Class LBPHFaceRecognizer


public class LBPHFaceRecognizer extends FaceRecognizer
  • Constructor Details

    • LBPHFaceRecognizer

      protected LBPHFaceRecognizer(long addr)
  • Method Details

    • __fromPtr__

      public static LBPHFaceRecognizer __fromPtr__(long addr)
    • 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 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 Throwable
      Overrides:
      finalize in class FaceRecognizer
      Throws:
      Throwable