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FaceRecognizer class is now derived from Algorithm, therefore it's possible to set and retrieve the parameters using conventional Algorithm::set and Algorithm::get methods

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This commit is contained in:
Vadim Pisarevsky 2012-05-21 15:27:40 +00:00
parent fd68cc768a
commit 41c932f306
2 changed files with 68 additions and 47 deletions
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4
modules/contrib/include/opencv2/contrib/contrib.hpp
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@ -918,7 +918,7 @@ namespace cv
void lda(InputArray src, InputArray labels);
};
class CV_EXPORTS FaceRecognizer
class CV_EXPORTS FaceRecognizer : public Algorithm
{
public:
//! virtual destructor
@ -970,6 +970,8 @@ namespace cv
};
CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap);
CV_EXPORTS bool initModule_contrib();
}

111
modules/contrib/src/facerec.cpp
View File

@ -90,7 +90,7 @@ class Eigenfaces : public FaceRecognizer
private:
int _num_components;
vector<Mat> _projections;
vector<int> _labels;
Mat _labels;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
@ -124,20 +124,10 @@ public:
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
// Returns the eigenvectors of this PCA.
Mat eigenvectors() const { return _eigenvectors; }
// Returns the eigenvalues of this PCA.
Mat eigenvalues() const { return _eigenvalues; }
// Returns the sample mean of this PCA.
Mat mean() const { return _mean; }
// Returns the number of components used in this PCA.
int num_components() const { return _num_components; }
AlgorithmInfo* info() const;
};
// Belhumeur, P. N., Hespanha, J., and Kriegman, D. "Eigenfaces vs. Fisher-
// faces: Recognition using class specific linear projection.". IEEE
// Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
@ -150,7 +140,7 @@ private:
Mat _eigenvalues;
Mat _mean;
vector<Mat> _projections;
vector<int> _labels;
Mat _labels;
public:
using FaceRecognizer::save;
@ -185,17 +175,7 @@ public:
// See FaceRecognizer::save.
virtual void save(FileStorage& fs) const;
// Returns the eigenvectors of this Fisherfaces model.
Mat eigenvectors() const { return _eigenvectors; }
// Returns the eigenvalues of this Fisherfaces model.
Mat eigenvalues() const { return _eigenvalues; }
// Returns the sample mean of this Fisherfaces model.
Mat mean() const { return _eigenvalues; }
// Returns the number of components used in this Fisherfaces model.
int num_components() const { return _num_components; }
AlgorithmInfo* info() const;
};
// Face Recognition based on Local Binary Patterns.
@ -217,7 +197,7 @@ private:
int _neighbors;
vector<Mat> _histograms;
vector<int> _labels;
Mat _labels;
public:
using FaceRecognizer::save;
@ -271,6 +251,7 @@ public:
int grid_x() const { return _grid_x; }
int grid_y() const { return _grid_y; }
AlgorithmInfo* info() const;
};
@ -302,7 +283,8 @@ void Eigenfaces::train(InputArray src, InputArray _lbls) {
if(_lbls.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// get labels
vector<int> labels = _lbls.getMat();
Mat labels = _lbls.getMat();
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
// observations in row
Mat data = asRowMatrix(src, CV_64FC1);
// number of samples
@ -310,7 +292,7 @@ void Eigenfaces::train(InputArray src, InputArray _lbls) {
// dimensionality of data
//int d = data.cols;
// assert there are as much samples as labels
if((size_t)n != labels.size())
if((size_t)n != labels.total())
CV_Error(CV_StsBadArg, "The number of samples must equal the number of labels!");
// clip number of components to be valid
if((_num_components <= 0) || (_num_components > n))
@ -321,7 +303,7 @@ void Eigenfaces::train(InputArray src, InputArray _lbls) {
_mean = pca.mean.reshape(1,1); // store the mean vector
_eigenvalues = pca.eigenvalues.clone(); // eigenvalues by row
transpose(pca.eigenvectors, _eigenvectors); // eigenvectors by column
_labels = labels; // store labels for prediction
labels.copyTo(_labels); // store labels for prediction
// save projections
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
@ -340,7 +322,7 @@ int Eigenfaces::predict(InputArray _src) const {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
@ -354,7 +336,7 @@ void Eigenfaces::load(const FileStorage& fs) {
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
readFileNodeList(fs["labels"], _labels);
fs["labels"] >> _labels;
}
void Eigenfaces::save(FileStorage& fs) const {
@ -365,7 +347,7 @@ void Eigenfaces::save(FileStorage& fs) const {
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
writeFileNodeList(fs, "labels", _labels);
fs << "labels" << _labels;
}
//------------------------------------------------------------------------------
@ -375,16 +357,22 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
if(_lbls.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// get data
vector<int> labels = _lbls.getMat();
Mat labels = _lbls.getMat();
Mat data = asRowMatrix(src, CV_64FC1);
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
// dimensionality
int N = data.rows; // number of samples
//int D = data.cols; // dimension of samples
// assert correct data alignment
if(labels.size() != (size_t)N)
if(labels.total() != (size_t)N)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// compute the Fisherfaces
int C = (int)remove_dups(labels).size(); // number of unique classes
vector<int> ll;
labels.copyTo(ll);
int C = (int)remove_dups(ll).size(); // number of unique classes
// clip number of components to be a valid number
if((_num_components <= 0) || (_num_components > (C-1)))
_num_components = (C-1);
@ -395,7 +383,7 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
// store the total mean vector
_mean = pca.mean.reshape(1,1);
// store labels
_labels = labels;
labels.copyTo(_labels);
// store the eigenvalues of the discriminants
lda.eigenvalues().convertTo(_eigenvalues, CV_64FC1);
// Now calculate the projection matrix as pca.eigenvectors * lda.eigenvectors.
@ -419,7 +407,7 @@ int Fisherfaces::predict(InputArray _src) const {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
@ -435,7 +423,7 @@ void Fisherfaces::load(const FileStorage& fs) {
fs["eigenvectors"] >> _eigenvectors;
// read sequences
readFileNodeList(fs["projections"], _projections);
readFileNodeList(fs["labels"], _labels);
fs["labels"] >> _labels;
}
// See FaceRecognizer::save.
@ -447,7 +435,7 @@ void Fisherfaces::save(FileStorage& fs) const {
fs << "eigenvectors" << _eigenvectors;
// write sequences
writeFileNodeList(fs, "projections", _projections);
writeFileNodeList(fs, "labels", _labels);
fs << "labels" << _labels;
}
//------------------------------------------------------------------------------
// LBPH
@ -630,7 +618,7 @@ void LBPH::load(const FileStorage& fs) {
fs["grid_y"] >> _grid_y;
//read matrices
readFileNodeList(fs["histograms"], _histograms);
readFileNodeList(fs["labels"], _labels);
fs["labels"] >> _labels;
}
// See FaceRecognizer::save.
@ -641,7 +629,7 @@ void LBPH::save(FileStorage& fs) const {
fs << "grid_y" << _grid_y;
// write matrices
writeFileNodeList(fs, "histograms", _histograms);
writeFileNodeList(fs, "labels", _labels);
fs << "labels" << _labels;
}
void LBPH::train(InputArray _src, InputArray _lbls) {
@ -651,11 +639,12 @@ void LBPH::train(InputArray _src, InputArray _lbls) {
vector<Mat> src;
_src.getMatVector(src);
// turn the label matrix into a vector
vector<int> labels = _lbls.getMat();
if(labels.size() != src.size())
Mat labels = _lbls.getMat();
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
if(labels.total() != src.size())
CV_Error(CV_StsUnsupportedFormat, "The number of labels must equal the number of samples.");
// store given labels
_labels = labels;
labels.copyTo(_labels);
// store the spatial histograms of the original data
for(size_t sampleIdx = 0; sampleIdx < src.size(); sampleIdx++) {
// calculate lbp image
@ -690,7 +679,7 @@ int LBPH::predict(InputArray _src) const {
double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR);
if(dist < minDist) {
minDist = dist;
minClass = _labels[sampleIdx];
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
@ -712,5 +701,35 @@ Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
{
return new LBPH(radius, neighbors, grid_x, grid_y);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
obj.info()->addParam(obj, "eigenvalues", obj._eigenvalues, true);
obj.info()->addParam(obj, "mean", obj._mean, true));
CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
obj.info()->addParam(obj, "radius", obj._radius);
obj.info()->addParam(obj, "neighbors", obj._neighbors);
obj.info()->addParam(obj, "grid_x", obj._grid_x);
obj.info()->addParam(obj, "grid_y", obj._grid_y);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));
bool initModule_contrib()
{
Ptr<Algorithm> efaces = createEigenfaces(), ffaces = createFisherfaces(), lbph = createLBPH();
return efaces->info() != 0 && ffaces->info() != 0 && lbph->info() != 0;
}
}