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Conflicts: modules/calib3d/doc/camera_calibration_and_3d_reconstruction.rst modules/features2d/doc/common_interfaces_of_descriptor_extractors.rst modules/features2d/doc/object_categorization.rst modules/gpu/doc/camera_calibration_and_3d_reconstruction.rst modules/gpu/doc/image_filtering.rst modules/gpu/doc/image_processing.rst modules/gpu/doc/video.rst modules/imgproc/doc/miscellaneous_transformations.rst modules/imgproc/doc/object_detection.rst modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst modules/imgproc/src/samplers.cpp modules/ml/doc/k_nearest_neighbors.rst modules/nonfree/doc/feature_detection.rst modules/ocl/include/opencv2/ocl/ocl.hpp modules/photo/doc/inpainting.rst modules/ts/include/opencv2/ts.hpp platforms/scripts/camera_build.conf samples/android/camera-calibration/AndroidManifest.xml
128 lines
5.3 KiB
ReStructuredText
128 lines
5.3 KiB
ReStructuredText
Background Segmentation
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=======================
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.. highlight:: cpp
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gpu::BackgroundSubtractorMOG
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----------------------------
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Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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.. ocv:class:: gpu::BackgroundSubtractorMOG : public cv::BackgroundSubtractorMOG
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The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2001]_.
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.. seealso:: :ocv:class:`BackgroundSubtractorMOG`
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.. note::
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* An example on gaussian mixture based background/foreground segmantation can be found at opencv_source_code/samples/gpu/bgfg_segm.cpp
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gpu::createBackgroundSubtractorMOG
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----------------------------------
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Creates mixture-of-gaussian background subtractor
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.. ocv:function:: Ptr<gpu::BackgroundSubtractorMOG> gpu::createBackgroundSubtractorMOG(int history=200, int nmixtures=5, double backgroundRatio=0.7, double noiseSigma=0)
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:param history: Length of the history.
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:param nmixtures: Number of Gaussian mixtures.
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:param backgroundRatio: Background ratio.
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:param noiseSigma: Noise strength (standard deviation of the brightness or each color channel). 0 means some automatic value.
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gpu::BackgroundSubtractorMOG2
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-----------------------------
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Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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.. ocv:class:: gpu::BackgroundSubtractorMOG2 : public cv::BackgroundSubtractorMOG2
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The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [MOG2004]_.
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.. seealso:: :ocv:class:`BackgroundSubtractorMOG2`
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gpu::createBackgroundSubtractorMOG2
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-----------------------------------
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Creates MOG2 Background Subtractor
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.. ocv:function:: Ptr<gpu::BackgroundSubtractorMOG2> gpu::createBackgroundSubtractorMOG2( int history=500, double varThreshold=16, bool detectShadows=true )
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:param history: Length of the history.
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: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.
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: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.
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gpu::BackgroundSubtractorGMG
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----------------------------
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Background/Foreground Segmentation Algorithm.
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.. ocv:class:: gpu::BackgroundSubtractorGMG : public cv::BackgroundSubtractorGMG
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The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [GMG2012]_.
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gpu::createBackgroundSubtractorGMG
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----------------------------------
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Creates GMG Background Subtractor
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.. ocv:function:: Ptr<gpu::BackgroundSubtractorGMG> gpu::createBackgroundSubtractorGMG(int initializationFrames = 120, double decisionThreshold = 0.8)
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:param initializationFrames: Number of frames of video to use to initialize histograms.
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:param decisionThreshold: Value above which pixel is determined to be FG.
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gpu::BackgroundSubtractorFGD
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----------------------------
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.. ocv:class:: gpu::BackgroundSubtractorFGD : public cv::BackgroundSubtractor
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The class discriminates between foreground and background pixels by building and maintaining a model of the background. Any pixel which does not fit this model is then deemed to be foreground. The class implements algorithm described in [FGD2003]_. ::
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class CV_EXPORTS BackgroundSubtractorFGD : public cv::BackgroundSubtractor
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{
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public:
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virtual void getForegroundRegions(OutputArrayOfArrays foreground_regions) = 0;
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};
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.. seealso:: :ocv:class:`BackgroundSubtractor`
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gpu::BackgroundSubtractorFGD::getForegroundRegions
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--------------------------------------------------
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Returns the output foreground regions calculated by :ocv:func:`findContours`.
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.. ocv:function:: void gpu::BackgroundSubtractorFGD::getForegroundRegions(OutputArrayOfArrays foreground_regions)
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:params foreground_regions: Output array (CPU memory).
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gpu::createBackgroundSubtractorFGD
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----------------------------------
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Creates FGD Background Subtractor
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.. ocv:function:: Ptr<gpu::BackgroundSubtractorGMG> gpu::createBackgroundSubtractorFGD(const FGDParams& params = FGDParams())
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:param params: Algorithm's parameters. See [FGD2003]_ for explanation.
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.. [FGD2003] Liyuan Li, Weimin Huang, Irene Y.H. Gu, and Qi Tian. *Foreground Object Detection from Videos Containing Complex Background*. ACM MM2003 9p, 2003.
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.. [MOG2001] 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
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.. [MOG2004] Z. Zivkovic. *Improved adaptive Gausian mixture model for background subtraction*. International Conference Pattern Recognition, UK, August, 2004
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.. [GMG2012] A. Godbehere, A. Matsukawa and K. Goldberg. *Visual Tracking of Human Visitors under Variable-Lighting Conditions for a Responsive Audio Art Installation*. American Control Conference, Montreal, June 2012
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