New KNN code -should compile now

modules/video/doc/motion_analysis_and_object_tracking.rst

@@ -596,7 +596,7 @@ Returns the number of gaussian components in the background model
 
 BackgroundSubtractorMOG2::setNMixtures
 --------------------------------------
-Sets the number of gaussian components in the background model
+Sets the number of gaussian components in the background model. The model needs to be reinitalized to reserve memory.
 
 .. ocv:function:: void BackgroundSubtractorMOG2::setNMixtures(int nmixtures)
 
@@ -615,9 +615,23 @@ Sets the "background ratio" parameter of the algorithm
 
 .. ocv:function:: void BackgroundSubtractorMOG2::setBackgroundRatio(double ratio)
 
+BackgroundSubtractorMOG2::getVarThreshold
+---------------------------------------------
+Returns the variance threshold for the pixel-model match
+
+.. ocv:function:: double BackgroundSubtractorMOG2::getVarThreshold() const
+
+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.
+
+BackgroundSubtractorMOG2::setVarThreshold
+---------------------------------------------
+Sets the variance threshold for the pixel-model match
+
+.. ocv:function:: void BackgroundSubtractorMOG2::setVarThreshold(double varThreshold)
+
 BackgroundSubtractorMOG2::getVarThresholdGen
 ---------------------------------------------
-Returns the variance scale factor for the pixel-model match
+Returns the variance threshold for the pixel-model match used for new mixture component generation
 
 .. ocv:function:: double BackgroundSubtractorMOG2::getVarThresholdGen() const
 
@@ -625,7 +639,7 @@ Threshold for the squared Mahalanobis distance that helps decide when a sample i
 
 BackgroundSubtractorMOG2::setVarThresholdGen
 ---------------------------------------------
-Sets the variance scale factor for the pixel-model match
+Sets the variance threshold for the pixel-model match used for new mixture component generation
 
 .. ocv:function:: void BackgroundSubtractorMOG2::setVarThresholdGen(double varThresholdGen)
 
@@ -700,6 +714,126 @@ Sets the shadow threshold
 .. ocv:function:: void BackgroundSubtractorMOG2::setShadowThreshold(double threshold)
 
 
+BackgroundSubtractorKNN
+------------------------
+K-nearest neigbours - based Background/Foreground Segmentation Algorithm.
+
+.. ocv:class:: BackgroundSubtractorKNN : public BackgroundSubtractor
+
+The class implements the K-nearest neigbours background subtraction described in [Zivkovic2006]_ . Very efficient if number of foreground pixels is low.
+
+
+createBackgroundSubtractorKNN
+--------------------------------------------------
+Creates KNN Background Subtractor
+
+.. ocv:function:: Ptr<BackgroundSubtractorKNN> createBackgroundSubtractorKNN( int history=500, double dist2Threshold=400.0, bool detectShadows=true )
+
+  :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.
+
+
+BackgroundSubtractorKNN::getHistory
+--------------------------------------
+Returns the number of last frames that affect the background model
+
+.. ocv:function:: int BackgroundSubtractorKNN::getHistory() const
+
+
+BackgroundSubtractorKNN::setHistory
+--------------------------------------
+Sets the number of last frames that affect the background model
+
+.. ocv:function:: void BackgroundSubtractorKNN::setHistory(int history)
+
+
+BackgroundSubtractorKNN::getNSamples
+--------------------------------------
+Returns the number of data samples in the background model
+
+.. ocv:function:: int BackgroundSubtractorKNN::getNSamples() const
+
+
+BackgroundSubtractorKNN::setNSamples
+--------------------------------------
+Sets the number of data samples in the background model. The model needs to be reinitalized to reserve memory.
+
+.. ocv:function:: void BackgroundSubtractorKNN::setNSamples(int nN)
+
+
+BackgroundSubtractorKNN::getDist2Threshold
+---------------------------------------------
+Returns the threshold on the squared distance between the pixel and the sample
+
+.. ocv:function:: double BackgroundSubtractorKNN::getDist2Threshold() const
+
+The threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to a data sample.
+
+BackgroundSubtractorKNN::setDist2Threshold
+---------------------------------------------
+Sets the threshold on the squared distance
+
+.. ocv:function:: void BackgroundSubtractorKNN::setDist2Threshold(double dist2Threshold)
+
+BackgroundSubtractorKNN::getkNNSamples
+---------------------------------------------
+Returns 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.
+
+.. ocv:function:: int BackgroundSubtractorKNN::getkNNSamples() const
+
+BackgroundSubtractorKNN::setkNNSamples
+---------------------------------------------
+Sets the k in the kNN. How many nearest neigbours need to match.
+
+.. ocv:function:: void BackgroundSubtractorKNN::setkNNSamples(int nKNN)
+
+
+BackgroundSubtractorKNN::getDetectShadows
+---------------------------------------------
+Returns the shadow detection flag
+
+.. ocv:function:: bool BackgroundSubtractorKNN::getDetectShadows() const
+
+If true, the algorithm detects shadows and marks them. See createBackgroundSubtractorKNN for details.
+
+BackgroundSubtractorKNN::setDetectShadows
+---------------------------------------------
+Enables or disables shadow detection
+
+.. ocv:function:: void BackgroundSubtractorKNN::setDetectShadows(bool detectShadows)
+
+BackgroundSubtractorKNN::getShadowValue
+---------------------------------------------
+Returns the shadow value
+
+.. ocv:function:: int BackgroundSubtractorKNN::getShadowValue() const
+
+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.
+
+BackgroundSubtractorKNN::setShadowValue
+---------------------------------------------
+Sets the shadow value
+
+.. ocv:function:: void BackgroundSubtractorKNN::setShadowValue(int value)
+
+BackgroundSubtractorKNN::getShadowThreshold
+---------------------------------------------
+Returns the shadow threshold
+
+.. ocv:function:: double BackgroundSubtractorKNN::getShadowThreshold() const
+
+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 Cucchiarra, *Detecting Moving Shadows...*, IEEE PAMI,2003.
+
+BackgroundSubtractorKNN::setShadowThreshold
+---------------------------------------------
+Sets the shadow threshold
+
+.. ocv:function:: void BackgroundSubtractorKNN::setShadowThreshold(double threshold)
+
+
 BackgroundSubtractorGMG
 ------------------------
 Background Subtractor module based on the algorithm given in [Gold2012]_.

modules/video/include/opencv2/video/background_segm.hpp

@@ -113,7 +113,7 @@ public:
     CV_WRAP virtual void setHistory(int history) = 0;
 
     CV_WRAP virtual int getNMixtures() const = 0;
-    CV_WRAP virtual void setNMixtures(int nmixtures) = 0;
+    CV_WRAP virtual void setNMixtures(int nmixtures) = 0;//needs reinitialization!
 
     CV_WRAP virtual double getBackgroundRatio() const = 0;
     CV_WRAP virtual void setBackgroundRatio(double ratio) = 0;
@@ -150,6 +150,45 @@ CV_EXPORTS_W Ptr<BackgroundSubtractorMOG2>
     createBackgroundSubtractorMOG2(int history=500, double varThreshold=16,
                                    bool detectShadows=true);
 
+/*!
+ The class implements the K nearest neigbours algorithm from:
+ "Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction"
+ Z.Zivkovic, F. van der Heijden
+ Pattern Recognition Letters, vol. 27, no. 7, pages 773-780, 2006
+ http://www.zoranz.net/Publications/zivkovicPRL2006.pdf
+
+ Fast for small foreground object. Results on the benchmark data is at http://www.changedetection.net.
+*/
+
+class CV_EXPORTS_W BackgroundSubtractorKNN : public BackgroundSubtractor
+{
+public:
+    CV_WRAP virtual int getHistory() const = 0;
+    CV_WRAP virtual void setHistory(int history) = 0;
+
+    CV_WRAP virtual int getNSamples() const = 0;
+    CV_WRAP virtual void setNSamples(int _nN) = 0;//needs reinitialization!
+
+    CV_WRAP virtual double getDist2Threshold() const = 0;
+    CV_WRAP virtual void setDist2Threshold(double _dist2Threshold) = 0;
+
+    CV_WRAP virtual int getkNNSamples() const = 0;
+    CV_WRAP virtual void setkNNSamples(int _nkNN) = 0;
+
+    CV_WRAP virtual bool getDetectShadows() const = 0;
+    CV_WRAP virtual void setDetectShadows(bool detectShadows) = 0;
+
+    CV_WRAP virtual int getShadowValue() const = 0;
+    CV_WRAP virtual void setShadowValue(int value) = 0;
+
+    CV_WRAP virtual double getShadowThreshold() const = 0;
+    CV_WRAP virtual void setShadowThreshold(double threshold) = 0;
+};
+
+CV_EXPORTS_W Ptr<BackgroundSubtractorKNN>
+    createBackgroundSubtractorKNN(int history=500, double dist2Threshold=400.0,
+                                   bool detectShadows=true);
+
 /**
  * Background Subtractor module. Takes a series of images and returns a sequence of mask (8UC1)
  * images of the same size, where 255 indicates Foreground and 0 represents Background.

samples/cpp/bgfg_segm.cpp

@@ -52,7 +52,7 @@ int main(int argc, const char** argv)
     namedWindow("foreground image", WINDOW_NORMAL);
     namedWindow("mean background image", WINDOW_NORMAL);
 
-    Ptr<BackgroundSubtractor> bg_model = createBackgroundSubtractorMOG2();
+    Ptr<BackgroundSubtractor> bg_model = createBackgroundSubtractorKNN();
 
     Mat img, fgmask, fgimg;