Package org.opencv.face
Class EigenFaceRecognizer
- java.lang.Object
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- org.opencv.core.Algorithm
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- org.opencv.face.FaceRecognizer
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- org.opencv.face.BasicFaceRecognizer
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- org.opencv.face.EigenFaceRecognizer
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public class EigenFaceRecognizer extends BasicFaceRecognizer
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Constructor Summary
Constructors Modifier Constructor Description protected
EigenFaceRecognizer(long addr)
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description static EigenFaceRecognizer
__fromPtr__(long addr)
static EigenFaceRecognizer
create()
Component Analysis.static EigenFaceRecognizer
create(int num_components)
static EigenFaceRecognizer
create(int num_components, double threshold)
protected void
finalize()
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Methods inherited from class org.opencv.face.BasicFaceRecognizer
getEigenValues, getEigenVectors, getLabels, getMean, getNumComponents, getProjections, getThreshold, setNumComponents, setThreshold
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Methods inherited from class org.opencv.face.FaceRecognizer
getLabelInfo, getLabelsByString, predict, predict_collect, predict_label, read, setLabelInfo, train, update, write
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Methods inherited from class org.opencv.core.Algorithm
clear, empty, getDefaultName, getNativeObjAddr, save
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Method Detail
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__fromPtr__
public static EigenFaceRecognizer __fromPtr__(long addr)
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create
public static EigenFaceRecognizer create(int num_components, double threshold)
- Parameters:
num_components
- The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient.threshold
- The threshold applied in the prediction. ### Notes:- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
- THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
- This model does not support updating.
- num_components see EigenFaceRecognizer::create.
- threshold see EigenFaceRecognizer::create.
- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
- eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
- mean The sample mean calculated from the training data.
- projections The projections of the training data.
- labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
- Returns:
- automatically generated
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create
public static EigenFaceRecognizer create(int num_components)
- Parameters:
num_components
- The number of components (read: Eigenfaces) kept for this Principal Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
- THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
- This model does not support updating.
- num_components see EigenFaceRecognizer::create.
- threshold see EigenFaceRecognizer::create.
- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
- eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
- mean The sample mean calculated from the training data.
- projections The projections of the training data.
- labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
- Returns:
- automatically generated
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create
public static EigenFaceRecognizer create()
Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be kept for good reconstruction capabilities. It is based on your input data, so experiment with the number. Keeping 80 components should almost always be sufficient. ### Notes:- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces.
- THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL SIZE. (caps-lock, because I got so many mails asking for this). You have to make sure your input data has the correct shape, else a meaningful exception is thrown. Use resize to resize the images.
- This model does not support updating.
- num_components see EigenFaceRecognizer::create.
- threshold see EigenFaceRecognizer::create.
- eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
- eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their eigenvalue).
- mean The sample mean calculated from the training data.
- projections The projections of the training data.
- labels The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.
- Returns:
- automatically generated
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finalize
protected void finalize() throws java.lang.Throwable
- Overrides:
finalize
in classBasicFaceRecognizer
- Throws:
java.lang.Throwable
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