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Revert "Merge pull request #836 from jet47:gpu-modules"

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This reverts commit fba72cb60d, reversing
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Andrey Kamaev
2013-04-18 15:03:50 +04:00
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modules/gpubgsegm/include/opencv2/gpubgsegm.hpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// copy or use the software.
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// License Agreement
// For Open Source Computer Vision Library
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//M*/
#ifndef __OPENCV_GPUBGSEGM_HPP__
#define __OPENCV_GPUBGSEGM_HPP__
#ifndef __cplusplus
# error gpubgsegm.hpp header must be compiled as C++
#endif
#include <memory>
#include "opencv2/core/gpumat.hpp"
#include "opencv2/gpufilters.hpp"
namespace cv { namespace gpu {
// Foreground Object Detection from Videos Containing Complex Background.
// Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian.
// ACM MM2003 9p
class CV_EXPORTS FGDStatModel
{
public:
struct CV_EXPORTS Params
{
int Lc; // Quantized levels per 'color' component. Power of two, typically 32, 64 or 128.
int N1c; // Number of color vectors used to model normal background color variation at a given pixel.
int N2c; // Number of color vectors retained at given pixel. Must be > N1c, typically ~ 5/3 of N1c.
// Used to allow the first N1c vectors to adapt over time to changing background.
int Lcc; // Quantized levels per 'color co-occurrence' component. Power of two, typically 16, 32 or 64.
int N1cc; // Number of color co-occurrence vectors used to model normal background color variation at a given pixel.
int N2cc; // Number of color co-occurrence vectors retained at given pixel. Must be > N1cc, typically ~ 5/3 of N1cc.
// Used to allow the first N1cc vectors to adapt over time to changing background.
bool is_obj_without_holes; // If TRUE we ignore holes within foreground blobs. Defaults to TRUE.
int perform_morphing; // Number of erode-dilate-erode foreground-blob cleanup iterations.
// These erase one-pixel junk blobs and merge almost-touching blobs. Default value is 1.
float alpha1; // How quickly we forget old background pixel values seen. Typically set to 0.1.
float alpha2; // "Controls speed of feature learning". Depends on T. Typical value circa 0.005.
float alpha3; // Alternate to alpha2, used (e.g.) for quicker initial convergence. Typical value 0.1.
float delta; // Affects color and color co-occurrence quantization, typically set to 2.
float T; // A percentage value which determines when new features can be recognized as new background. (Typically 0.9).
float minArea; // Discard foreground blobs whose bounding box is smaller than this threshold.
// default Params
Params();
};
// out_cn - channels count in output result (can be 3 or 4)
// 4-channels require more memory, but a bit faster
explicit FGDStatModel(int out_cn = 3);
explicit FGDStatModel(const cv::gpu::GpuMat& firstFrame, const Params& params = Params(), int out_cn = 3);
~FGDStatModel();
void create(const cv::gpu::GpuMat& firstFrame, const Params& params = Params());
void release();
int update(const cv::gpu::GpuMat& curFrame);
//8UC3 or 8UC4 reference background image
cv::gpu::GpuMat background;
//8UC1 foreground image
cv::gpu::GpuMat foreground;
std::vector< std::vector<cv::Point> > foreground_regions;
private:
FGDStatModel(const FGDStatModel&);
FGDStatModel& operator=(const FGDStatModel&);
class Impl;
std::auto_ptr<Impl> impl_;
};
/*!
Gaussian Mixture-based Backbround/Foreground Segmentation Algorithm
The class implements the following algorithm:
"An improved adaptive background mixture model for real-time tracking with shadow detection"
P. KadewTraKuPong and R. Bowden,
Proc. 2nd European Workshp on Advanced Video-Based Surveillance Systems, 2001."
http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
*/
class CV_EXPORTS MOG_GPU
{
public:
//! the default constructor
MOG_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = 0.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
int history;
float varThreshold;
float backgroundRatio;
float noiseSigma;
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat sortKey_;
GpuMat mean_;
GpuMat var_;
};
/*!
The class implements the following algorithm:
"Improved adaptive Gausian mixture model for background subtraction"
Z.Zivkovic
International Conference Pattern Recognition, UK, August, 2004.
http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf
*/
class CV_EXPORTS MOG2_GPU
{
public:
//! the default constructor
MOG2_GPU(int nmixtures = -1);
//! re-initiaization method
void initialize(Size frameSize, int frameType);
//! the update operator
void operator()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! computes a background image which are the mean of all background gaussians
void getBackgroundImage(GpuMat& backgroundImage, Stream& stream = Stream::Null()) const;
//! releases all inner buffers
void release();
// parameters
// you should call initialize after parameters changes
int history;
//! here it is the maximum allowed number of mixture components.
//! Actual number is determined dynamically per pixel
float varThreshold;
// threshold on the squared Mahalanobis distance to decide if it is well described
// by the background model or not. Related to Cthr from the paper.
// This does not influence the update of the background. A typical value could be 4 sigma
// and that is varThreshold=4*4=16; Corresponds to Tb in the paper.
/////////////////////////
// less important parameters - things you might change but be carefull
////////////////////////
float backgroundRatio;
// corresponds to fTB=1-cf from the paper
// TB - threshold when the component becomes significant enough to be included into
// the background model. It is the TB=1-cf from the paper. So I use cf=0.1 => TB=0.
// For alpha=0.001 it means that the mode should exist for approximately 105 frames before
// it is considered foreground
// float noiseSigma;
float varThresholdGen;
//correspondts to Tg - threshold on the squared Mahalan. dist. to decide
//when a sample is close to the existing components. If it is not close
//to any a new component will be generated. I use 3 sigma => Tg=3*3=9.
//Smaller Tg leads to more generated components and higher Tg might make
//lead to small number of components but they can grow too large
float fVarInit;
float fVarMin;
float fVarMax;
//initial variance for the newly generated components.
//It will will influence the speed of adaptation. A good guess should be made.
//A simple way is to estimate the typical standard deviation from the images.
//I used here 10 as a reasonable value
// min and max can be used to further control the variance
float fCT; //CT - complexity reduction prior
//this is related to the number of samples needed to accept that a component
//actually exists. We use CT=0.05 of all the samples. By setting CT=0 you get
//the standard Stauffer&Grimson algorithm (maybe not exact but very similar)
//shadow detection parameters
bool bShadowDetection; //default 1 - do shadow detection
unsigned char nShadowDetection; //do shadow detection - insert this value as the detection result - 127 default value
float fTau;
// Tau - shadow threshold. The shadow is detected if the pixel is darker
//version of the background. Tau is a threshold on how much darker the shadow can be.
//Tau= 0.5 means that if pixel is more than 2 times darker then it is not shadow
//See: Prati,Mikic,Trivedi,Cucchiarra,"Detecting Moving Shadows...",IEEE PAMI,2003.
private:
int nmixtures_;
Size frameSize_;
int frameType_;
int nframes_;
GpuMat weight_;
GpuMat variance_;
GpuMat mean_;
GpuMat bgmodelUsedModes_; //keep track of number of modes per pixel
};
/**
* Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
* images of the same size, where 255 indicates Foreground and 0 represents Background.
* 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.
*/
class CV_EXPORTS GMG_GPU
{
public:
GMG_GPU();
/**
* Validate parameters and set up data structures for appropriate frame size.
* @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, float min = 0.0f, float max = 255.0f);
/**
* Performs single-frame background subtraction and builds up a statistical background image
* model.
* @param frame Input frame
* @param fgmask Output mask image representing foreground and background pixels
* @param stream Stream for the asynchronous version
*/
void operator ()(const GpuMat& frame, GpuMat& fgmask, float learningRate = -1.0f, Stream& stream = Stream::Null());
//! Releases all inner buffers
void release();
//! 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.
float 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.
float backgroundPrior;
//! Value above which pixel is determined to be FG.
float decisionThreshold;
//! Smoothing radius, in pixels, for cleaning up FG image.
int smoothingRadius;
//! Perform background model update.
bool updateBackgroundModel;
private:
float maxVal_, minVal_;
Size frameSize_;
int frameNum_;
GpuMat nfeatures_;
GpuMat colors_;
GpuMat weights_;
Ptr<FilterEngine_GPU> boxFilter_;
GpuMat buf_;
};
}} // namespace cv { namespace gpu {
#endif /* __OPENCV_GPUBGSEGM_HPP__ */