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some fixes

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U-WBI\nlv20442 2014-01-30 14:50:35 +01:00
parent d950adc0d2
commit be7eb72051
2 changed files with 14 additions and 11 deletions
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12
modules/video/doc/motion_analysis_and_object_tracking.rst
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@ -761,7 +761,7 @@ BackgroundSubtractorKNN::setNSamples
-------------------------------------- --------------------------------------
Sets the number of data samples in the background model. The model needs to be reinitalized to reserve memory. 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) .. ocv:function:: void BackgroundSubtractorKNN::setNSamples(int _nN)
BackgroundSubtractorKNN::getDist2Threshold BackgroundSubtractorKNN::getDist2Threshold
@ -776,7 +776,7 @@ BackgroundSubtractorKNN::setDist2Threshold
--------------------------------------------- ---------------------------------------------
Sets the threshold on the squared distance Sets the threshold on the squared distance
.. ocv:function:: void BackgroundSubtractorKNN::setDist2Threshold(double dist2Threshold) .. ocv:function:: void BackgroundSubtractorKNN::setDist2Threshold(double _dist2Threshold)
BackgroundSubtractorKNN::getkNNSamples BackgroundSubtractorKNN::getkNNSamples
--------------------------------------------- ---------------------------------------------
@ -788,7 +788,7 @@ BackgroundSubtractorKNN::setkNNSamples
--------------------------------------------- ---------------------------------------------
Sets the k in the kNN. How many nearest neigbours need to match. Sets the k in the kNN. How many nearest neigbours need to match.
.. ocv:function:: void BackgroundSubtractorKNN::setkNNSamples(int nKNN) .. ocv:function:: void BackgroundSubtractorKNN::setkNNSamples(int _nkNN)
BackgroundSubtractorKNN::getDetectShadows BackgroundSubtractorKNN::getDetectShadows
@ -1108,9 +1108,9 @@ Releases all inner buffers.
.. [Bradski98] Bradski, G.R. "Computer Vision Face Tracking for Use in a Perceptual User Interface", Intel, 1998 .. [Bradski98] Bradski, G.R. "Computer Vision Face Tracking for Use in a Perceptual User Interface", Intel, 1998
.. [Bradski00] Davis, J.W. and Bradski, G.R. “Motion Segmentation and Pose Recognition with Motion History Gradients”, WACV00, 2000 .. [Bradski00] Davis, J.W. and Bradski, G.R. “Motion Segmentation and Pose Recognition with Motion History Gradientsâ€?, WACV00, 2000
.. [Davis97] Davis, J.W. and Bobick, A.F. “The Representation and Recognition of Action Using Temporal Templates”, CVPR97, 1997 .. [Davis97] Davis, J.W. and Bobick, A.F. “The Representation and Recognition of Action Using Temporal Templatesâ€?, 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 .. [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
@ -1124,7 +1124,7 @@ Releases all inner buffers.
.. [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. .. [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 .. [Welch95] Greg Welch and Gary Bishop “An Introduction to the Kalman Filterâ€?, 1995
.. [Tao2012] Michael Tao, Jiamin Bai, Pushmeet Kohli and Sylvain Paris. SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm. Computer Graphics Forum (Eurographics 2012) .. [Tao2012] Michael Tao, Jiamin Bai, Pushmeet Kohli and Sylvain Paris. SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm. Computer Graphics Forum (Eurographics 2012)

13
modules/video/src/bgfg_KNN.cpp
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@ -594,7 +594,7 @@ void BackgroundSubtractorKNNImpl::apply(InputArray _image, OutputArray _fgmask,
bShadowDetection, bShadowDetection,
nShadowDetection nShadowDetection
); );
}; }
void BackgroundSubtractorKNNImpl::getBackgroundImage(OutputArray backgroundImage) const void BackgroundSubtractorKNNImpl::getBackgroundImage(OutputArray backgroundImage) const
{ {
@ -640,12 +640,15 @@ void BackgroundSubtractorKNNImpl::getBackgroundImage(OutputArray backgroundImage
default: default:
CV_Error(Error::StsUnsupportedFormat, ""); CV_Error(Error::StsUnsupportedFormat, "");
} }
}; }
Ptr<BackgroundSubtractorKNN> createBackgroundSubtractorKNN(int _history, double _threshold2,bool _bShadowDetection) Ptr<BackgroundSubtractorKNN> createBackgroundSubtractorKNN(int _history, double _threshold2,
bool _bShadowDetection)
{ {
return makePtr<BackgroundSubtractorKNNImpl>(_history, (float)_threshold2, _bShadowDetection); return makePtr<BackgroundSubtractorKNNImpl>(_history, (float)_threshold2, _bShadowDetection);
}; }
};//namespace cv }
/* End of file. */