## Class LBPHFaceRecognizer

• public class LBPHFaceRecognizer
extends FaceRecognizer

• ### Fields inherited from class org.opencv.core.Algorithm

nativeObj
• ### Constructor Summary

Constructors
Modifier Constructor Description
protected  LBPHFaceRecognizer​(long addr)
• ### Method Summary

All Methods
Modifier and Type Method Description
static LBPHFaceRecognizer __fromPtr__​(long addr)
static LBPHFaceRecognizer create()
radius, the smoother the image but more spatial information you can get.
static LBPHFaceRecognizer create​(int radius)
static LBPHFaceRecognizer create​(int radius, int neighbors)
static LBPHFaceRecognizer create​(int radius, int neighbors, int grid_x)
static LBPHFaceRecognizer create​(int radius, int neighbors, int grid_x, int grid_y)
static LBPHFaceRecognizer create​(int radius, int neighbors, int grid_x, int grid_y, double threshold)
protected void finalize()
int getGridX()
SEE: setGridX
int getGridY()
SEE: setGridY
java.util.List<Mat> getHistograms()
Mat getLabels()
int getNeighbors()
SEE: setNeighbors
int getRadius()
double getThreshold()
SEE: setThreshold
void setGridX​(int val)
getGridX SEE: getGridX
void setGridY​(int val)
getGridY SEE: getGridY
void setNeighbors​(int val)
getNeighbors SEE: getNeighbors
void setRadius​(int val)
void setThreshold​(double val)
getThreshold SEE: getThreshold
• ### Methods inherited from class org.opencv.face.FaceRecognizer

getLabelInfo, getLabelsByString, predict, predict_collect, predict_label, read, setLabelInfo, train, update, write
• ### Methods inherited from class org.opencv.core.Algorithm

clear, empty, getDefaultName, getNativeObjAddr, save
• ### Methods inherited from class java.lang.Object

clone, equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
• ### Constructor Detail

• #### LBPHFaceRecognizer

protected LBPHFaceRecognizer​(long addr)
• ### Method Detail

• #### __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

public int getRadius()
Returns:
automatically generated

public void setRadius​(int val)
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:
• 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:
• 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:
• 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:
• 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:
• 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:
• 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