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Python typdef fixed. Reverted the example to MOG2. Not time to make the command line switch for now.

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unknown 2014-03-28 14:23:10 +01:00
parent be7eb72051
commit 75534a4828
3 changed files with 6 additions and 5 deletions
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1
modules/python/src2/cv2.cpp
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@ -134,6 +134,7 @@ typedef Ptr<DescriptorMatcher> Ptr_DescriptorMatcher;
typedef Ptr<BackgroundSubtractor> Ptr_BackgroundSubtractor; typedef Ptr<BackgroundSubtractor> Ptr_BackgroundSubtractor;
typedef Ptr<BackgroundSubtractorMOG> Ptr_BackgroundSubtractorMOG; typedef Ptr<BackgroundSubtractorMOG> Ptr_BackgroundSubtractorMOG;
typedef Ptr<BackgroundSubtractorMOG2> Ptr_BackgroundSubtractorMOG2; typedef Ptr<BackgroundSubtractorMOG2> Ptr_BackgroundSubtractorMOG2;
typedef Ptr<BackgroundSubtractorKNN> Ptr_BackgroundSubtractorKNN;
typedef Ptr<BackgroundSubtractorGMG> Ptr_BackgroundSubtractorGMG; typedef Ptr<BackgroundSubtractorGMG> Ptr_BackgroundSubtractorGMG;
typedef Ptr<StereoMatcher> Ptr_StereoMatcher; typedef Ptr<StereoMatcher> Ptr_StereoMatcher;

8
modules/video/doc/motion_analysis_and_object_tracking.rst
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@ -780,7 +780,7 @@ Sets the threshold on the squared distance
BackgroundSubtractorKNN::getkNNSamples 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. 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.
.. ocv:function:: int BackgroundSubtractorKNN::getkNNSamples() const .. ocv:function:: int BackgroundSubtractorKNN::getkNNSamples() const
@ -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 Gradients", 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 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 .. [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 Filter", 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)

2
samples/cpp/bgfg_segm.cpp
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@ -52,7 +52,7 @@ int main(int argc, const char** argv)
namedWindow("foreground image", WINDOW_NORMAL); namedWindow("foreground image", WINDOW_NORMAL);
namedWindow("mean background image", WINDOW_NORMAL); namedWindow("mean background image", WINDOW_NORMAL);
Ptr<BackgroundSubtractor> bg_model = createBackgroundSubtractorKNN(); Ptr<BackgroundSubtractor> bg_model = createBackgroundSubtractorMOG2();
Mat img, fgmask, fgimg; Mat img, fgmask, fgimg;