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BING files location update and Start SuBSENSE porting
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jaco
@@ -43,20 +43,34 @@
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#define __OPENCV_SALIENCY_SPECIALIZED_CLASSES_HPP_
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#include "saliencyBaseClasses.hpp"
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#include "kyheader.h"
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#include "ValStructVec.h"
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#include "FilterTIG.h"
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#include "BING/kyheader.h"
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#include "BING/ValStructVec.h"
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#include "BING/FilterTIG.h"
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#include "SuBSENSE/BackgroundSubtractorLBSP.h"
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#include <cstdio>
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#include <string>
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#include <iostream>
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//! defines the default value for BackgroundSubtractorLBSP::m_fRelLBSPThreshold
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#define BGSSUBSENSE_DEFAULT_LBSP_REL_SIMILARITY_THRESHOLD (0.333f)
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//! defines the default value for BackgroundSubtractorLBSP::m_nDescDistThreshold
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#define BGSSUBSENSE_DEFAULT_DESC_DIST_THRESHOLD (3)
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//! defines the default value for BackgroundSubtractorSuBSENSE::m_nMinColorDistThreshold
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#define BGSSUBSENSE_DEFAULT_COLOR_DIST_THRESHOLD (30)
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//! defines the default value for BackgroundSubtractorSuBSENSE::m_nBGSamples
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#define BGSSUBSENSE_DEFAULT_NB_BG_SAMPLES (50)
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//! defines the default value for BackgroundSubtractorSuBSENSE::m_nRequiredBGSamples
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#define BGSSUBSENSE_DEFAULT_REQUIRED_NB_BG_SAMPLES (2)
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//! defines the default value for BackgroundSubtractorSuBSENSE::m_nSamplesForMovingAvgs
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#define BGSSUBSENSE_DEFAULT_N_SAMPLES_FOR_MV_AVGS (25)
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namespace cv
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{
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/************************************ Specific Static Saliency Specialized Classes ************************************/
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/**
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* \brief Saliency based on algorithms based on [1]
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* \brief Saliency based on algorithms described in [1]
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* [1]Hou, Xiaodi, and Liqing Zhang. "Saliency detection: A spectral residual approach." Computer Vision and Pattern Recognition, 2007. CVPR'07. IEEE Conference on. IEEE, 2007.
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*/
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class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency
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@@ -67,10 +81,10 @@ class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency
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StaticSaliencySpectralResidual();
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~StaticSaliencySpectralResidual();
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typedef cv::Ptr<Size> (cv::Algorithm::*SizeGetter)();
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typedef Ptr<Size> (cv::Algorithm::*SizeGetter)();
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typedef void (cv::Algorithm::*SizeSetter)( const cv::Ptr<Size> & );
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cv::Ptr<Size> getWsize();
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Ptr<Size> getWsize();
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void setWsize( const cv::Ptr<Size> &arrPtr );
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void read( const FileNode& fn );
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@@ -86,7 +100,7 @@ class CV_EXPORTS_W StaticSaliencySpectralResidual : public StaticSaliency
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/************************************ Specific Motion Saliency Specialized Classes ************************************/
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/**
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* \brief Saliency based on algorithms based on [2]
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* \brief Saliency based on algorithms described in [2]
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* [2] Hofmann, Martin, Philipp Tiefenbacher, and Gerhard Rigoll. "Background segmentation with feedback: The pixel-based adaptive segmenter."
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* Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 IEEE Computer Society Conference on. IEEE, 2012.
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*/
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@@ -107,11 +121,125 @@ class CV_EXPORTS_W MotionSaliencyPBAS : public MotionSaliency
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};
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/*!
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Self-Balanced Sensitivity segmenTER (SuBSENSE) foreground-background segmentation algorithm.
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Note: both grayscale and RGB/BGR images may be used with this extractor (parameters are adjusted automatically).
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For optimal grayscale results, use CV_8UC1 frames instead of CV_8UC3.
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For more details on the different parametersor on the algorithm itself, see P.-L. St-Charles et al.,
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"Flexible Background Subtraction With Self-Balanced Local Sensitivity", in CVPRW 2014.
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This algorithm is currently NOT thread-safe.
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*/
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class CV_EXPORTS_W MotionSaliencySuBSENSE : public BackgroundSubtractorLBSP, public MotionSaliency
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{
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public:
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//! full constructor
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MotionSaliencySuBSENSE( float fRelLBSPThreshold = BGSSUBSENSE_DEFAULT_LBSP_REL_SIMILARITY_THRESHOLD, size_t nMinDescDistThreshold =
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BGSSUBSENSE_DEFAULT_DESC_DIST_THRESHOLD,
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size_t nMinColorDistThreshold = BGSSUBSENSE_DEFAULT_COLOR_DIST_THRESHOLD, size_t nBGSamples =
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BGSSUBSENSE_DEFAULT_NB_BG_SAMPLES,
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size_t nRequiredBGSamples = BGSSUBSENSE_DEFAULT_REQUIRED_NB_BG_SAMPLES, size_t nSamplesForMovingAvgs =
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BGSSUBSENSE_DEFAULT_N_SAMPLES_FOR_MV_AVGS );
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virtual ~MotionSaliencySuBSENSE();
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//! (re)initiaization method; needs to be called before starting background subtraction (note: also reinitializes the keypoints vector)
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virtual void initialize( const cv::Mat& oInitImg, const std::vector<cv::KeyPoint>& voKeyPoints );
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//! refreshes all samples based on the last analyzed frame
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virtual void refreshModel( float fSamplesRefreshFrac );
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//! primary model update function; the learning param is used to override the internal learning thresholds (ignored when <= 0)
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virtual void operator()( cv::InputArray image, cv::OutputArray fgmask, double learningRateOverride = 0 );
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//! returns a copy of the latest reconstructed background image
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void getBackgroundImage( cv::OutputArray backgroundImage ) const;
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//! turns automatic model reset on or off
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void setAutomaticModelReset( bool );
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void read( const FileNode& fn );
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void write( FileStorage& fs ) const;
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protected:
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bool computeSaliencyImpl( const InputArray src, OutputArray dst );
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AlgorithmInfo* info() const;
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//! indicates whether internal structures have already been initialized (LBSP lookup tables, samples, etc.)
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bool m_bInitializedInternalStructs;
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//! absolute minimal color distance threshold ('R' or 'radius' in the original ViBe paper, used as the default/initial 'R(x)' value here, paired with BackgroundSubtractorLBSP::m_nDescDistThreshold)
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const size_t m_nMinColorDistThreshold;
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//! number of different samples per pixel/block to be taken from input frames to build the background model (same as 'N' in ViBe/PBAS)
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const size_t m_nBGSamples;
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//! number of similar samples needed to consider the current pixel/block as 'background' (same as '#_min' in ViBe/PBAS)
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const size_t m_nRequiredBGSamples;
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//! number of samples to use to compute the learning rate of moving averages
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const size_t m_nSamplesForMovingAvgs;
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//! current frame index, frame count since last model reset & model reset cooldown counters
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size_t m_nFrameIndex, m_nFramesSinceLastReset, m_nModelResetCooldown;
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//! last calculated non-zero desc ratio
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float m_fLastNonZeroDescRatio;
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//! specifies whether automatic model reset is enabled or not
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bool m_bAutoModelResetEnabled;
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//! specifies whether Tmin/Tmax scaling is enabled or not
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bool m_bLearningRateScalingEnabled;
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//! current learning rate caps
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float m_fCurrLearningRateLowerCap, m_fCurrLearningRateUpperCap;
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//! current kernel size for median blur post-proc filtering
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int m_nMedianBlurKernelSize;
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//! specifies the px update spread range
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bool m_bUse3x3Spread;
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//! specifies the downsampled frame size (used for cam motion analysis)
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Size m_oDownSampledFrameSize;
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//! background model pixel color intensity samples (equivalent to 'B(x)' in PBAS, but also paired with BackgroundSubtractorLBSP::m_voBGDescSamples to create our complete model)
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std::vector<Mat> m_voBGColorSamples;
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//! per-pixel update rates ('T(x)' in PBAS, which contains pixel-level 'sigmas', as referred to in ViBe)
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cv::Mat m_oUpdateRateFrame;
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//! per-pixel distance thresholds (equivalent to 'R(x)' in PBAS, but used as a relative value to determine both intensity and descriptor variation thresholds)
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cv::Mat m_oDistThresholdFrame;
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//! per-pixel distance variation modulators ('v(x)', relative value used to modulate 'R(x)' and 'T(x)' variations)
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cv::Mat m_oVariationModulatorFrame;
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//! per-pixel mean distances between consecutive frames ('D_last(x)', used to detect ghosts and high variation regions in the sequence)
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cv::Mat m_oMeanLastDistFrame;
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//! per-pixel mean minimal distances from the model ('D_min(x)' in PBAS, used to control variation magnitude and direction of 'T(x)' and 'R(x)')
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cv::Mat m_oMeanMinDistFrame_LT, m_oMeanMinDistFrame_ST;
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//! per-pixel mean downsampled distances between consecutive frames (used to analyze camera movement and control max learning rates globally)
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cv::Mat m_oMeanDownSampledLastDistFrame_LT, m_oMeanDownSampledLastDistFrame_ST;
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//! per-pixel mean raw segmentation results
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cv::Mat m_oMeanRawSegmResFrame_LT, m_oMeanRawSegmResFrame_ST;
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//! per-pixel mean final segmentation results
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cv::Mat m_oMeanFinalSegmResFrame_LT, m_oMeanFinalSegmResFrame_ST;
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//! a lookup map used to keep track of unstable regions (based on segm. noise & local dist. thresholds)
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cv::Mat m_oUnstableRegionMask;
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//! per-pixel blink detection results ('Z(x)')
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cv::Mat m_oBlinksFrame;
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//! pre-allocated matrix used to downsample (1/8) the input frame when needed
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cv::Mat m_oDownSampledColorFrame;
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//! copy of previously used pixel intensities used to calculate 'D_last(x)'
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cv::Mat m_oLastColorFrame;
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//! copy of previously used descriptors used to calculate 'D_last(x)'
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cv::Mat m_oLastDescFrame;
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//! the foreground mask generated by the method at [t-1] (without post-proc, used for blinking px detection)
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cv::Mat m_oRawFGMask_last;
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//! the foreground mask generated by the method at [t-1] (with post-proc)
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cv::Mat m_oFGMask_last;
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//! pre-allocated CV_8UC1 matrices used to speed up morph ops
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cv::Mat m_oFGMask_PreFlood;
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cv::Mat m_oFGMask_FloodedHoles;
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cv::Mat m_oFGMask_last_dilated;
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cv::Mat m_oFGMask_last_dilated_inverted;
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cv::Mat m_oRawFGBlinkMask_curr;
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cv::Mat m_oRawFGBlinkMask_last;
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};
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/************************************ Specific Objectness Specialized Classes ************************************/
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/**
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* \brief Objectness algorithms based on [3]
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* [3] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.
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* \brief Objectness algorithms based on [4]
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* [4] Cheng, Ming-Ming, et al. "BING: Binarized normed gradients for objectness estimation at 300fps." IEEE CVPR. 2014.
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*/
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class CV_EXPORTS_W ObjectnessBING : public Objectness
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{
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