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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 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 createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true); //! @} video_motion } // namespace cv #endif