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307 lines
13 KiB
307 lines
13 KiB
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef __OPENCV_BACKGROUND_SEGM_HPP__
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#define __OPENCV_BACKGROUND_SEGM_HPP__
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#include "opencv2/core.hpp"
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namespace cv
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{
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//! @addtogroup video_motion
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//! @{
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/** @brief Base class for background/foreground segmentation. :
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The class is only used to define the common interface for the whole family of background/foreground
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segmentation algorithms.
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*/
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class CV_EXPORTS_W BackgroundSubtractor : public Algorithm
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{
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public:
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/** @brief Computes a foreground mask.
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@param image Next video frame.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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*/
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CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) = 0;
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/** @brief Computes a background image.
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@param backgroundImage The output background image.
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@note Sometimes the background image can be very blurry, as it contain the average background
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statistics.
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*/
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CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const = 0;
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};
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/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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The class implements the Gaussian mixture model background subtraction described in @cite Zivkovic2004
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and @cite Zivkovic2006 .
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*/
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class CV_EXPORTS_W BackgroundSubtractorMOG2 : public BackgroundSubtractor
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{
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public:
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/** @brief Returns the number of last frames that affect the background model
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*/
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CV_WRAP virtual int getHistory() const = 0;
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/** @brief Sets the number of last frames that affect the background model
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*/
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CV_WRAP virtual void setHistory(int history) = 0;
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/** @brief Returns the number of gaussian components in the background model
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*/
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CV_WRAP virtual int getNMixtures() const = 0;
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/** @brief Sets the number of gaussian components in the background model.
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The model needs to be reinitalized to reserve memory.
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*/
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CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
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/** @brief Returns the "background ratio" parameter of the algorithm
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If a foreground pixel keeps semi-constant value for about backgroundRatio\*history frames, it's
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considered background and added to the model as a center of a new component. It corresponds to TB
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parameter in the paper.
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*/
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CV_WRAP virtual double getBackgroundRatio() const = 0;
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/** @brief Sets the "background ratio" parameter of the algorithm
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*/
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CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
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/** @brief Returns the variance threshold for the pixel-model match
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The main threshold on the squared Mahalanobis distance to decide if the sample is well described by
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the background model or not. Related to Cthr from the paper.
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*/
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CV_WRAP virtual double getVarThreshold() const = 0;
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/** @brief Sets the variance threshold for the pixel-model match
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*/
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CV_WRAP virtual void setVarThreshold(double varThreshold) = 0;
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/** @brief Returns the variance threshold for the pixel-model match used for new mixture component generation
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Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the
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existing components (corresponds to Tg in the paper). If a pixel is not close to any component, it
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is considered foreground or added as a new component. 3 sigma =\> Tg=3\*3=9 is default. A smaller Tg
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value generates more components. A higher Tg value may result in a small number of components but
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they can grow too large.
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*/
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CV_WRAP virtual double getVarThresholdGen() const = 0;
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/** @brief Sets the variance threshold for the pixel-model match used for new mixture component generation
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*/
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CV_WRAP virtual void setVarThresholdGen(double varThresholdGen) = 0;
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/** @brief Returns the initial variance of each gaussian component
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*/
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CV_WRAP virtual double getVarInit() const = 0;
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/** @brief Sets the initial variance of each gaussian component
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*/
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CV_WRAP virtual void setVarInit(double varInit) = 0;
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CV_WRAP virtual double getVarMin() const = 0;
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CV_WRAP virtual void setVarMin(double varMin) = 0;
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CV_WRAP virtual double getVarMax() const = 0;
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CV_WRAP virtual void setVarMax(double varMax) = 0;
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/** @brief Returns the complexity reduction threshold
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This parameter defines the number of samples needed to accept to prove the component exists. CT=0.05
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is a default value for all the samples. By setting CT=0 you get an algorithm very similar to the
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standard Stauffer&Grimson algorithm.
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*/
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CV_WRAP virtual double getComplexityReductionThreshold() const = 0;
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/** @brief Sets the complexity reduction threshold
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*/
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CV_WRAP virtual void setComplexityReductionThreshold(double ct) = 0;
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/** @brief Returns the shadow detection flag
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If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for
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details.
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*/
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CV_WRAP virtual bool getDetectShadows() const = 0;
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/** @brief Enables or disables shadow detection
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*/
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CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
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/** @brief Returns the shadow value
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Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
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in the mask always means background, 255 means foreground.
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*/
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CV_WRAP virtual int getShadowValue() const = 0;
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/** @brief Sets the shadow value
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*/
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CV_WRAP virtual void setShadowValue(int value) = 0;
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/** @brief Returns the shadow threshold
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A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
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the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
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is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
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*Detecting Moving Shadows...*, IEEE PAMI,2003.
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*/
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CV_WRAP virtual double getShadowThreshold() const = 0;
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/** @brief Sets the shadow threshold
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*/
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CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
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};
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/** @brief Creates MOG2 Background Subtractor
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@param history Length of the history.
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@param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
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to decide whether a pixel is well described by the background model. This parameter does not
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affect the background update.
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@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
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speed a bit, so if you do not need this feature, set the parameter to false.
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*/
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CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2>
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createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
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bool detectShadows=true);
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/** @brief K-nearest neigbours - based Background/Foreground Segmentation Algorithm.
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The class implements the K-nearest neigbours background subtraction described in @cite Zivkovic2006 .
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Very efficient if number of foreground pixels is low.
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*/
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class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
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{
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public:
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/** @brief Returns the number of last frames that affect the background model
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*/
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CV_WRAP virtual int getHistory() const = 0;
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/** @brief Sets the number of last frames that affect the background model
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*/
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CV_WRAP virtual void setHistory(int history) = 0;
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/** @brief Returns the number of data samples in the background model
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*/
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CV_WRAP virtual int getNSamples() const = 0;
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/** @brief Sets the number of data samples in the background model.
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The model needs to be reinitalized to reserve memory.
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*/
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CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
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/** @brief Returns the threshold on the squared distance between the pixel and the sample
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The threshold on the squared distance between the pixel and the sample to decide whether a pixel is
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close to a data sample.
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*/
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CV_WRAP virtual double getDist2Threshold() const = 0;
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/** @brief Sets the threshold on the squared distance
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*/
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CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
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/** @brief Returns the number of neighbours, the k in the kNN.
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K is the number of samples that need to be within dist2Threshold in order to decide that that
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pixel is matching the kNN background model.
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*/
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CV_WRAP virtual int getkNNSamples() const = 0;
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/** @brief Sets the k in the kNN. How many nearest neigbours need to match.
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*/
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CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
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/** @brief Returns the shadow detection flag
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If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for
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details.
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*/
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CV_WRAP virtual bool getDetectShadows() const = 0;
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/** @brief Enables or disables shadow detection
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*/
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CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
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/** @brief Returns the shadow value
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Shadow value is the value used to mark shadows in the foreground mask. Default value is 127. Value 0
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in the mask always means background, 255 means foreground.
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*/
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CV_WRAP virtual int getShadowValue() const = 0;
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/** @brief Sets the shadow value
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*/
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CV_WRAP virtual void setShadowValue(int value) = 0;
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/** @brief Returns the shadow threshold
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A shadow is detected if pixel is a darker version of the background. The shadow threshold (Tau in
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the paper) is a threshold defining how much darker the shadow can be. Tau= 0.5 means that if a pixel
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is more than twice darker then it is not shadow. See Prati, Mikic, Trivedi and Cucchiarra,
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*Detecting Moving Shadows...*, IEEE PAMI,2003.
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*/
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CV_WRAP virtual double getShadowThreshold() const = 0;
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/** @brief Sets the shadow threshold
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*/
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CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
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};
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/** @brief Creates KNN Background Subtractor
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@param history Length of the history.
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@param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
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whether a pixel is close to that sample. This parameter does not affect the background update.
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@param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
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speed a bit, so if you do not need this feature, set the parameter to false.
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*/
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CV_EXPORTS_W Ptr<BackgroundSubtractorKNN>
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createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
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bool detectShadows=true);
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//! @} video_motion
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} // cv
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#endif
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