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Merge branch 'master' of code.opencv.org:opencv
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a717b7cbed
@ -861,18 +861,7 @@ namespace cv
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// Optimization Criterion on given data in src and corresponding labels
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// in labels. If 0 (or less) number of components are given, they are
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// automatically determined for given data in computation.
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LDA(const Mat& src, vector<int> labels,
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int num_components = 0) :
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_num_components(num_components)
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{
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this->compute(src, labels); //! compute eigenvectors and eigenvalues
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}
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// Initializes and performs a Discriminant Analysis with Fisher's
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// Optimization Criterion on given data in src and corresponding labels
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// in labels. If 0 (or less) number of components are given, they are
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// automatically determined for given data in computation.
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LDA(InputArray src, InputArray labels,
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LDA(InputArrayOfArrays src, InputArray labels,
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int num_components = 0) :
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_num_components(num_components)
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{
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@ -895,7 +884,7 @@ namespace cv
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~LDA() {}
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//! Compute the discriminants for data in src and labels.
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void compute(InputArray src, InputArray labels);
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void compute(InputArrayOfArrays src, InputArray labels);
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// Projects samples into the LDA subspace.
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Mat project(InputArray src);
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@ -915,7 +904,7 @@ namespace cv
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Mat _eigenvectors;
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Mat _eigenvalues;
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void lda(InputArray src, InputArray labels);
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void lda(InputArrayOfArrays src, InputArray labels);
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};
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class CV_EXPORTS_W FaceRecognizer : public Algorithm
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@ -464,9 +464,12 @@ void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
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// clear existing model data
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_labels.release();
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_projections.clear();
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// get the number of unique classes (provide a cv::Mat overloaded version?)
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// safely copy from cv::Mat to std::vector
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vector<int> ll;
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labels.copyTo(ll);
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for(unsigned int i = 0; i < labels.total(); i++) {
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ll.push_back(labels.at<int>(i));
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}
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// get the number of unique classes
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int C = (int) remove_dups(ll).size();
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// clip number of components to be a valid number
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if((_num_components <= 0) || (_num_components > (C-1)))
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@ -975,10 +975,17 @@ void LDA::load(const FileStorage& fs) {
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fs["eigenvectors"] >> _eigenvectors;
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}
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void LDA::lda(InputArray _src, InputArray _lbls) {
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void LDA::lda(InputArrayOfArrays _src, InputArray _lbls) {
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// get data
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Mat src = _src.getMat();
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vector<int> labels = _lbls.getMat();
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vector<int> labels;
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// safely copy the labels
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{
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Mat tmp = _lbls.getMat();
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for(unsigned int i = 0; i < tmp.total(); i++) {
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labels.push_back(tmp.at<int>(i));
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}
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}
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// turn into row sampled matrix
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Mat data;
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// ensure working matrix is double precision
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@ -1078,7 +1085,7 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
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_eigenvectors = Mat(_eigenvectors, Range::all(), Range(0, _num_components));
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}
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void LDA::compute(InputArray _src, InputArray _lbls) {
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void LDA::compute(InputArrayOfArrays _src, InputArray _lbls) {
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switch(_src.kind()) {
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case _InputArray::STD_VECTOR_MAT:
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lda(asRowMatrix(_src, CV_64FC1), _lbls);
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