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#ifndef __OPENCV_CUDABGSEGM_HPP__
#define __OPENCV_CUDABGSEGM_HPP__
#ifndef __cplusplus
# error cudabgsegm.hpp header must be compiled as C++
#endif
#include "opencv2/core/cuda.hpp"
#include "opencv2/video/background_segm.hpp"
/**
@addtogroup cuda
@{
@defgroup cudabgsegm Background Segmentation
@}
*/
namespace cv { namespace cuda {
//! @addtogroup cudabgsegm
//! @{
////////////////////////////////////////////////////
// MOG
/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
The class discriminates between foreground and background pixels by building and maintaining a model
of the background. Any pixel which does not fit this model is then deemed to be foreground. The
class implements algorithm described in @cite MOG2001.
@sa BackgroundSubtractorMOG
@note
- An example on gaussian mixture based background/foreground segmantation can be found at
opencv_source_code/samples/gpu/bgfg_segm.cpp
*/
class CV_EXPORTS BackgroundSubtractorMOG : public cv::BackgroundSubtractor
{
public:
using cv::BackgroundSubtractor::apply;
virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
using cv::BackgroundSubtractor::getBackgroundImage;
virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
virtual int getHistory() const = 0;
virtual void setHistory(int nframes) = 0;
virtual int getNMixtures() const = 0;
virtual void setNMixtures(int nmix) = 0;
virtual double getBackgroundRatio() const = 0;
virtual void setBackgroundRatio(double backgroundRatio) = 0;
virtual double getNoiseSigma() const = 0;
virtual void setNoiseSigma(double noiseSigma) = 0;
};
/** @brief Creates mixture-of-gaussian background subtractor
@param history Length of the history.
@param nmixtures Number of Gaussian mixtures.
@param backgroundRatio Background ratio.
@param noiseSigma Noise strength (standard deviation of the brightness or each color channel). 0
means some automatic value.
*/
CV_EXPORTS Ptr<cuda::BackgroundSubtractorMOG>
createBackgroundSubtractorMOG(int history = 200, int nmixtures = 5,
double backgroundRatio = 0.7, double noiseSigma = 0);
////////////////////////////////////////////////////
// MOG2
/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
The class discriminates between foreground and background pixels by building and maintaining a model
of the background. Any pixel which does not fit this model is then deemed to be foreground. The
class implements algorithm described in @cite MOG2004.
@sa BackgroundSubtractorMOG2
*/
class CV_EXPORTS BackgroundSubtractorMOG2 : public cv::BackgroundSubtractorMOG2
{
public:
using cv::BackgroundSubtractorMOG2::apply;
using cv::BackgroundSubtractorMOG2::getBackgroundImage;
virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
virtual void getBackgroundImage(OutputArray backgroundImage, Stream& stream) const = 0;
};
/** @brief Creates MOG2 Background Subtractor
@param history Length of the history.
@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
to decide whether a pixel is well described by the background model. This parameter does not
affect the background update.
@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
speed a bit, so if you do not need this feature, set the parameter to false.
*/
CV_EXPORTS Ptr<cuda::BackgroundSubtractorMOG2>
createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16,
bool detectShadows = true);
////////////////////////////////////////////////////
// GMG
/** @brief Background/Foreground Segmentation Algorithm.
The class discriminates between foreground and background pixels by building and maintaining a model
of the background. Any pixel which does not fit this model is then deemed to be foreground. The
class implements algorithm described in @cite GMG2012.
*/
class CV_EXPORTS BackgroundSubtractorGMG : public cv::BackgroundSubtractor
{
public:
using cv::BackgroundSubtractor::apply;
virtual void apply(InputArray image, OutputArray fgmask, double learningRate, Stream& stream) = 0;
virtual int getMaxFeatures() const = 0;
virtual void setMaxFeatures(int maxFeatures) = 0;
virtual double getDefaultLearningRate() const = 0;
virtual void setDefaultLearningRate(double lr) = 0;
virtual int getNumFrames() const = 0;
virtual void setNumFrames(int nframes) = 0;
virtual int getQuantizationLevels() const = 0;
virtual void setQuantizationLevels(int nlevels) = 0;
virtual double getBackgroundPrior() const = 0;
virtual void setBackgroundPrior(double bgprior) = 0;
virtual int getSmoothingRadius() const = 0;
virtual void setSmoothingRadius(int radius) = 0;
virtual double getDecisionThreshold() const = 0;
virtual void setDecisionThreshold(double thresh) = 0;
virtual bool getUpdateBackgroundModel() const = 0;
virtual void setUpdateBackgroundModel(bool update) = 0;
virtual double getMinVal() const = 0;
virtual void setMinVal(double val) = 0;
virtual double getMaxVal() const = 0;
virtual void setMaxVal(double val) = 0;
};
/** @brief Creates GMG Background Subtractor
@param initializationFrames Number of frames of video to use to initialize histograms.
@param decisionThreshold Value above which pixel is determined to be FG.
*/
CV_EXPORTS Ptr<cuda::BackgroundSubtractorGMG>
createBackgroundSubtractorGMG(int initializationFrames = 120, double decisionThreshold = 0.8);
////////////////////////////////////////////////////
// FGD
/** @brief The class discriminates between foreground and background pixels by building and maintaining a model
of the background.
Any pixel which does not fit this model is then deemed to be foreground. The class implements
algorithm described in @cite FGD2003.
@sa BackgroundSubtractor
*/
class CV_EXPORTS BackgroundSubtractorFGD : public cv::BackgroundSubtractor
{
public:
/** @brief Returns the output foreground regions calculated by findContours.
@param foreground_regions Output array (CPU memory).
*/
virtual void getForegroundRegions(OutputArrayOfArrays foreground_regions) = 0;
};
struct CV_EXPORTS FGDParams
{
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
FGDParams();
};
/** @brief Creates FGD Background Subtractor
@param params Algorithm's parameters. See @cite FGD2003 for explanation.
*/
CV_EXPORTS Ptr<cuda::BackgroundSubtractorFGD>
createBackgroundSubtractorFGD(const FGDParams& params = FGDParams());
//! @}
}} // namespace cv { namespace cuda {
#endif /* __OPENCV_CUDABGSEGM_HPP__ */