318 lines
13 KiB
C++
318 lines
13 KiB
C++
/*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) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., 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*/
|
|
|
|
#ifndef OPENCV_BACKGROUND_SEGM_HPP
|
|
#define OPENCV_BACKGROUND_SEGM_HPP
|
|
|
|
#include "opencv2/core.hpp"
|
|
|
|
namespace cv
|
|
{
|
|
|
|
//! @addtogroup video_motion
|
|
//! @{
|
|
|
|
/** @brief Base class for background/foreground segmentation. :
|
|
|
|
The class is only used to define the common interface for the whole family of background/foreground
|
|
segmentation algorithms.
|
|
*/
|
|
class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
|
|
{
|
|
public:
|
|
/** @brief Computes a foreground mask.
|
|
|
|
@param image Next video frame.
|
|
@param fgmask The output foreground mask as an 8-bit binary image.
|
|
@param learningRate The value between 0 and 1 that indicates how fast the background model is
|
|
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
|
|
rate. 0 means that the background model is not updated at all, 1 means that the background model
|
|
is completely reinitialized from the last frame.
|
|
*/
|
|
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
|
|
|
|
/** @brief Computes a background image.
|
|
|
|
@param backgroundImage The output background image.
|
|
|
|
@note Sometimes the background image can be very blurry, as it contain the average background
|
|
statistics.
|
|
*/
|
|
CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
|
|
};
|
|
|
|
|
|
/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
|
|
|
|
The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
|
|
and @cite Zivkovic2006 .
|
|
*/
|
|
class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor
|
|
{
|
|
public:
|
|
/** @brief Returns the number of last frames that affect the background model
|
|
*/
|
|
CV_WRAP virtual int getHistory() const = 0;
|
|
/** @brief Sets the number of last frames that affect the background model
|
|
*/
|
|
CV_WRAP virtual void setHistory(int history) = 0;
|
|
|
|
/** @brief Returns the number of gaussian components in the background model
|
|
*/
|
|
CV_WRAP virtual int getNMixtures() const = 0;
|
|
/** @brief Sets the number of gaussian components in the background model.
|
|
|
|
The model needs to be reinitalized to reserve memory.
|
|
*/
|
|
CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
|
|
|
|
/** @brief Returns the "background ratio" parameter of the algorithm
|
|
|
|
If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
|
|
considered background and added to the model as a center of a new component. It corresponds to TB
|
|
parameter in the paper.
|
|
*/
|
|
CV_WRAP virtual double getBackgroundRatio() const = 0;
|
|
/** @brief Sets the "background ratio" parameter of the algorithm
|
|
*/
|
|
CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
|
|
|
|
/** @brief Returns the variance threshold for the pixel-model match
|
|
|
|
The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
|
|
the background model or not. Related to Cthr from the paper.
|
|
*/
|
|
CV_WRAP virtual double getVarThreshold() const = 0;
|
|
/** @brief Sets the variance threshold for the pixel-model match
|
|
*/
|
|
CV_WRAP virtual void setVarThreshold(double varThreshold) = 0;
|
|
|
|
/** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation
|
|
|
|
Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
|
|
existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
|
|
is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
|
|
value generates more components. A higher Tg value may result in a small number of components but
|
|
they can grow too large.
|
|
*/
|
|
CV_WRAP virtual double getVarThresholdGen() const = 0;
|
|
/** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation
|
|
*/
|
|
CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0;
|
|
|
|
/** @brief Returns the initial variance of each gaussian component
|
|
*/
|
|
CV_WRAP virtual double getVarInit() const = 0;
|
|
/** @brief Sets the initial variance of each gaussian component
|
|
*/
|
|
CV_WRAP virtual void setVarInit(double varInit) = 0;
|
|
|
|
CV_WRAP virtual double getVarMin() const = 0;
|
|
CV_WRAP virtual void setVarMin(double varMin) = 0;
|
|
|
|
CV_WRAP virtual double getVarMax() const = 0;
|
|
CV_WRAP virtual void setVarMax(double varMax) = 0;
|
|
|
|
/** @brief Returns the complexity reduction threshold
|
|
|
|
This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
|
|
is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
|
|
standard Stauffer&Grimson algorithm.
|
|
*/
|
|
CV_WRAP virtual double getComplexityReductionThreshold() const = 0;
|
|
/** @brief Sets the complexity reduction threshold
|
|
*/
|
|
CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0;
|
|
|
|
/** @brief Returns the shadow detection flag
|
|
|
|
If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
|
|
details.
|
|
*/
|
|
CV_WRAP virtual bool getDetectShadows() const = 0;
|
|
/** @brief Enables or disables shadow detection
|
|
*/
|
|
CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
|
|
|
|
/** @brief Returns the shadow value
|
|
|
|
Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
|
|
in the mask always means background, 255 means foreground.
|
|
*/
|
|
CV_WRAP virtual int getShadowValue() const = 0;
|
|
/** @brief Sets the shadow value
|
|
*/
|
|
CV_WRAP virtual void setShadowValue(int value) = 0;
|
|
|
|
/** @brief Returns the shadow threshold
|
|
|
|
A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
|
|
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
|
|
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
|
|
*Detecting Moving Shadows...*, IEEE PAMI,2003.
|
|
*/
|
|
CV_WRAP virtual double getShadowThreshold() const = 0;
|
|
/** @brief Sets the shadow threshold
|
|
*/
|
|
CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
|
|
|
|
/** @brief Computes a foreground mask.
|
|
|
|
@param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$.
|
|
@param fgmask The output foreground mask as an 8-bit binary image.
|
|
@param learningRate The value between 0 and 1 that indicates how fast the background model is
|
|
learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
|
|
rate. 0 means that the background model is not updated at all, 1 means that the background model
|
|
is completely reinitialized from the last frame.
|
|
*/
|
|
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 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_W Ptr<BackgroundSubtractorMOG2>
|
|
createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
|
|
bool detectShadows=true);
|
|
|
|
/** @brief K-nearest neighbours - based Background/Foreground Segmentation Algorithm.
|
|
|
|
The class implements the K-nearest neighbours background subtraction described in @cite Zivkovic2006 .
|
|
Very efficient if number of foreground pixels is low.
|
|
*/
|
|
class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
|
|
{
|
|
public:
|
|
/** @brief Returns the number of last frames that affect the background model
|
|
*/
|
|
CV_WRAP virtual int getHistory() const = 0;
|
|
/** @brief Sets the number of last frames that affect the background model
|
|
*/
|
|
CV_WRAP virtual void setHistory(int history) = 0;
|
|
|
|
/** @brief Returns the number of data samples in the background model
|
|
*/
|
|
CV_WRAP virtual int getNSamples() const = 0;
|
|
/** @brief Sets the number of data samples in the background model.
|
|
|
|
The model needs to be reinitalized to reserve memory.
|
|
*/
|
|
CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
|
|
|
|
/** @brief Returns the threshold on the squared distance between the pixel and the sample
|
|
|
|
The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
|
|
close to a data sample.
|
|
*/
|
|
CV_WRAP virtual double getDist2Threshold() const = 0;
|
|
/** @brief Sets the threshold on the squared distance
|
|
*/
|
|
CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
|
|
|
|
/** @brief Returns the number of neighbours, the k in the kNN.
|
|
|
|
K is the number of samples that need to be within dist2Threshold in order to decide that that
|
|
pixel is matching the kNN background model.
|
|
*/
|
|
CV_WRAP virtual int getkNNSamples() const = 0;
|
|
/** @brief Sets the k in the kNN. How many nearest neighbours need to match.
|
|
*/
|
|
CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
|
|
|
|
/** @brief Returns the shadow detection flag
|
|
|
|
If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
|
|
details.
|
|
*/
|
|
CV_WRAP virtual bool getDetectShadows() const = 0;
|
|
/** @brief Enables or disables shadow detection
|
|
*/
|
|
CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
|
|
|
|
/** @brief Returns the shadow value
|
|
|
|
Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
|
|
in the mask always means background, 255 means foreground.
|
|
*/
|
|
CV_WRAP virtual int getShadowValue() const = 0;
|
|
/** @brief Sets the shadow value
|
|
*/
|
|
CV_WRAP virtual void setShadowValue(int value) = 0;
|
|
|
|
/** @brief Returns the shadow threshold
|
|
|
|
A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
|
|
the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
|
|
is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiara,
|
|
*Detecting Moving Shadows...*, IEEE PAMI,2003.
|
|
*/
|
|
CV_WRAP virtual double getShadowThreshold() const = 0;
|
|
/** @brief Sets the shadow threshold
|
|
*/
|
|
CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
|
|
};
|
|
|
|
/** @brief Creates KNN Background Subtractor
|
|
|
|
@param history Length of the history.
|
|
@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
|
|
whether a pixel is close to that sample. 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_W Ptr<BackgroundSubtractorKNN>
|
|
createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
|
|
bool detectShadows=true);
|
|
|
|
//! @} video_motion
|
|
|
|
} // cv
|
|
|
|
#endif
|