void PCAKNN::train(const list<Interval> &intervals, const bool console_output) {
if (intervals.size() == 0) return;
projections.clear(); n = 0;
width = intervals.front().start->face.cols;
height = intervals.front().start->face.rows;
list<Interval>::const_iterator itr;
vector<Face>::const_iterator start, end;
//add the number of images from all intervals
for (itr = intervals.cbegin(); itr != intervals.cend(); itr++) {
n += itr->Length();
}
Mat pca_matrix(static_cast<int>(n), width*height, data_type);
int c = 0;
for (itr = intervals.cbegin(); itr != intervals.cend(); itr++) {
//for each image in the current interval
for (start = itr->start, end = itr->end; start != end; start++, ++c) {
if (console_output) printf("Preparing samples %d/%d\n", c + 1, n);
//insert current image into pca_matrix
Mat image_row = start->face.clone().reshape(1, 1);
Mat row_i = pca_matrix.row(c);
image_row.convertTo(row_i, data_type);//CV_64FC1 ?
Face f; f.name = start->name;
projections.push_back(f);//save the names for later
}
}
if (console_output) printf("TRAINING...\n");
//Perfrom principal component analysis on pca_matrix
PCA pca(pca_matrix, Mat(), CV_PCA_DATA_AS_ROW, pca_matrix.rows);
//extract mean/eigenvalues
mean = pca.mean.reshape(1, 1);
ev = pca.eigenvalues.clone();
transpose(pca.eigenvectors, w);
//project each face into subspace and save them with the name above for recognition
for (unsigned int i = 0; i<n; ++i) {
if (console_output) printf("Projecting %d/%d\n", i + 1, n);//project so subspace
projections[i].face = subspaceProject(w, mean, pca_matrix.row(i));
}
}
static MATRIX *
align_pca(MRI *mri_in, MRI *mri_ref) {
int row, col, i ;
float dot ;
MATRIX *m_ref_evectors = NULL, *m_in_evectors = NULL ;
float in_evalues[3], ref_evalues[3] ;
double ref_means[3], in_means[3] ;
#if 0
MRI *mri_in_windowed, *mri_ref_windowed ;
mri_in_windowed = MRIwindow(mri_in, NULL, WINDOW_HANNING,127,127,127,100.0f);
mri_ref_windowed = MRIwindow(mri_ref,NULL,WINDOW_HANNING,127,127,127,100.0f);
if (Gdiag & DIAG_WRITE) {
MRIwriteImageViews(mri_in_windowed, "in_windowed", 400) ;
MRIwriteImageViews(mri_ref_windowed, "ref_windowed", 400) ;
}
#endif
if (!m_ref_evectors)
m_ref_evectors = MatrixAlloc(3,3,MATRIX_REAL) ;
if (!m_in_evectors)
m_in_evectors = MatrixAlloc(3,3,MATRIX_REAL) ;
MRIprincipleComponents(mri_ref, m_ref_evectors, ref_evalues,
ref_means, thresh_low);
MRIprincipleComponents(mri_in,m_in_evectors,in_evalues,in_means,thresh_low);
/* check to make sure eigenvectors aren't reversed */
for (col = 1 ; col <= 3 ; col++) {
#if 0
float theta ;
#endif
for (dot = 0.0f, row = 1 ; row <= 3 ; row++)
dot += m_in_evectors->rptr[row][col] * m_ref_evectors->rptr[row][col] ;
if (dot < 0.0f) {
printf("WARNING: mirror image detected in eigenvector #%d\n",
col) ;
dot *= -1.0f ;
for (row = 1 ; row <= 3 ; row++)
m_in_evectors->rptr[row][col] *= -1.0f ;
}
#if 0
theta = acos(dot) ;
printf("angle[%d] = %2.1f\n", col, DEGREES(theta)) ;
#endif
}
printf("ref_evectors = \n") ;
for (i = 1 ; i <= 3 ; i++)
printf("\t\t%2.2f %2.2f %2.2f\n",
m_ref_evectors->rptr[i][1],
m_ref_evectors->rptr[i][2],
m_ref_evectors->rptr[i][3]) ;
printf("\nin_evectors = \n") ;
for (i = 1 ; i <= 3 ; i++)
printf("\t\t%2.2f %2.2f %2.2f\n",
m_in_evectors->rptr[i][1],
m_in_evectors->rptr[i][2],
m_in_evectors->rptr[i][3]) ;
return(pca_matrix(m_in_evectors, in_means,m_ref_evectors, ref_means)) ;
}
static MATRIX *
compute_pca(MRI *mri_in, MRI *mri_ref) {
int row, col, i ;
float dot ;
MATRIX *m_ref_evectors = NULL, *m_in_evectors = NULL ;
float in_evalues[3], ref_evalues[3] ;
double ref_means[3], in_means[3] ;
if (!m_ref_evectors)
m_ref_evectors = MatrixAlloc(3,3,MATRIX_REAL) ;
if (!m_in_evectors)
m_in_evectors = MatrixAlloc(3,3,MATRIX_REAL) ;
if (binarize) {
MRIbinaryPrincipleComponents(mri_ref, m_ref_evectors, ref_evalues,
ref_means, thresh_low);
MRIbinaryPrincipleComponents(mri_in,m_in_evectors,in_evalues,in_means,
thresh_low);
} else {
MRIprincipleComponents(mri_ref, m_ref_evectors, ref_evalues, ref_means,
thresh_low);
MRIprincipleComponents(mri_in,m_in_evectors,in_evalues,in_means,
thresh_low);
}
order_eigenvectors(m_in_evectors, m_in_evectors) ;
order_eigenvectors(m_ref_evectors, m_ref_evectors) ;
/* check to make sure eigenvectors aren't reversed */
for (col = 1 ; col <= 3 ; col++) {
#if 0
float theta ;
#endif
for (dot = 0.0f, row = 1 ; row <= 3 ; row++)
dot += m_in_evectors->rptr[row][col] * m_ref_evectors->rptr[row][col] ;
if (dot < 0.0f) {
fprintf(stderr, "WARNING: mirror image detected in eigenvector #%d\n",
col) ;
dot *= -1.0f ;
for (row = 1 ; row <= 3 ; row++)
m_in_evectors->rptr[row][col] *= -1.0f ;
}
#if 0
theta = acos(dot) ;
fprintf(stderr, "angle[%d] = %2.1f\n", col, DEGREES(theta)) ;
#endif
}
fprintf(stderr, "ref_evectors = \n") ;
for (i = 1 ; i <= 3 ; i++)
fprintf(stderr, "\t\t%2.2f %2.2f %2.2f\n",
m_ref_evectors->rptr[i][1],
m_ref_evectors->rptr[i][2],
m_ref_evectors->rptr[i][3]) ;
fprintf(stderr, "\nin_evectors = \n") ;
for (i = 1 ; i <= 3 ; i++)
fprintf(stderr, "\t\t%2.2f %2.2f %2.2f\n",
m_in_evectors->rptr[i][1],
m_in_evectors->rptr[i][2],
m_in_evectors->rptr[i][3]) ;
return(pca_matrix(m_in_evectors, in_means,m_ref_evectors, ref_means)) ;
}
