frameSize, double minVal, double maxVal)
{
CV_Assert(minVal < maxVal);
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_ = minVal;
maxVal_ = maxVal;
frameSize_ = frameSize;
frameNum_ = 0;
nfeatures_.create(frameSize_);
colors_.create(frameSize_.area(), maxFeatures);
weights_.create(frameSize_.area(), maxFeatures);
nfeatures_.setTo(Scalar::all(0));
}
static float findFeature(int color, const 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;
}
static 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;
}
}
static bool insertFeature(int color, float weight, 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(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(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;
}
template <typename T> struct Quantization
{
static 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 ParallelLoopBody
{
public:
GMG_LoopBody(const Mat& frame, const Mat& fgmask, const Mat_<int>& nfeatures, const Mat_<int>& colors, const 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 Range& range) const CV_OVERRIDE;
private:
Mat frame_;
mutable Mat_<uchar> fgmask_;
mutable Mat_<int> nfeatures_;
mutable Mat_<int> colors_;
mutable 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 Range& range) const
{
typedef 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];
int* colors = colors_[featureIdx];
float* weights = weights_[featureIdx];
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 if (updateBackgroundModel_)
{
// 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 BackgroundSubtractorGMGImpl::apply(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_)
{
double minval = minVal_;
double maxval = maxVal_;
if( minVal_ == 0 && maxVal_ == 0 )
{
minval = 0;
maxval = frame.depth() == CV_8U ? 255.0 : frame.depth() == CV_16U ? std::numeric_limits<ushort>::max() : 1.0;
}
initialize(frame.size(), minval, maxval);
}
_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, fgmask, smoothingRadius);
}
// keep track of how many frames we have processed
++frameNum_;
}
void BackgroundSubtractorGMGImpl::release()
{
frameSize_ = Size();
nfeatures_.release();
colors_.release();
weights_.release();
}
Ptr<BackgroundSubtractorGMG> createBackgroundSubtractorGMG(int initializationFrames, double decisionThreshold)
{
Ptr<BackgroundSubtractorGMG> bgfg = makePtr<BackgroundSubtractorGMGImpl>();
bgfg->setNumFrames(initializationFrames);
bgfg->setDecisionThreshold(decisionThreshold);
return bgfg;
}
/*
///////////////////////////////////////////////////////////////////////////////////////////////////////////
CV_INIT_ALGORITHM(BackgroundSubtractorGMG, "BackgroundSubtractor.GMG",
obj.info()->addParam(obj, "maxFeatures", obj.maxFeatures,false,0,0,
"Maximum number of features to store in histogram. Harsh enforcement of sparsity constraint.");
obj.info()->addParam(obj, "learningRate", obj.learningRate,false,0,0,
"Adaptation rate of histogram. Close to 1, slow adaptation. Close to 0, fast adaptation, features forgotten quickly.");
obj.info()->addParam(obj, "initializationFrames", obj.numInitializationFrames,false,0,0,
"Number of frames to use to initialize histograms of pixels.");
obj.info()->addParam(obj, "quantizationLevels", obj.quantizationLevels,false,0,0,
"Number of discrete colors to be used in histograms. Up-front quantization.");
obj.info()->addParam(obj, "backgroundPrior", obj.backgroundPrior,false,0,0,
"Prior probability that each individual pixel is a background pixel.");
obj.info()->addParam(obj, "smoothingRadius", obj.smoothingRadius,false,0,0,
"Radius of smoothing kernel to filter noise from FG mask image.");
obj.info()->addParam(obj, "decisionThreshold", obj.decisionThreshold,false,0,0,
"Threshold for FG decision rule. Pixel is FG if posterior probability exceeds threshold.");
obj.info()->addParam(obj, "updateBackgroundModel", obj.updateBackgroundModel,false,0,0,
"Perform background model update.");
obj.info()->addParam(obj, "minVal", obj.minVal_,false,0,0,
"Minimum of the value range (mostly for regression testing)");
obj.info()->addParam(obj, "maxVal", obj.maxVal_,false,0,0,
"Maximum of the value range (mostly for regression testing)");
);
*/
}
}