Package org.opencv.face

Class LBPHFaceRecognizer

    • Constructor Detail

      • LBPHFaceRecognizer

        protected LBPHFaceRecognizer​(long addr)

    • Method Detail

      • getGridX

        public int getGridX()
        SEE: setGridX
        Returns:
        automatically generated

      • setGridX

        public void setGridX​(int val)
        getGridX SEE: getGridX
        Parameters:
        val - automatically generated

      • getGridY

        public int getGridY()
        SEE: setGridY
        Returns:
        automatically generated

      • setGridY

        public void setGridY​(int val)
        getGridY SEE: getGridY
        Parameters:
        val - automatically generated

      • getRadius

        public int getRadius()
        SEE: setRadius
        Returns:
        automatically generated

      • setRadius

        public void setRadius​(int val)
        getRadius SEE: getRadius
        Parameters:
        val - automatically generated

      • getNeighbors

        public int getNeighbors()
        SEE: setNeighbors
        Returns:
        automatically generated

      • setNeighbors

        public void setNeighbors​(int val)
        getNeighbors SEE: getNeighbors
        Parameters:
        val - automatically generated

      • getThreshold

        public double getThreshold()
        SEE: setThreshold
        Returns:
        automatically generated

      • setThreshold

        public void setThreshold​(double val)
        getThreshold SEE: getThreshold
        Parameters:
        val - automatically generated

      • getHistograms

        public java.util.List<Mat> getHistograms()

      • getLabels

        public Mat getLabels()

      • create

        public static LBPHFaceRecognizer create​(int radius,
                                        int neighbors,
                                        int grid_x,
                                        int grid_y,
                                        double threshold)
        Parameters:
        radius - The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.
        neighbors - The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.
        grid_x - The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
        grid_y - The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
        threshold - The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • create

        public static LBPHFaceRecognizer create​(int radius,
                                        int neighbors,
                                        int grid_x,
                                        int grid_y)
        Parameters:
        radius - The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.
        neighbors - The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.
        grid_x - The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector.
        grid_y - The number of cells in the vertical direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • create

        public static LBPHFaceRecognizer create​(int radius,
                                        int neighbors,
                                        int grid_x)
        Parameters:
        radius - The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.
        neighbors - The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost.
        grid_x - The number of cells in the horizontal direction, 8 is a common value used in publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • create

        public static LBPHFaceRecognizer create​(int radius,
                                        int neighbors)
        Parameters:
        radius - The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get.
        neighbors - The number of sample points to build a Circular Local Binary Pattern from. An appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • create

        public static LBPHFaceRecognizer create​(int radius)
        Parameters:
        radius - The radius used for building the Circular Local Binary Pattern. The greater the radius, the smoother the image but more spatial information you can get. appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • create

        public static LBPHFaceRecognizer create()
        radius, the smoother the image but more spatial information you can get. appropriate value is to use 8 sample points. Keep in mind: the more sample points you include, the higher the computational cost. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. publications. The more cells, the finer the grid, the higher the dimensionality of the resulting feature vector. is larger than the threshold, this method returns -1. ### Notes:
        • The Circular Local Binary Patterns (used in training and prediction) expect the data given as grayscale images, use cvtColor to convert between the color spaces.
        • This model supports updating.
        ### Model internal data:
        • radius see LBPHFaceRecognizer::create.
        • neighbors see LBPHFaceRecognizer::create.
        • grid_x see LLBPHFaceRecognizer::create.
        • grid_y see LBPHFaceRecognizer::create.
        • threshold see LBPHFaceRecognizer::create.
        • histograms Local Binary Patterns Histograms calculated from the given training data (empty if none was given).
        • labels Labels corresponding to the calculated Local Binary Patterns Histograms.
        Returns:
        automatically generated

      • finalize

        protected void finalize()
                 throws java.lang.Throwable
        Overrides:
        finalize in class FaceRecognizer
        Throws:
        java.lang.Throwable