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123 lines
4.1 KiB
123 lines
4.1 KiB
9 years ago
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/** @file */
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/* Copyright (c) 2008, Naotoshi Seo. All rights reserved.
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*
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* The program is free to use for non-commercial academic purposes,
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* but for course works, you must understand what is going inside to
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* use. The program can be used, modified, or re-distributed for any
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* purposes only if you or one of your group understand not only
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* programming codes but also theory and math behind (if any).
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* Please contact the authors if you are interested in using the
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* program without meeting the above conditions.
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*/
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#include "cv.h"
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#include "cvaux.h"
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#include <stdio.h>
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#include <iostream>
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#define _USE_MATH_DEFINES
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#include <math.h>
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#ifndef CV_PCADIST_INCLUDED
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#define CV_PCADIST_INCLUDED
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#ifndef NO_DOXYGEN
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CVAPI(void)
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cvMatPcaDist( const CvMat* samples, const CvMat* avg,
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const CvMat* eigenvectors, CvMat* dists );
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CV_INLINE double
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cvPcaDist( const CvMat* sample, const CvMat* avg,
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const CvMat* eigenvectors );
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#endif
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/**
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* Distance between sample and PCA subspace, i.e, reconstruction error
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*
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* @param samples D x N sample vectors
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* @param avg D x 1 mean vector
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* @param eigenvectors M x D or D x M (automatically adjusted) eigen vectors
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* @param dists 1 x N computed distances
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*
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* @see CVAPI(void) cvCalcPCA( const CvArr* data, CvArr* avg, CvArr* eigenvalues, CvArr* eigenvectors, int flags );
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* @see CVAPI(void) cvProjectPCA( const CvArr* data, const CvArr* avg, const CvArr* eigenvectors, CvArr* result );
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* @see CVAPI(void) cvBackProjectPCA( const CvArr* proj, const CvArr* avg,const CvArr* eigenvects, CvArr* result );
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*/
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CVAPI(void)
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cvMatPcaDist( const CvMat* samples, const CvMat* avg,
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const CvMat* eigenvectors, CvMat* dists )
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{
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int D = samples->rows;
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int N = samples->cols;
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int M = (eigenvectors->rows == D) ? eigenvectors->cols : eigenvectors->rows;
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int type = samples->type;
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int d, n;
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CvMat *_eigenvectors; // cvProjectPCA requires M x D vec
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CvMat *proj = cvCreateMat( N, M, type );
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CvMat *subproj, subprojhdr;
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CvMat *samples0 = cvCreateMat( D, N, type ); // mean subtracted samples
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CvMat *subsamples0, subsamples0hdr;
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CvMat *_proj;
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CV_FUNCNAME( "cvMatPcaDist" );
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__CV_BEGIN__;
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CV_ASSERT( CV_IS_MAT(samples) );
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CV_ASSERT( CV_IS_MAT(avg) );
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CV_ASSERT( CV_IS_MAT(eigenvectors) );
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CV_ASSERT( D == eigenvectors->rows || D == eigenvectors->cols );
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CV_ASSERT( D == avg->rows && 1 == avg->cols );
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CV_ASSERT( 1 == dists->rows && N == dists->cols );
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if( D == eigenvectors->rows ) { // support transpose
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_eigenvectors = cvCreateMat( M, D, type );
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cvT( eigenvectors, _eigenvectors );
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} else {
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_eigenvectors = (CvMat*)eigenvectors;
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}
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//cvProjectPCA( samples, avg, _eigenvectors, proj );
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for( n = 0; n < N; n++ ) { // want mean subtracted samples for laster too
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for( d = 0; d < D; d++ ) {
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cvmSet( samples0, d, n, cvmGet( samples, d, n ) - cvmGet( avg, d, 0 ) );
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}
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}
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_proj = cvCreateMat( M, N, type );
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cvMatMul( _eigenvectors, samples0, _proj );
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cvT( _proj, proj );
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cvReleaseMat( &_proj );
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// distance from feature space
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for( n = 0; n < N; n++ ) {
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subsamples0 = cvGetCol( samples0, &subsamples0hdr, n );
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subproj = cvGetRow( proj, &subprojhdr, n );
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cvmSet( dists, 0, n, pow(cvNorm( subsamples0, NULL, CV_L2 ),2) - pow(cvNorm( subproj, NULL, CV_L2 ),2) );
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}
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cvReleaseMat( &proj );
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cvReleaseMat( &samples0 );
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if( D == eigenvectors->rows ) {
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cvReleaseMat( &_eigenvectors );
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}
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__CV_END__;
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}
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/**
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* Distance between sample and PCA subspace, i.e, reconstruction error
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*
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* @param sample D x 1 feature vector
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* @param avg D x 1 mean vector
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* @param eigenvectors M x D eigen vectors
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* @return double
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*/
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CV_INLINE double
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cvPcaDist( const CvMat* sample, const CvMat* avg, const CvMat* eigenvectors )
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{
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double prob;
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CvMat *_dists = cvCreateMat( 1, 1, sample->type );
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cvMatPcaDist( sample, avg, eigenvectors, _dists );
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prob = cvmGet(_dists, 0, 0);
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cvReleaseMat( &_dists );
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return prob;
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}
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#endif
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