OpenCV: cv::PCA Class Reference (2024)

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

// are stored as matrix rows

// (use 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;

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;

}

enum cv::PCA::Flags

default constructor

The default constructor initializes an empty PCA structure. The other constructors initialize the structure and call PCA::operator()().

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

data	input samples stored as matrix rows or matrix columns.
mean	optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
flags	operation flags; currently the parameter is only used to specify the data layout (PCA::Flags)
maxComponents	maximum number of components that PCA should retain; by default, all the components are retained.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

data	input samples stored as matrix rows or matrix columns.
mean	optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
flags	operation flags; currently the parameter is only used to specify the data layout (PCA::Flags)
retainedVariance	Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.

Reconstructs vectors from their PC projections.

The methods are inverse operations to PCA::project. They take PC coordinates of projected vectors and reconstruct the original vectors. Unless all the principal components have been retained, the reconstructed vectors are different from the originals. But typically, the difference is small if the number of components is large enough (but still much smaller than the original vector dimensionality). As a result, PCA is used.

Parameters

vec	coordinates of the vectors in the principal component subspace, the layout and size are the same as of PCA::project output vectors.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

vec	coordinates of the vectors in the principal component subspace, the layout and size are the same as of PCA::project output vectors.
result	reconstructed vectors; the layout and size are the same as of PCA::project input vectors.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

data	input samples stored as the matrix rows or as the matrix columns.
mean	optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
flags	operation flags; currently the parameter is only used to specify the data layout. (PCA::Flags)
retainedVariance	Percentage of variance that PCA should retain. Using this parameter will let the PCA decided how many components to retain but it will always keep at least 2.

performs PCA

The operator performs PCA of the supplied dataset. It is safe to reuse the same PCA structure for multiple datasets. That is, if the structure has been previously used with another dataset, the existing internal data is reclaimed and the new eigenvalues, eigenvectors and mean are allocated and computed.

The computed eigenvalues are sorted from the largest to the smallest and the corresponding eigenvectors are stored as eigenvectors rows.

Parameters

data	input samples stored as the matrix rows or as the matrix columns.
mean	optional mean value; if the matrix is empty (noArray()), the mean is computed from the data.
flags	operation flags; currently the parameter is only used to specify the data layout. (Flags)
maxComponents	maximum number of components that PCA should retain; by default, all the components are retained.

Projects vector(s) to the principal component subspace.

The methods project one or more vectors to the principal component subspace, where each vector projection is represented by coefficients in the principal component basis. The first form of the method returns the matrix that the second form writes to the result. So the first form can be used as a part of expression while the second form can be more efficient in a processing loop.

Parameters

vec	input vector(s); must have the same dimensionality and the same layout as the input data used at PCA phase, that is, if DATA_AS_ROW are specified, then vec.cols==data.cols (vector dimensionality) and vec.rows is the number of vectors to project, and the same is true for the PCA::DATA_AS_COL case.

This is an overloaded member function, provided for convenience. It differs from the above function only in what argument(s) it accepts.

Parameters

vec	input vector(s); must have the same dimensionality and the same layout as the input data used at PCA phase, that is, if DATA_AS_ROW are specified, then vec.cols==data.cols (vector dimensionality) and vec.rows is the number of vectors to project, and the same is true for the PCA::DATA_AS_COL case.
result	output vectors; in case of PCA::DATA_AS_COL, the output matrix has as many columns as the number of input vectors, this means that result.cols==vec.cols and the number of rows match the number of principal components (for example, maxComponents parameter passed to the constructor).

load PCA objects

Loads eigenvalues eigenvectors and mean from specified FileNode

write PCA objects

Writes eigenvalues eigenvectors and mean to specified FileStorage

Mat cv::PCA::eigenvalues

Mat cv::PCA::eigenvectors

Mat cv::PCA::mean