Package org.opencv.face

Class LBPHFaceRecognizer

java.lang.Object

org.opencv.core.Algorithm

org.opencv.face.FaceRecognizer

org.opencv.face.LBPHFaceRecognizer

public class LBPHFaceRecognizer
extends FaceRecognizer

Field Summary

Fields inherited from class org.opencv.core.Algorithm

nativeObj

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	LBPHFaceRecognizer(long addr)

Method Summary

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()	SEE: setRadius
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)	getRadius SEE: getRadius
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

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