org.bytedeco.javacpp

Class opencv_face

java.lang.Object

org.bytedeco.javacpp.presets.opencv_face

org.bytedeco.javacpp.opencv_face

All Implemented Interfaces:

InfoMapper

public class opencv_face
extends opencv_face

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	opencv_face.BasicFaceRecognizer \addtogroup face \{
static class	opencv_face.FaceRecognizer \addtogroup face \{
static class	opencv_face.LBPHFaceRecognizer
static class	opencv_face.MinDistancePredictCollector \brief default predict collector that trace minimal distance with treshhold checking (that is default behavior for most predict logic)
static class	opencv_face.PredictCollector \addtogroup face \{ /** \brief Abstract base class for all strategies of prediction result handling

Constructor Summary

Constructors

Constructor and Description
opencv_face()

Method Summary

Methods

Modifier and Type	Method and Description
static opencv_face.BasicFaceRecognizer	createEigenFaceRecognizer()
static opencv_face.BasicFaceRecognizer	createEigenFaceRecognizer(int num_components, double threshold)
static opencv_face.BasicFaceRecognizer	createFisherFaceRecognizer()
static opencv_face.BasicFaceRecognizer	createFisherFaceRecognizer(int num_components, double threshold)
static opencv_face.LBPHFaceRecognizer	createLBPHFaceRecognizer()
static opencv_face.LBPHFaceRecognizer	createLBPHFaceRecognizer(int radius, int neighbors, int grid_x, int grid_y, double threshold)

Methods inherited from class org.bytedeco.javacpp.presets.opencv_face

map

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

opencv_face

public opencv_face()

Method Detail

createEigenFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.BasicFaceRecognizer createEigenFaceRecognizer(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.

### Model internal data:

- num_components see createEigenFaceRecognizer. - threshold see createEigenFaceRecognizer. - 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.

createEigenFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.BasicFaceRecognizer createEigenFaceRecognizer()

createFisherFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.BasicFaceRecognizer createFisherFaceRecognizer(int num_components,
                                                                                        double threshold)

Parameters:

num_components - The number of components (read: Fisherfaces) kept for this Linear Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that means the number of your classes c (read: subjects, persons you want to recognize). If you leave this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the correct number (c-1) automatically.

threshold - The threshold applied in the prediction. If the distance to the nearest neighbor is larger than the threshold, this method returns -1.

### Notes:

- Training and prediction must be done on grayscale images, use cvtColor to convert between the color spaces. - **THE FISHERFACES 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.

### Model internal data:

- num_components see createFisherFaceRecognizer. - threshold see createFisherFaceRecognizer. - eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending). - eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their eigenvalue). - mean The sample mean calculated from the training data. - projections The projections of the training data. - labels The labels corresponding to the projections.

createFisherFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.BasicFaceRecognizer createFisherFaceRecognizer()

createLBPHFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.LBPHFaceRecognizer createLBPHFaceRecognizer(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

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 createLBPHFaceRecognizer. - neighbors see createLBPHFaceRecognizer. - grid_x see createLBPHFaceRecognizer. - grid_y see createLBPHFaceRecognizer. - threshold see createLBPHFaceRecognizer. - 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.

createLBPHFaceRecognizer

@Namespace(value="cv::face")
@opencv_core.Ptr
public static opencv_face.LBPHFaceRecognizer createLBPHFaceRecognizer()