Package org.opencv.face
Class LBPHFaceRecognizer
java.lang.Object
org.opencv.core.Algorithm
org.opencv.face.FaceRecognizer
org.opencv.face.LBPHFaceRecognizer
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Field Summary
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Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionstatic 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: setGridXint
getGridY()
SEE: setGridYint
SEE: setNeighborsint
SEE: setRadiusdouble
SEE: setThresholdvoid
setGridX
(int val) getGridX SEE: getGridXvoid
setGridY
(int val) getGridY SEE: getGridYvoid
setNeighbors
(int val) getNeighbors SEE: getNeighborsvoid
setRadius
(int val) getRadius SEE: getRadiusvoid
setThreshold
(double val) getThreshold SEE: getThresholdMethods 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
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Constructor Details
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LBPHFaceRecognizer
protected LBPHFaceRecognizer(long addr)
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Method Details
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__fromPtr__
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getGridX
public int getGridX()SEE: setGridX- Returns:
- automatically generated
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setGridX
public void setGridX(int val) getGridX SEE: getGridX- Parameters:
val
- automatically generated
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getGridY
public int getGridY()SEE: setGridY- Returns:
- automatically generated
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setGridY
public void setGridY(int val) getGridY SEE: getGridY- Parameters:
val
- automatically generated
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getRadius
public int getRadius()SEE: setRadius- Returns:
- automatically generated
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setRadius
public void setRadius(int val) getRadius SEE: getRadius- Parameters:
val
- automatically generated
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getNeighbors
public int getNeighbors()SEE: setNeighbors- Returns:
- automatically generated
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setNeighbors
public void setNeighbors(int val) getNeighbors SEE: getNeighbors- Parameters:
val
- automatically generated
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getThreshold
public double getThreshold()SEE: setThreshold- Returns:
- automatically generated
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setThreshold
public void setThreshold(double val) getThreshold SEE: getThreshold- Parameters:
val
- automatically generated
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getHistograms
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getLabels
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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 use8
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.
- 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
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create
- 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 use8
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.
- 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
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create
- 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 use8
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.
- 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
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create
- 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 use8
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.
- 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
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create
- 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 use8
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.
- 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
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create
radius, the smoother the image but more spatial information you can get. appropriate value is to use8
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.
- 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
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finalize
- Overrides:
finalize
in classFaceRecognizer
- Throws:
Throwable
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