org.bytedeco.javacpp

Class opencv_core.PCA

java.lang.Object

org.bytedeco.javacpp.Pointer

org.bytedeco.javacpp.opencv_core.PCA

Enclosing class:

opencv_core

public static class opencv_core.PCA
extends Pointer

Principal Component Analysis The class PCA is used to compute the special basis for a set of vectors. The basis will consist of eigenvectors of the covariance matrix computed from the input set of vectors. After PCA is performed, vectors can be transformed from the original high-dimensional space to the subspace formed by a few most prominent eigenvectors (called the principal components), corresponding to the largest eigenvalues of the covariation matrix. Thus the dimensionality of the vector and the correlation between the coordinates is reduced. The following sample is the function that takes two matrices. The first one stores the set of vectors (a row per vector) that is used to compute PCA, the second one stores another "test" set of vectors (a row per vector) that are first compressed with PCA, then reconstructed back and then the reconstruction error norm is computed and printed for each vector. \code using namespace cv; PCA compressPCA(const Mat& pcaset, int maxComponents, const Mat& testset, Mat& compressed) { PCA pca(pcaset, // pass the data Mat(), // we do not have a pre-computed mean vector, // so let the PCA engine to compute it CV_PCA_DATA_AS_ROW, // indicate that the vectors // are stored as matrix rows // (use CV_PCA_DATA_AS_COL if the vectors are // the matrix columns) maxComponents // specify, how many principal components to retain ); // if there is no test data, just return the computed basis, ready-to-use if( !testset.data ) return pca; CV_Assert( testset.cols == pcaset.cols ); compressed.create(testset.rows, maxComponents, testset.type()); Mat reconstructed; for( int i = 0; i < testset.rows; i++ ) { Mat vec = testset.row(i), coeffs = compressed.row(i), reconstructed; // compress the vector, the result will be stored // in the i-th row of the output matrix pca.project(vec, coeffs); // and then reconstruct it pca.backProject(coeffs, reconstructed); // and measure the error printf("%d. diff = %g\n", i, norm(vec, reconstructed, NORM_L2)); } return pca; } \endcode

Nested Class Summary

Nested classes/interfaces inherited from class org.bytedeco.javacpp.Pointer

Pointer.CustomDeallocator, Pointer.Deallocator, Pointer.NativeDeallocator

Field Summary

Fields inherited from class org.bytedeco.javacpp.Pointer

address, capacity, limit, position

Constructor Summary

Constructors

Constructor and Description
opencv_core.PCA() default constructor
opencv_core.PCA(int size)
opencv_core.PCA(opencv_core.Mat data, opencv_core.Mat mean, int flags)
opencv_core.PCA(opencv_core.Mat data, opencv_core.Mat mean, int flags, double retainedVariance)
opencv_core.PCA(opencv_core.Mat data, opencv_core.Mat mean, int flags, int maxComponents) the constructor that performs PCA
opencv_core.PCA(Pointer p)

Method Summary

Methods

Modifier and Type	Method and Description
opencv_core.PCA	apply(opencv_core.Mat data, opencv_core.Mat mean, int flags)
opencv_core.PCA	apply(opencv_core.Mat data, opencv_core.Mat mean, int flags, int maxComponents) operator that performs PCA.
opencv_core.Mat	backProject(opencv_core.Mat vec) reconstructs the original vector from the projection
void	backProject(opencv_core.Mat vec, opencv_core.Mat result) reconstructs the original vector from the projection
opencv_core.PCA	computeVar(opencv_core.Mat data, opencv_core.Mat mean, int flags, double retainedVariance)
opencv_core.Mat	eigenvalues() eigenvalues of the covariation matrix
opencv_core.PCA	eigenvalues(opencv_core.Mat eigenvalues)
opencv_core.Mat	eigenvectors() eigenvectors of the covariation matrix
opencv_core.PCA	eigenvectors(opencv_core.Mat eigenvectors)
opencv_core.Mat	mean() mean value subtracted before the projection and added after the back projection
opencv_core.PCA	mean(opencv_core.Mat mean)
opencv_core.PCA	position(int position)
opencv_core.Mat	project(opencv_core.Mat vec) projects vector from the original space to the principal components subspace
void	project(opencv_core.Mat vec, opencv_core.Mat result) projects vector from the original space to the principal components subspace

Methods inherited from class org.bytedeco.javacpp.Pointer

address, asBuffer, asByteBuffer, capacity, capacity, deallocate, deallocate, deallocateReferences, deallocator, deallocator, equals, fill, hashCode, isNull, limit, limit, memchr, memcmp, memcpy, memmove, memset, offsetof, position, put, setNull, sizeof, toString, withDeallocator, zero

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

opencv_core.PCA

public opencv_core.PCA(Pointer p)

opencv_core.PCA

public opencv_core.PCA(int size)

opencv_core.PCA

public opencv_core.PCA()

default constructor

opencv_core.PCA

public opencv_core.PCA(opencv_core.Mat data,
               opencv_core.Mat mean,
               int flags,
               int maxComponents)

the constructor that performs PCA

opencv_core.PCA

public opencv_core.PCA(opencv_core.Mat data,
               opencv_core.Mat mean,
               int flags)

opencv_core.PCA

public opencv_core.PCA(opencv_core.Mat data,
               opencv_core.Mat mean,
               int flags,
               double retainedVariance)

Method Detail

position

public opencv_core.PCA position(int position)

Overrides:

position in class Pointer

apply

public opencv_core.PCA apply(opencv_core.Mat data,
                    opencv_core.Mat mean,
                    int flags,
                    int maxComponents)

operator that performs PCA. The previously stored data, if any, is released

apply

public opencv_core.PCA apply(opencv_core.Mat data,
                    opencv_core.Mat mean,
                    int flags)

computeVar

public opencv_core.PCA computeVar(opencv_core.Mat data,
                         opencv_core.Mat mean,
                         int flags,
                         double retainedVariance)

project

public opencv_core.Mat project(opencv_core.Mat vec)

projects vector from the original space to the principal components subspace

project

public void project(opencv_core.Mat vec,
           opencv_core.Mat result)

projects vector from the original space to the principal components subspace

backProject

public opencv_core.Mat backProject(opencv_core.Mat vec)

reconstructs the original vector from the projection

backProject

public void backProject(opencv_core.Mat vec,
               opencv_core.Mat result)

reconstructs the original vector from the projection

eigenvectors

public opencv_core.Mat eigenvectors()

eigenvectors of the covariation matrix

eigenvectors

public opencv_core.PCA eigenvectors(opencv_core.Mat eigenvectors)

eigenvalues

public opencv_core.Mat eigenvalues()

eigenvalues of the covariation matrix

eigenvalues

public opencv_core.PCA eigenvalues(opencv_core.Mat eigenvalues)

mean

public opencv_core.Mat mean()

mean value subtracted before the projection and added after the back projection

mean

public opencv_core.PCA mean(opencv_core.Mat mean)