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Commit 42c1d4f4 authored by Vladislav Vinogradov's avatar Vladislav Vinogradov
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new optimized version of BackgroundSubtractorGMG

parent 1995b1a0
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Showing with 87 additions and 224 deletions
modules/video/include/opencv2/video/background_segm.hpp
View file @ 42c1d4f4
......@@ -199,111 +199,20 @@ protected:
*/
class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor
{
protected:
/**
* Used internally to represent a single feature in a histogram.
* Feature is a color and an associated likelihood (weight in the histogram).
*/
struct CV_EXPORTS HistogramFeatureGMG
{
/**
* Default constructor.
* Initializes likelihood of feature to 0, color remains uninitialized.
*/
HistogramFeatureGMG(){likelihood = 0.0;}
/**
* Copy constructor.
* Required to use HistogramFeatureGMG in a std::vector
* @see operator =()
*/
HistogramFeatureGMG(const HistogramFeatureGMG& orig){
color = orig.color; likelihood = orig.likelihood;
}
/**
* Assignment operator.
* Required to use HistogramFeatureGMG in a std::vector
*/
HistogramFeatureGMG& operator =(const HistogramFeatureGMG& orig){
color = orig.color; likelihood = orig.likelihood; return *this;
}
/**
* Tests equality of histogram features.
* Equality is tested only by matching the color (feature), not the likelihood.
* This operator is used to look up an observed feature in a histogram.
*/
bool operator ==(HistogramFeatureGMG &rhs);
//! Regardless of the image datatype, it is quantized and mapped to an integer and represented as a vector.
vector<size_t> color;
//! Represents the weight of feature in the histogram.
float likelihood;
friend class PixelModelGMG;
};
/**
* Representation of the statistical model of a single pixel for use in the background subtraction
* algorithm.
*/
class CV_EXPORTS PixelModelGMG
{
public:
PixelModelGMG();
~PixelModelGMG();
/**
* Incorporate the last observed feature into the statistical model.
*
* @param learningRate The adaptation parameter for the histogram. -1.0 to use default. Value
* should be between 0.0 and 1.0, the higher the value, the faster the
* adaptation. 1.0 is limiting case where fast adaptation means no memory.
*/
void insertFeature(double learningRate = -1.0);
/**
* Set the feature last observed, to save before incorporating it into the statistical
* model with insertFeature().
*
* @param feature The feature (color) just observed.
*/
void setLastObservedFeature(BackgroundSubtractorGMG::HistogramFeatureGMG feature);
/**
* Set the upper limit for the number of features to store in the histogram. Use to adjust
* memory requirements.
*
* @param max size_t representing the max number of features.
*/
void setMaxFeatures(size_t max) {
maxFeatures = max; histogram.resize(max); histogram.clear();
}
/**
* Normalize the histogram, so sum of weights of all features = 1.0
*/
void normalizeHistogram();
/**
* Return the weight of a feature in the histogram. If the feature is not represented in the
* histogram, the weight returned is 0.0.
*/
double getLikelihood(HistogramFeatureGMG f);
PixelModelGMG& operator *=(const float &rhs);
//friend class BackgroundSubtractorGMG;
//friend class HistogramFeatureGMG;
private:
size_t numFeatures; //!< number of features in histogram
size_t maxFeatures; //!< max allowable features in histogram
std::list<HistogramFeatureGMG> histogram; //!< represents the histogram as a list of features
HistogramFeatureGMG lastObservedFeature;
//!< store last observed feature in case we need to add it to histogram
};
public:
BackgroundSubtractorGMG();
virtual ~BackgroundSubtractorGMG();
virtual AlgorithmInfo* info() const;
/**
* 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(cv::Size frameSize, double min, double max);
/**
* Performs single-frame background subtraction and builds up a statistical background image
* model.
......@@ -312,28 +221,6 @@ public:
*/
virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0);
/**
* Validate parameters and set up data structures for appropriate image type. Must call before
* running on data.
* @param image One sample image from dataset
* @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 initializeType(InputArray image, double min, double max);
/**
* Selectively update the background model. Only update background model for pixels identified
* as background.
* @param mask Mask image same size as images in sequence. Must be 8UC1 matrix, 255 for foreground
* and 0 for background.
*/
void updateBackgroundModel(InputArray mask);
/**
* Retrieve the greyscale image representing the probability that each pixel is foreground given
* the current estimated background model. Values are 0.0 (black) to 1.0 (white).
* @param img The 32FC1 image representing per-pixel probabilities that the pixel is foreground.
*/
void getPosteriorImage(OutputArray img);
protected:
//! Total number of distinct colors to maintain in histogram.
int maxFeatures;
......@@ -345,31 +232,23 @@ protected:
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;
double decisionThreshold; //!< value above which pixel is determined to be FG.
int smoothingRadius; //!< smoothing radius, in pixels, for cleaning up FG image.
private:
double maxVal_;
double minVal_;
double maxVal, minVal;
cv::Size frameSize_;
size_t frameNum_;
/*
* General Parameters
*/
int imWidth; //!< width of image.
int imHeight; //!< height of image.
size_t numPixels;
cv::Mat_<int> nfeatures_;
cv::Mat_<int> colors_;
cv::Mat_<float> weights_;
unsigned int numChannels; //!< Number of channels in image.
bool isDataInitialized;
//!< After general parameters are set, data structures must be initialized.
/*
* Data Structures
*/
vector<PixelModelGMG> pixels; //!< Probabilistic background models for each pixel in image.
int frameNum; //!< Frame number counter, used to count frames in training mode.
Mat posteriorImage; //!< Posterior probability image.
Mat fgMaskImage; //!< Foreground mask image.
cv::Mat buf_;
};
}
......
modules/video/src/bgfg_gmg.cpp
View file @ 42c1d4f4
This diff is collapsed.
modules/video/test/test_backgroundsubtractor_gbh.cpp
View file @ 42c1d4f4
......@@ -115,43 +115,43 @@ void CV_BackgroundSubtractorTest::run(int)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned char)(minuc/2+maxuc/2),maxuc);
if (i == 0)
fgbg->initializeType(simImage,minuc,maxuc);
fgbg->initialize(simImage.size(),minuc,maxuc);
}
else if (type == CV_8S)
{
rng.fill(simImage,RNG::UNIFORM,(char)(minc/2+maxc/2),maxc);
if (i==0)
fgbg->initializeType(simImage,minc,maxc);
fgbg->initialize(simImage.size(),minc,maxc);
}
else if (type == CV_16U)
{
rng.fill(simImage,RNG::UNIFORM,(unsigned int)(minui/2+maxui/2),maxui);
if (i==0)
fgbg->initializeType(simImage,minui,maxui);
fgbg->initialize(simImage.size(),minui,maxui);
}
else if (type == CV_16S)
{
rng.fill(simImage,RNG::UNIFORM,(int)(mini/2+maxi/2),maxi);
if (i==0)
fgbg->initializeType(simImage,mini,maxi);
fgbg->initialize(simImage.size(),mini,maxi);
}
else if (type == CV_32F)
{
rng.fill(simImage,RNG::UNIFORM,(float)(minf/2.0+maxf/2.0),maxf);
if (i==0)
fgbg->initializeType(simImage,minf,maxf);
fgbg->initialize(simImage.size(),minf,maxf);
}
else if (type == CV_32S)
{
rng.fill(simImage,RNG::UNIFORM,(long int)(minli/2+maxli/2),maxli);
if (i==0)
fgbg->initializeType(simImage,minli,maxli);
fgbg->initialize(simImage.size(),minli,maxli);
}
else if (type == CV_64F)
{
rng.fill(simImage,RNG::UNIFORM,(double)(mind/2.0+maxd/2.0),maxd);
if (i==0)
fgbg->initializeType(simImage,mind,maxd);
fgbg->initialize(simImage.size(),mind,maxd);
}
/**
......@@ -159,7 +159,6 @@ void CV_BackgroundSubtractorTest::run(int)
*/
(*fgbg)(simImage,fgmask);
Mat fullbg = Mat::zeros(simImage.rows, simImage.cols, CV_8U);
fgbg->updateBackgroundModel(fullbg);
//! fgmask should be entirely background during training
code = cvtest::cmpEps2( ts, fgmask, fullbg, 0, false, "The training foreground mask" );
......
samples/cpp/bgfg_gmg.cpp
View file @ 42c1d4f4
......@@ -7,91 +7,76 @@
#include <opencv2/opencv.hpp>
#include <iostream>
#include <sstream>
using namespace cv;
static void help()
{
std::cout <<
"\nA program demonstrating the use and capabilities of a particular BackgroundSubtraction\n"
"algorithm described in A. Godbehere, A. Matsukawa, K. Goldberg, \n"
"\"Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive\n"
"Audio Art Installation\", American Control Conference, 2012, used in an interactive\n"
"installation at the Contemporary Jewish Museum in San Francisco, CA from March 31 through\n"
"July 31, 2011.\n"
"Call:\n"
"./BackgroundSubtractorGMG_sample\n"
"Using OpenCV version " << CV_VERSION << "\n"<<std::endl;
std::cout <<
"\nA program demonstrating the use and capabilities of a particular BackgroundSubtraction\n"
"algorithm described in A. Godbehere, A. Matsukawa, K. Goldberg, \n"
"\"Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive\n"
"Audio Art Installation\", American Control Conference, 2012, used in an interactive\n"
"installation at the Contemporary Jewish Museum in San Francisco, CA from March 31 through\n"
"July 31, 2011.\n"
"Call:\n"
"./BackgroundSubtractorGMG_sample\n"
"Using OpenCV version " << CV_VERSION << "\n"<<std::endl;
}
int main(int argc, char** argv)
{
help();
setUseOptimized(true);
setNumThreads(8);
Ptr<BackgroundSubtractorGMG> fgbg = Algorithm::create<BackgroundSubtractorGMG>("BackgroundSubtractor.GMG");
if (fgbg == NULL)
{
CV_Error(CV_StsError,"Failed to create Algorithm\n");
}
fgbg->set("smoothingRadius",7);
fgbg->set("decisionThreshold",0.7);
VideoCapture cap;
if( argc > 1 )
help();
initModule_video();
setUseOptimized(true);
setNumThreads(8);
Ptr<BackgroundSubtractorGMG> fgbg = Algorithm::create<BackgroundSubtractorGMG>("BackgroundSubtractor.GMG");
if (fgbg.empty())
{
std::cerr << "Failed to create BackgroundSubtractor.GMG Algorithm." << std::endl;
return -1;
}
fgbg->set("initializationFrames", 20);
fgbg->set("decisionThreshold", 0.7);
VideoCapture cap;
if (argc > 1)
cap.open(argv[1]);
else
cap.open(0);
if (!cap.isOpened())
{
std::cout << "error: cannot read video. Try moving video file to sample directory.\n";
return -1;
}
Mat img, downimg, downimg2, fgmask, upfgmask, posterior, upposterior;
bool first = true;
namedWindow("posterior");
namedWindow("fgmask");
namedWindow("FG Segmentation");
int i = 0;
for (;;)
{
std::stringstream txt;
txt << "frame: ";
txt << i++;
cap >> img;
putText(img,txt.str(),Point(20,40),FONT_HERSHEY_SIMPLEX,0.8,Scalar(1.0,0.0,0.0));
resize(img,downimg,Size(160,120),0,0,INTER_NEAREST); // Size(cols, rows) or Size(width,height)
if (first)
{
fgbg->initializeType(downimg,0,255);
first = false;
}
if (img.empty())
{
return 0;
}
(*fgbg)(downimg,fgmask);
fgbg->updateBackgroundModel(Mat::zeros(120,160,CV_8U));
fgbg->getPosteriorImage(posterior);
resize(fgmask,upfgmask,Size(640,480),0,0,INTER_NEAREST);
Mat coloredFG = Mat::zeros(480,640,CV_8UC3);
coloredFG.setTo(Scalar(100,100,0),upfgmask);
resize(posterior,upposterior,Size(640,480),0,0,INTER_NEAREST);
imshow("posterior",upposterior);
imshow("fgmask",upfgmask);
resize(img, downimg2, Size(640, 480),0,0,INTER_LINEAR);
imshow("FG Segmentation",downimg2 + coloredFG);
if (!cap.isOpened())
{
std::cerr << "Cannot read video. Try moving video file to sample directory." << std::endl;
return -1;
}
Mat frame, fgmask, segm;
namedWindow("FG Segmentation", WINDOW_NORMAL);
for (;;)
{
cap >> frame;
if (frame.empty())
break;
(*fgbg)(frame, fgmask);
frame.copyTo(segm);
add(frame, Scalar(100, 100, 0), segm, fgmask);
imshow("FG Segmentation", segm);
int c = waitKey(30);
if( c == 'q' || c == 'Q' || (c & 255) == 27 )
break;
}
if (c == 'q' || c == 'Q' || (c & 255) == 27)
break;
}
return 0;
}
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