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317 lines
13 KiB
317 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 Cucchiara, |
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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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/** @brief Computes a foreground mask. |
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@param image Next video frame. Floating point frame will be used without scaling and should be in range \f$[0,255]\f$. |
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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) CV_OVERRIDE = 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 neighbours - based Background/Foreground Segmentation Algorithm. |
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The class implements the K-nearest neighbours 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 neighbours 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 Cucchiara, |
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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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