vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e
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/*
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By downloading, copying, installing or using the software you agree to this
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license. If you do not agree to this license, do not download, install,
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copy or use the software.
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|
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License Agreement
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For Open Source Computer Vision Library
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(3-clause BSD License)
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Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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Third party copyrights are property of their respective owners.
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Redistribution and use in source and binary forms, with or without modification,
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are permitted provided that the following conditions are met:
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|
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* Redistributions of source code must retain the above copyright notice,
|
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this list of conditions and the following disclaimer.
|
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|
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* Redistributions in binary form must reproduce the above copyright notice,
|
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this list of conditions and the following disclaimer in the documentation
|
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and/or other materials provided with the distribution.
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* Neither the names of the copyright holders nor the names of the contributors
|
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may be used to endorse or promote products derived from this software
|
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without specific prior written permission.
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|
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This software is provided by the copyright holders and contributors "as is" and
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any express or implied warranties, including, but not limited to, the implied
|
||||
warranties of merchantability and fitness for a particular purpose are
|
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disclaimed. In no event shall copyright holders or contributors be liable for
|
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any direct, indirect, incidental, special, exemplary, or consequential damages
|
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(including, but not limited to, procurement of substitute goods or services;
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loss of use, data, or profits; or business interruption) however caused
|
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and on any theory of liability, whether in contract, strict liability,
|
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or tort (including negligence or otherwise) arising in any way out of
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the use of this software, even if advised of the possibility of such damage.
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*/
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#ifndef __OPENCV_BGSEGM_HPP__
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#define __OPENCV_BGSEGM_HPP__
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#include "opencv2/video.hpp"
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#ifdef __cplusplus
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/** @defgroup bgsegm Improved Background-Foreground Segmentation Methods
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*/
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namespace cv
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{
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namespace bgsegm
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{
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//! @addtogroup bgsegm
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//! @{
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/** @brief Gaussian Mixture-based Background/Foreground Segmentation Algorithm.
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The class implements the algorithm described in @cite KB2001 .
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*/
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class CV_EXPORTS_W BackgroundSubtractorMOG : public BackgroundSubtractor
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{
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public:
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// BackgroundSubtractor interface
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/** @brief Computes a foreground mask.
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@param image Next video frame of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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*/
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CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
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/** @brief Computes a foreground mask and skips known foreground in evaluation.
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@param image Next video frame of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param knownForegroundMask The mask for inputting already known foreground, allows model to ignore learning known pixels.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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*/
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CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
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CV_WRAP virtual int getHistory() const = 0;
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CV_WRAP virtual void setHistory(int nframes) = 0;
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CV_WRAP virtual int getNMixtures() const = 0;
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CV_WRAP virtual void setNMixtures(int nmix) = 0;
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CV_WRAP virtual double getBackgroundRatio() const = 0;
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CV_WRAP virtual void setBackgroundRatio(double backgroundRatio) = 0;
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CV_WRAP virtual double getNoiseSigma() const = 0;
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CV_WRAP virtual void setNoiseSigma(double noiseSigma) = 0;
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};
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/** @brief Creates mixture-of-gaussian background subtractor
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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
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means some automatic value.
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*/
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CV_EXPORTS_W Ptr<BackgroundSubtractorMOG>
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createBackgroundSubtractorMOG(int history=200, int nmixtures=5,
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double backgroundRatio=0.7, double noiseSigma=0);
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/** @brief Background Subtractor module based on the algorithm given in @cite Gold2012 .
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Takes a series of images and returns a sequence of mask (8UC1)
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images of the same size, where 255 indicates Foreground and 0 represents Background.
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This class implements an algorithm described in "Visual Tracking of Human Visitors under
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Variable-Lighting Conditions for a Responsive Audio Art Installation," A. Godbehere,
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A. Matsukawa, K. Goldberg, American Control Conference, Montreal, June 2012.
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*/
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class CV_EXPORTS_W BackgroundSubtractorGMG : public BackgroundSubtractor
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{
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public:
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// BackgroundSubtractor interface
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/** @brief Computes a foreground mask.
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@param image Next video frame of type CV_8UC(n),CV_8SC(n),CV_16UC(n),CV_16SC(n),CV_32SC(n),CV_32FC(n),CV_64FC(n), where n is 1,2,3,4.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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*/
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CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
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/** @brief Computes a foreground mask with known foreground mask input.
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@param image Next video frame.
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@param fgmask The output foreground mask as an 8-bit binary image.
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@param knownForegroundMask The mask for inputting already known foreground.
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
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rate. 0 means that the background model is not updated at all, 1 means that the background model
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is completely reinitialized from the last frame.
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@note This method has a default virtual implementation that throws a "not implemented" error.
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Foreground masking may not be supported by all background subtractors.
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*/
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CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
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CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
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/** @brief Returns total number of distinct colors to maintain in histogram.
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*/
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CV_WRAP virtual int getMaxFeatures() const = 0;
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/** @brief Sets total number of distinct colors to maintain in histogram.
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*/
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CV_WRAP virtual void setMaxFeatures(int maxFeatures) = 0;
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/** @brief Returns the learning rate of the algorithm.
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It lies between 0.0 and 1.0. It determines how quickly features are "forgotten" from
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histograms.
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*/
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CV_WRAP virtual double getDefaultLearningRate() const = 0;
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/** @brief Sets the learning rate of the algorithm.
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*/
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CV_WRAP virtual void setDefaultLearningRate(double lr) = 0;
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/** @brief Returns the number of frames used to initialize background model.
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*/
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CV_WRAP virtual int getNumFrames() const = 0;
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/** @brief Sets the number of frames used to initialize background model.
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*/
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CV_WRAP virtual void setNumFrames(int nframes) = 0;
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/** @brief Returns the parameter used for quantization of color-space.
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It is the number of discrete levels in each channel to be used in histograms.
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*/
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CV_WRAP virtual int getQuantizationLevels() const = 0;
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/** @brief Sets the parameter used for quantization of color-space
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*/
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CV_WRAP virtual void setQuantizationLevels(int nlevels) = 0;
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/** @brief Returns the prior probability that each individual pixel is a background pixel.
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*/
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CV_WRAP virtual double getBackgroundPrior() const = 0;
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/** @brief Sets the prior probability that each individual pixel is a background pixel.
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*/
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CV_WRAP virtual void setBackgroundPrior(double bgprior) = 0;
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/** @brief Returns the kernel radius used for morphological operations
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*/
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CV_WRAP virtual int getSmoothingRadius() const = 0;
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/** @brief Sets the kernel radius used for morphological operations
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*/
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CV_WRAP virtual void setSmoothingRadius(int radius) = 0;
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/** @brief Returns the value of decision threshold.
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Decision value is the value above which pixel is determined to be FG.
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*/
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CV_WRAP virtual double getDecisionThreshold() const = 0;
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/** @brief Sets the value of decision threshold.
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*/
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CV_WRAP virtual void setDecisionThreshold(double thresh) = 0;
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/** @brief Returns the status of background model update
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*/
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CV_WRAP virtual bool getUpdateBackgroundModel() const = 0;
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/** @brief Sets the status of background model update
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*/
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CV_WRAP virtual void setUpdateBackgroundModel(bool update) = 0;
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/** @brief Returns the minimum value taken on by pixels in image sequence. Usually 0.
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*/
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CV_WRAP virtual double getMinVal() const = 0;
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/** @brief Sets the minimum value taken on by pixels in image sequence.
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*/
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CV_WRAP virtual void setMinVal(double val) = 0;
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/** @brief Returns the maximum value taken on by pixels in image sequence. e.g. 1.0 or 255.
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*/
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CV_WRAP virtual double getMaxVal() const = 0;
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/** @brief Sets the maximum value taken on by pixels in image sequence.
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*/
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CV_WRAP virtual void setMaxVal(double val) = 0;
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};
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/** @brief Creates a GMG Background Subtractor
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@param initializationFrames number of frames used to initialize the background models.
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@param decisionThreshold Threshold value, above which it is marked foreground, else background.
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*/
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CV_EXPORTS_W Ptr<BackgroundSubtractorGMG> createBackgroundSubtractorGMG(int initializationFrames=120,
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double decisionThreshold=0.8);
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/** @brief Background subtraction based on counting.
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About as fast as MOG2 on a high end system.
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More than twice faster than MOG2 on cheap hardware (benchmarked on Raspberry Pi3).
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|
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%Algorithm by Sagi Zeevi ( https://github.com/sagi-z/BackgroundSubtractorCNT )
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*/
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class CV_EXPORTS_W BackgroundSubtractorCNT : public BackgroundSubtractor
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{
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public:
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// BackgroundSubtractor interface
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CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
|
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|
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/** @brief Computes a foreground mask with known foreground mask input.
|
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|
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@param image Next video frame.
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@param knownForegroundMask The mask for inputting already known foreground.
|
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@param fgmask The output foreground mask as an 8-bit binary image.
|
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@param learningRate The value between 0 and 1 that indicates how fast the background model is
|
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learnt. Negative parameter value makes the algorithm to use some automatically chosen learning
|
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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.
|
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|
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@note This method has a default virtual implementation that throws a "not impemented" error.
|
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Foreground masking may not be supported by all background subtractors.
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*/
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CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
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CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
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/** @brief Returns number of frames with same pixel color to consider stable.
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*/
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CV_WRAP virtual int getMinPixelStability() const = 0;
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/** @brief Sets the number of frames with same pixel color to consider stable.
|
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*/
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CV_WRAP virtual void setMinPixelStability(int value) = 0;
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|
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/** @brief Returns maximum allowed credit for a pixel in history.
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*/
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CV_WRAP virtual int getMaxPixelStability() const = 0;
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/** @brief Sets the maximum allowed credit for a pixel in history.
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*/
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CV_WRAP virtual void setMaxPixelStability(int value) = 0;
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/** @brief Returns if we're giving a pixel credit for being stable for a long time.
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*/
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CV_WRAP virtual bool getUseHistory() const = 0;
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/** @brief Sets if we're giving a pixel credit for being stable for a long time.
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*/
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CV_WRAP virtual void setUseHistory(bool value) = 0;
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/** @brief Returns if we're parallelizing the algorithm.
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*/
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CV_WRAP virtual bool getIsParallel() const = 0;
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/** @brief Sets if we're parallelizing the algorithm.
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*/
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CV_WRAP virtual void setIsParallel(bool value) = 0;
|
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};
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/** @brief Creates a CNT Background Subtractor
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@param minPixelStability number of frames with same pixel color to consider stable
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@param useHistory determines if we're giving a pixel credit for being stable for a long time
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@param maxPixelStability maximum allowed credit for a pixel in history
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@param isParallel determines if we're parallelizing the algorithm
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*/
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CV_EXPORTS_W Ptr<BackgroundSubtractorCNT>
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createBackgroundSubtractorCNT(int minPixelStability = 15,
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bool useHistory = true,
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int maxPixelStability = 15*60,
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bool isParallel = true);
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enum LSBPCameraMotionCompensation {
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LSBP_CAMERA_MOTION_COMPENSATION_NONE = 0,
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LSBP_CAMERA_MOTION_COMPENSATION_LK
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};
|
||||
|
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/** @brief Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
|
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|
||||
This algorithm demonstrates better performance on CDNET 2014 dataset compared to other algorithms in OpenCV.
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*/
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class CV_EXPORTS_W BackgroundSubtractorGSOC : public BackgroundSubtractor
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{
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||||
public:
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// BackgroundSubtractor interface
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||||
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
|
||||
CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
|
||||
|
||||
CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
|
||||
};
|
||||
|
||||
/** @brief Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
|
||||
*/
|
||||
class CV_EXPORTS_W BackgroundSubtractorLSBP : public BackgroundSubtractor
|
||||
{
|
||||
public:
|
||||
// BackgroundSubtractor interface
|
||||
CV_WRAP virtual void apply(InputArray image, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
|
||||
CV_WRAP virtual void apply(InputArray image, InputArray knownForegroundMask, OutputArray fgmask, double learningRate=-1) CV_OVERRIDE = 0;
|
||||
|
||||
CV_WRAP virtual void getBackgroundImage(OutputArray backgroundImage) const CV_OVERRIDE = 0;
|
||||
};
|
||||
|
||||
/** @brief This is for calculation of the LSBP descriptors.
|
||||
*/
|
||||
class CV_EXPORTS_W BackgroundSubtractorLSBPDesc
|
||||
{
|
||||
public:
|
||||
static void calcLocalSVDValues(OutputArray localSVDValues, const Mat& frame);
|
||||
|
||||
static void computeFromLocalSVDValues(OutputArray desc, const Mat& localSVDValues, const Point2i* LSBPSamplePoints);
|
||||
|
||||
static void compute(OutputArray desc, const Mat& frame, const Point2i* LSBPSamplePoints);
|
||||
};
|
||||
|
||||
/** @brief Creates an instance of BackgroundSubtractorGSOC algorithm.
|
||||
|
||||
Implementation of the different yet better algorithm which is called GSOC, as it was implemented during GSOC and was not originated from any paper.
|
||||
|
||||
@param mc Whether to use camera motion compensation.
|
||||
@param nSamples Number of samples to maintain at each point of the frame.
|
||||
@param replaceRate Probability of replacing the old sample - how fast the model will update itself.
|
||||
@param propagationRate Probability of propagating to neighbors.
|
||||
@param hitsThreshold How many positives the sample must get before it will be considered as a possible replacement.
|
||||
@param alpha Scale coefficient for threshold.
|
||||
@param beta Bias coefficient for threshold.
|
||||
@param blinkingSupressionDecay Blinking supression decay factor.
|
||||
@param blinkingSupressionMultiplier Blinking supression multiplier.
|
||||
@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
|
||||
@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
|
||||
*/
|
||||
CV_EXPORTS_W Ptr<BackgroundSubtractorGSOC> createBackgroundSubtractorGSOC(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, float replaceRate = 0.003f, float propagationRate = 0.01f, int hitsThreshold = 32, float alpha = 0.01f, float beta = 0.0022f, float blinkingSupressionDecay = 0.1f, float blinkingSupressionMultiplier = 0.1f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f);
|
||||
|
||||
/** @brief Creates an instance of BackgroundSubtractorLSBP algorithm.
|
||||
|
||||
Background Subtraction using Local SVD Binary Pattern. More details about the algorithm can be found at @cite LGuo2016
|
||||
|
||||
@param mc Whether to use camera motion compensation.
|
||||
@param nSamples Number of samples to maintain at each point of the frame.
|
||||
@param LSBPRadius LSBP descriptor radius.
|
||||
@param Tlower Lower bound for T-values. See @cite LGuo2016 for details.
|
||||
@param Tupper Upper bound for T-values. See @cite LGuo2016 for details.
|
||||
@param Tinc Increase step for T-values. See @cite LGuo2016 for details.
|
||||
@param Tdec Decrease step for T-values. See @cite LGuo2016 for details.
|
||||
@param Rscale Scale coefficient for threshold values.
|
||||
@param Rincdec Increase/Decrease step for threshold values.
|
||||
@param noiseRemovalThresholdFacBG Strength of the noise removal for background points.
|
||||
@param noiseRemovalThresholdFacFG Strength of the noise removal for foreground points.
|
||||
@param LSBPthreshold Threshold for LSBP binary string.
|
||||
@param minCount Minimal number of matches for sample to be considered as foreground.
|
||||
*/
|
||||
CV_EXPORTS_W Ptr<BackgroundSubtractorLSBP> createBackgroundSubtractorLSBP(int mc = LSBP_CAMERA_MOTION_COMPENSATION_NONE, int nSamples = 20, int LSBPRadius = 16, float Tlower = 2.0f, float Tupper = 32.0f, float Tinc = 1.0f, float Tdec = 0.05f, float Rscale = 10.0f, float Rincdec = 0.005f, float noiseRemovalThresholdFacBG = 0.0004f, float noiseRemovalThresholdFacFG = 0.0008f, int LSBPthreshold = 8, int minCount = 2);
|
||||
|
||||
/** @brief Synthetic frame sequence generator for testing background subtraction algorithms.
|
||||
|
||||
It will generate the moving object on top of the background.
|
||||
It will apply some distortion to the background to make the test more complex.
|
||||
*/
|
||||
class CV_EXPORTS_W SyntheticSequenceGenerator : public Algorithm
|
||||
{
|
||||
private:
|
||||
const double amplitude;
|
||||
const double wavelength;
|
||||
const double wavespeed;
|
||||
const double objspeed;
|
||||
unsigned timeStep;
|
||||
Point2d pos;
|
||||
Point2d dir;
|
||||
Mat background;
|
||||
Mat object;
|
||||
RNG rng;
|
||||
|
||||
public:
|
||||
/** @brief Creates an instance of SyntheticSequenceGenerator.
|
||||
|
||||
@param background Background image for object.
|
||||
@param object Object image which will move slowly over the background.
|
||||
@param amplitude Amplitude of wave distortion applied to background.
|
||||
@param wavelength Length of waves in distortion applied to background.
|
||||
@param wavespeed How fast waves will move.
|
||||
@param objspeed How fast object will fly over background.
|
||||
*/
|
||||
CV_WRAP SyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude, double wavelength, double wavespeed, double objspeed);
|
||||
|
||||
/** @brief Obtain the next frame in the sequence.
|
||||
|
||||
@param frame Output frame.
|
||||
@param gtMask Output ground-truth (reference) segmentation mask object/background.
|
||||
*/
|
||||
CV_WRAP void getNextFrame(OutputArray frame, OutputArray gtMask);
|
||||
};
|
||||
|
||||
/** @brief Creates an instance of SyntheticSequenceGenerator.
|
||||
|
||||
@param background Background image for object.
|
||||
@param object Object image which will move slowly over the background.
|
||||
@param amplitude Amplitude of wave distortion applied to background.
|
||||
@param wavelength Length of waves in distortion applied to background.
|
||||
@param wavespeed How fast waves will move.
|
||||
@param objspeed How fast object will fly over background.
|
||||
*/
|
||||
CV_EXPORTS_W Ptr<SyntheticSequenceGenerator> createSyntheticSequenceGenerator(InputArray background, InputArray object, double amplitude = 2.0, double wavelength = 20.0, double wavespeed = 0.2, double objspeed = 6.0);
|
||||
|
||||
//! @}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
#endif
|
||||
#endif
|
||||
Reference in New Issue
Block a user