new optimized version of BackgroundSubtractorGMG
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Vladislav Vinogradov
@@ -199,111 +199,20 @@ protected:
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*/
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class CV_EXPORTS BackgroundSubtractorGMG: public cv::BackgroundSubtractor
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{
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protected:
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/**
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* Used internally to represent a single feature in a histogram.
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* Feature is a color and an associated likelihood (weight in the histogram).
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*/
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struct CV_EXPORTS HistogramFeatureGMG
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{
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/**
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* Default constructor.
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* Initializes likelihood of feature to 0, color remains uninitialized.
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*/
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HistogramFeatureGMG(){likelihood = 0.0;}
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/**
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* Copy constructor.
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* Required to use HistogramFeatureGMG in a std::vector
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* @see operator =()
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*/
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HistogramFeatureGMG(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood;
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}
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/**
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* Assignment operator.
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* Required to use HistogramFeatureGMG in a std::vector
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*/
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HistogramFeatureGMG& operator =(const HistogramFeatureGMG& orig){
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color = orig.color; likelihood = orig.likelihood; return *this;
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}
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/**
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* Tests equality of histogram features.
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* Equality is tested only by matching the color (feature), not the likelihood.
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* This operator is used to look up an observed feature in a histogram.
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*/
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bool operator ==(HistogramFeatureGMG &rhs);
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//! Regardless of the image datatype, it is quantized and mapped to an integer and represented as a vector.
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vector<size_t> color;
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//! Represents the weight of feature in the histogram.
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float likelihood;
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friend class PixelModelGMG;
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};
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/**
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* Representation of the statistical model of a single pixel for use in the background subtraction
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* algorithm.
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*/
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class CV_EXPORTS PixelModelGMG
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{
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public:
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PixelModelGMG();
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~PixelModelGMG();
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/**
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* Incorporate the last observed feature into the statistical model.
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*
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* @param learningRate The adaptation parameter for the histogram. -1.0 to use default. Value
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* should be between 0.0 and 1.0, the higher the value, the faster the
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* adaptation. 1.0 is limiting case where fast adaptation means no memory.
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*/
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void insertFeature(double learningRate = -1.0);
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/**
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* Set the feature last observed, to save before incorporating it into the statistical
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* model with insertFeature().
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*
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* @param feature The feature (color) just observed.
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*/
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void setLastObservedFeature(BackgroundSubtractorGMG::HistogramFeatureGMG feature);
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/**
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* Set the upper limit for the number of features to store in the histogram. Use to adjust
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* memory requirements.
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*
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* @param max size_t representing the max number of features.
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*/
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void setMaxFeatures(size_t max) {
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maxFeatures = max; histogram.resize(max); histogram.clear();
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}
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/**
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* Normalize the histogram, so sum of weights of all features = 1.0
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*/
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void normalizeHistogram();
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/**
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* Return the weight of a feature in the histogram. If the feature is not represented in the
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* histogram, the weight returned is 0.0.
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*/
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double getLikelihood(HistogramFeatureGMG f);
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PixelModelGMG& operator *=(const float &rhs);
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//friend class BackgroundSubtractorGMG;
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//friend class HistogramFeatureGMG;
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private:
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size_t numFeatures; //!< number of features in histogram
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size_t maxFeatures; //!< max allowable features in histogram
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std::list<HistogramFeatureGMG> histogram; //!< represents the histogram as a list of features
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HistogramFeatureGMG lastObservedFeature;
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//!< store last observed feature in case we need to add it to histogram
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};
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public:
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BackgroundSubtractorGMG();
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virtual ~BackgroundSubtractorGMG();
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virtual AlgorithmInfo* info() const;
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/**
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* Validate parameters and set up data structures for appropriate image size.
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* Must call before running on data.
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* @param frameSize input frame size
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* @param min minimum value taken on by pixels in image sequence. Usually 0
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* @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
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*/
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void initialize(cv::Size frameSize, double min, double max);
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/**
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* Performs single-frame background subtraction and builds up a statistical background image
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* model.
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@@ -312,28 +221,6 @@ public:
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*/
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virtual void operator()(InputArray image, OutputArray fgmask, double learningRate=-1.0);
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/**
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* Validate parameters and set up data structures for appropriate image type. Must call before
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* running on data.
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* @param image One sample image from dataset
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* @param min minimum value taken on by pixels in image sequence. Usually 0
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* @param max maximum value taken on by pixels in image sequence. e.g. 1.0 or 255
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*/
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void initializeType(InputArray image, double min, double max);
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/**
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* Selectively update the background model. Only update background model for pixels identified
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* as background.
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* @param mask Mask image same size as images in sequence. Must be 8UC1 matrix, 255 for foreground
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* and 0 for background.
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*/
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void updateBackgroundModel(InputArray mask);
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/**
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* Retrieve the greyscale image representing the probability that each pixel is foreground given
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* the current estimated background model. Values are 0.0 (black) to 1.0 (white).
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* @param img The 32FC1 image representing per-pixel probabilities that the pixel is foreground.
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*/
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void getPosteriorImage(OutputArray img);
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protected:
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//! Total number of distinct colors to maintain in histogram.
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int maxFeatures;
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@@ -345,31 +232,23 @@ protected:
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int quantizationLevels;
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//! Prior probability that any given pixel is a background pixel. A sensitivity parameter.
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double backgroundPrior;
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//! value above which pixel is determined to be FG.
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double decisionThreshold;
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//! smoothing radius, in pixels, for cleaning up FG image.
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int smoothingRadius;
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double decisionThreshold; //!< value above which pixel is determined to be FG.
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int smoothingRadius; //!< smoothing radius, in pixels, for cleaning up FG image.
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private:
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double maxVal_;
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double minVal_;
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double maxVal, minVal;
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cv::Size frameSize_;
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size_t frameNum_;
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/*
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* General Parameters
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*/
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int imWidth; //!< width of image.
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int imHeight; //!< height of image.
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size_t numPixels;
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cv::Mat_<int> nfeatures_;
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cv::Mat_<int> colors_;
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cv::Mat_<float> weights_;
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unsigned int numChannels; //!< Number of channels in image.
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bool isDataInitialized;
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//!< After general parameters are set, data structures must be initialized.
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/*
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* Data Structures
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*/
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vector<PixelModelGMG> pixels; //!< Probabilistic background models for each pixel in image.
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int frameNum; //!< Frame number counter, used to count frames in training mode.
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Mat posteriorImage; //!< Posterior probability image.
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Mat fgMaskImage; //!< Foreground mask image.
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cv::Mat buf_;
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};
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}
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