Finds the geometric transform (warp) between two images in terms of the ECC criterion [EP08]_.
.. ocv:function:: double findTransformECC( InputArray templateImage, InputArray inputImage, InputOutputArray warpMatrix, int motionType=MOTION_AFFINE, TermCriteria criteria=TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001))
-.. ocv:cfunction:: double cvFindTransformECC( const CvArr* templateImage, const CvArr* inputImage, CvMat* warpMatrix, const int motionType, const CvTermCriteria criteria)
:param templateImage: single-channel template image; ``CV_8U`` or ``CV_32F`` array.
:param warpMatrix: floating-point :math:`2\times 3` or :math:`3\times 3` mapping matrix (warp).
:param motionType: parameter, specifying the type of motion:
+
* **MOTION_TRANSLATION** sets a translational motion model; ``warpMatrix`` is :math:`2\times 3` with the first :math:`2\times 2` part being the unity matrix and the rest two parameters being estimated.
+
* **MOTION_EUCLIDEAN** sets a Euclidean (rigid) transformation as motion model; three parameters are estimated; ``warpMatrix`` is :math:`2\times 3`.
+
* **MOTION_AFFINE** sets an affine motion model (DEFAULT); six parameters are estimated; ``warpMatrix`` is :math:`2\times 3`.
+
* **MOTION_HOMOGRAPHY** sets a homography as a motion model; eight parameters are estimated;``warpMatrix`` is :math:`3\times 3`.
:param criteria: parameter, specifying the termination criteria of the ECC algorithm; ``criteria.epsilon`` defines the threshold of the increment in the correlation coefficient between two iterations (a negative ``criteria.epsilon`` makes ``criteria.maxcount`` the only termination criterion). Default values are shown in the declaration above.
The function estimates the optimum transformation (``warpMatrix``) with respect to ECC criterion ([EP08]_), that is
-..math::
+.. math::
\texttt{warpMatrix} = \texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))
where
-..math::
+.. math::
\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}
{
public:
virtual ~BackgroundSubtractor();
- virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=0);
+ virtual void apply(InputArray image, OutputArray fgmask, double learningRate=0);
virtual void getBackgroundImage(OutputArray backgroundImage) const;
};
The class is only used to define the common interface for the whole family of background/foreground segmentation algorithms.
-BackgroundSubtractor::operator()
+BackgroundSubtractor::apply
--------------------------------
Computes a foreground mask.
-.. ocv:function:: void BackgroundSubtractor::operator()(InputArray image, OutputArray fgmask, double learningRate=0)
+.. ocv:function:: void BackgroundSubtractor::apply(InputArray image, OutputArray fgmask, double learningRate=-1)
.. ocv:pyfunction:: cv2.BackgroundSubtractor.apply(image[, fgmask[, learningRate]]) -> fgmask
: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.
BackgroundSubtractor::getBackgroundImage
----------------------------------------
Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
-The class implements the algorithm described in P. KadewTraKuPong and R. Bowden, *An improved adaptive background mixture model for real-time tracking with shadow detection*, Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
-
-
+The class implements the algorithm described in [KB2001]_.
-BackgroundSubtractorMOG::BackgroundSubtractorMOG
+createBackgroundSubtractorMOG
------------------------------------------------
-The constructors.
-
-.. ocv:function:: BackgroundSubtractorMOG::BackgroundSubtractorMOG()
+Creates mixture-of-gaussian background subtractor
-.. ocv:function:: BackgroundSubtractorMOG::BackgroundSubtractorMOG(int history, int nmixtures, double backgroundRatio, double noiseSigma=0)
+.. ocv:function:: Ptr<BackgroundSubtractorMOG> createBackgroundSubtractorMOG(int history=200, int nmixtures=5, double backgroundRatio=0.7, double noiseSigma=0)
-.. ocv:pyfunction:: cv2.BackgroundSubtractorMOG([history, nmixtures, backgroundRatio[, noiseSigma]]) -> <BackgroundSubtractorMOG object>
+.. ocv:pyfunction:: cv2.createBackgroundSubtractorMOG([history, nmixtures, backgroundRatio, noiseSigma]) -> <BackgroundSubtractorMOG object>
:param history: Length of the history.
:param backgroundRatio: Background ratio.
- :param noiseSigma: Noise strength.
+ :param noiseSigma: Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.
-Default constructor sets all parameters to default values.
+BackgroundSubtractorMOG2
+------------------------
+Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
+.. ocv:class:: BackgroundSubtractorMOG2 : public BackgroundSubtractor
+The class implements the Gaussian mixture model background subtraction described in [Zivkovic2004]_ and [Zivkovic2006]_ .
-BackgroundSubtractorMOG::operator()
------------------------------------
-Updates the background model and returns the foreground mask
-.. ocv:function:: void BackgroundSubtractorMOG::operator()(InputArray image, OutputArray fgmask, double learningRate=0)
+createBackgroundSubtractorMOG2
+--------------------------------------------------
+Creates MOG2 Background Subtractor
-Parameters are the same as in :ocv:funcx:`BackgroundSubtractor::operator()`
+.. ocv:function:: Ptr<BackgroundSubtractorMOG2> createBackgroundSubtractorMOG2( int history=500, double varThreshold=16, bool detectShadows=true )
+ :param history: Length of the history.
-BackgroundSubtractorMOG2
-------------------------
-Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
+ :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.
-.. ocv:class:: BackgroundSubtractorMOG2 : public BackgroundSubtractor
+ :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.
- Here are important members of the class that control the algorithm, which you can set after constructing the class instance:
- .. ocv:member:: int nmixtures
+BackgroundSubtractorMOG2::getHistory
+--------------------------------------
+Returns the number of last frames that affect the background model
- Maximum allowed number of mixture components. Actual number is determined dynamically per pixel.
+.. ocv:function:: int BackgroundSubtractorMOG2::getHistory() const
- .. ocv:member:: float backgroundRatio
- Threshold defining whether the component is significant enough to be included into the background model ( corresponds to ``TB=1-cf`` from the paper??which paper??). ``cf=0.1 => TB=0.9`` is default. For ``alpha=0.001``, it means that the mode should exist for approximately 105 frames before it is considered foreground.
+BackgroundSubtractorMOG2::setHistory
+--------------------------------------
+Sets the number of last frames that affect the background model
- .. ocv:member:: float varThresholdGen
+.. ocv:function:: void BackgroundSubtractorMOG2::setHistory(int history)
- Threshold for the squared Mahalanobis distance that helps decide when a sample is close to the existing components (corresponds to ``Tg``). If it is not close to any component, a new component is generated. ``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.
- .. ocv:member:: float fVarInit
+BackgroundSubtractorMOG2::getNMixtures
+--------------------------------------
+Returns the number of gaussian components in the background model
- Initial variance for the newly generated components. It affects the speed of adaptation. The parameter value is based on your estimate of the typical standard deviation from the images. OpenCV uses 15 as a reasonable value.
+.. ocv:function:: int BackgroundSubtractorMOG2::getNMixtures() const
- .. ocv:member:: float fVarMin
- Parameter used to further control the variance.
+BackgroundSubtractorMOG2::setNMixtures
+--------------------------------------
+Sets the number of gaussian components in the background model
- .. ocv:member:: float fVarMax
+.. ocv:function:: void BackgroundSubtractorMOG2::setNMixtures(int nmixtures)
- Parameter used to further control the variance.
- .. ocv:member:: float fCT
+BackgroundSubtractorMOG2::getBackgroundRatio
+---------------------------------------------
+Returns the "background ratio" parameter of the algorithm
- Complexity reduction parameter. 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.
+.. ocv:function:: double BackgroundSubtractorMOG2::getBackgroundRatio() const
- .. ocv:member:: uchar nShadowDetection
+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.
- The value for marking shadow pixels in the output foreground mask. Default value is 127.
+BackgroundSubtractorMOG2::setBackgroundRatio
+---------------------------------------------
+Sets the "background ratio" parameter of the algorithm
- .. ocv:member:: float fTau
+.. ocv:function:: void BackgroundSubtractorMOG2::setBackgroundRatio(double ratio)
- Shadow threshold. The shadow is detected if the pixel is a darker version of the background. ``Tau`` 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,Cucchiarra, *Detecting Moving Shadows...*, IEEE PAMI,2003.
+BackgroundSubtractorMOG2::getVarThresholdGen
+---------------------------------------------
+Returns the variance scale factor for the pixel-model match
+.. ocv:function:: double BackgroundSubtractorMOG2::getVarThresholdGen() const
-The class implements the Gaussian mixture model background subtraction described in:
+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.
- * Z.Zivkovic, *Improved adaptive Gausian mixture model for background subtraction*, International Conference Pattern Recognition, UK, August, 2004, http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf. The code is very fast and performs also shadow detection. Number of Gausssian components is adapted per pixel.
+BackgroundSubtractorMOG2::setVarThresholdGen
+---------------------------------------------
+Sets the variance scale factor for the pixel-model match
- * Z.Zivkovic, F. van der Heijden, *Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction*, Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006. The algorithm similar to the standard Stauffer&Grimson algorithm with additional selection of the number of the Gaussian components based on: Z.Zivkovic, F.van der Heijden, Recursive unsupervised learning of finite mixture models, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.26, no.5, pages 651-656, 2004.
+.. ocv:function:: void BackgroundSubtractorMOG2::setVarThresholdGen(double varThresholdGen)
+BackgroundSubtractorMOG2::getVarInit
+---------------------------------------------
+Returns the initial variance of each gaussian component
-BackgroundSubtractorMOG2::BackgroundSubtractorMOG2
---------------------------------------------------
-The constructors.
+.. ocv:function:: double BackgroundSubtractorMOG2::getVarInit() const
-.. ocv:function:: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2()
+BackgroundSubtractorMOG2::setVarInit
+---------------------------------------------
+Sets the initial variance of each gaussian component
-.. ocv:function:: BackgroundSubtractorMOG2::BackgroundSubtractorMOG2( int history, float varThreshold, bool bShadowDetection=true )
+.. ocv:function:: void BackgroundSubtractorMOG2::setVarInit(double varInit)
- :param history: Length of the history.
- :param varThreshold: Threshold on the squared Mahalanobis distance to decide whether it is well described by the background model (see Cthr??). This parameter does not affect the background update. A typical value could be 4 sigma, that is, ``varThreshold=4*4=16;`` (see Tb??).
+BackgroundSubtractorMOG2::getComplexityReductionThreshold
+----------------------------------------------------------
+Returns the complexity reduction threshold
+
+.. ocv:function:: double BackgroundSubtractorMOG2::getComplexityReductionThreshold() const
+
+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.
+
+BackgroundSubtractorMOG2::setComplexityReductionThreshold
+----------------------------------------------------------
+Sets the complexity reduction threshold
+
+.. ocv:function:: void BackgroundSubtractorMOG2::setComplexityReductionThreshold(double ct)
+
+
+BackgroundSubtractorMOG2::getDetectShadows
+---------------------------------------------
+Returns the shadow detection flag
+
+.. ocv:function:: bool BackgroundSubtractorMOG2::getDetectShadows() const
+
+If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorMOG2 for details.
- :param bShadowDetection: Parameter defining whether shadow detection should be enabled (``true`` or ``false``).
+BackgroundSubtractorMOG2::setDetectShadows
+---------------------------------------------
+Enables or disables shadow detection
+.. ocv:function:: void BackgroundSubtractorMOG2::setDetectShadows(bool detectShadows)
+BackgroundSubtractorMOG2::getShadowValue
+---------------------------------------------
+Returns the shadow value
-BackgroundSubtractorMOG2::operator()
-------------------------------------
-Updates the background model and computes the foreground mask
+.. ocv:function:: int BackgroundSubtractorMOG2::getShadowValue() const
-.. ocv:function:: void BackgroundSubtractorMOG2::operator()(InputArray image, OutputArray fgmask, double learningRate=-1)
+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.
- See :ocv:funcx:`BackgroundSubtractor::operator()`.
+BackgroundSubtractorMOG2::setShadowValue
+---------------------------------------------
+Sets the shadow value
+.. ocv:function:: void BackgroundSubtractorMOG2::setShadowValue(int shadowValue)
-BackgroundSubtractorMOG2::getBackgroundImage
---------------------------------------------
-Returns background image
+BackgroundSubtractorMOG2::getShadowThreshold
+---------------------------------------------
+Returns the shadow threshold
-.. ocv:function:: void BackgroundSubtractorMOG2::getBackgroundImage(OutputArray backgroundImage)
+.. ocv:function:: int BackgroundSubtractorMOG2::getShadowThreshold() const
-See :ocv:func:`BackgroundSubtractor::getBackgroundImage`.
+A shadow is detected if the 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 Cucchiarra, *Detecting Moving Shadows...*, IEEE PAMI,2003.
+
+BackgroundSubtractorMOG2::setShadowThreshold
+---------------------------------------------
+Sets the shadow threshold
+
+.. ocv:function:: void BackgroundSubtractorMOG2::setShadowThreshold(int shadowValue)
calcOpticalFlowSF
.. [Davis97] Davis, J.W. and Bobick, A.F. “The Representation and Recognition of Action Using Temporal Templates”, CVPR97, 1997
+.. [EP08] Evangelidis, G.D. and Psarakis E.Z. "Parametric Image Alignment using Enhanced Correlation Coefficient Maximization", IEEE Transactions on PAMI, vol. 32, no. 10, 2008
+
.. [Farneback2003] Gunnar Farneback, Two-frame motion estimation based on polynomial expansion, Lecture Notes in Computer Science, 2003, (2749), , 363-370.
.. [Horn81] Berthold K.P. Horn and Brian G. Schunck. Determining Optical Flow. Artificial Intelligence, 17, pp. 185-203, 1981.
+.. [KB2001] P. KadewTraKuPong and R. Bowden. "An improved adaptive background mixture model for real-time tracking with shadow detection", Proc. 2nd European Workshop on Advanced Video-Based Surveillance Systems, 2001: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/avbs01/avbs01.pdf
+
+.. [Javier2012] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
+
.. [Lucas81] Lucas, B., and Kanade, T. An Iterative Image Registration Technique with an Application to Stereo Vision, Proc. of 7th International Joint Conference on Artificial Intelligence (IJCAI), pp. 674-679.
.. [Welch95] Greg Welch and Gary Bishop “An Introduction to the Kalman Filter”, 1995
.. [Zach2007] C. Zach, T. Pock and H. Bischof. "A Duality Based Approach for Realtime TV-L1 Optical Flow", In Proceedings of Pattern Recognition (DAGM), Heidelberg, Germany, pp. 214-223, 2007
-.. [Javier2012] Javier Sanchez, Enric Meinhardt-Llopis and Gabriele Facciolo. "TV-L1 Optical Flow Estimation".
+.. [Zivkovic2004] Z. Zivkovic. Improved adaptive Gausian mixture model for background subtraction*, International Conference Pattern Recognition, UK, August, 2004, http://www.zoranz.net/Publications/zivkovic2004ICPR.pdf. The code is very fast and performs also shadow detection. Number of Gausssian components is adapted per pixel.
-.. [EP08] Evangelidis, G.D. and Psarakis E.Z. "Parametric Image Alignment using Enhanced Correlation Coefficient Maximization", IEEE Transactions on PAMI, vol. 32, no. 10, 2008
+.. [Zivkovic2006] Z.Zivkovic, F. van der Heijden. "Efficient Adaptive Density Estimapion per Image Pixel for the Task of Background Subtraction", Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006.
projects / profile / ivi / opencv.git / commitdiff