generic-library/opencv
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The labels of a model are now cloned instead of using Mat::copyTo, because Mat::copyTo leads to a crash with the Python wrapper. I need to further investigate it.

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Philipp Wagner 2012-09-07 22:42:46 +02:00
parent a5e37779fb
commit cfa250eff5
1 changed files with 4 additions and 2 deletions
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6
modules/contrib/src/facerec.cpp
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@ -345,6 +345,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
Mat labels = _local_labels.getMat(); Mat labels = _local_labels.getMat();
// observations in row // observations in row
Mat data = asRowMatrix(_src, CV_64FC1); Mat data = asRowMatrix(_src, CV_64FC1);
// number of samples // number of samples
int n = data.rows; int n = data.rows;
// assert there are as much samples as labels // assert there are as much samples as labels
@ -358,6 +359,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
// clip number of components to be valid // clip number of components to be valid
if((_num_components <= 0) || (_num_components > n)) if((_num_components <= 0) || (_num_components > n))
_num_components = n; _num_components = n;
// perform the PCA // perform the PCA
PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components); PCA pca(data, Mat(), CV_PCA_DATA_AS_ROW, _num_components);
// copy the PCA results // copy the PCA results
@ -365,7 +367,7 @@ void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
_eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row _eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row
transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column
// store labels for prediction // store labels for prediction
labels.copyTo(_labels); _labels = labels.clone();
// save projections // save projections
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) { for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx)); Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
@ -481,7 +483,7 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
// store the total mean vector // store the total mean vector
_mean = pca.mean.reshape(1,1); _mean = pca.mean.reshape(1,1);
// store labels // store labels
labels.copyTo(_labels); _labels = labels.clone();
// store the eigenvalues of the discriminants // store the eigenvalues of the discriminants
lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1); lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);
// Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors. // Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors.