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Exceptions now go into CV_Error. Added thresholding to the FaceRecognizer and updated the demo accordingly.

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This commit is contained in:
Philipp Wagner 2012-06-10 22:23:18 +00:00
parent ee1b671279
commit cd7d93f362
4 changed files with 155 additions and 88 deletions
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9
modules/contrib/include/opencv2/contrib/contrib.hpp
View File

@ -930,6 +930,9 @@ namespace cv
// Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0;
// Predicts the label and confidence for a given sample.
virtual void predict(InputArray src, int &label, double &dist) const = 0;
// Serializes this object to a given filename.
virtual void save(const string& filename) const;
@ -944,10 +947,10 @@ namespace cv
};
CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0);
CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
CV_EXPORTS Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8);
int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
enum
{

150
modules/contrib/src/facerec.cpp
View File

@ -52,7 +52,7 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0) {
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
string error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).";
string error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).";
error(Exception(CV_StsBadArg, error_message, "asRowMatrix", __FILE__, __LINE__));
}
// number of samples
@ -105,6 +105,7 @@ class Eigenfaces : public FaceRecognizer
{
private:
int _num_components;
double _threshold;
vector<Mat> _projections;
Mat _labels;
Mat _eigenvectors;
@ -116,15 +117,18 @@ public:
using FaceRecognizer::load;
// Initializes an empty Eigenfaces model.
Eigenfaces(int num_components = 0) :
_num_components(num_components) { }
Eigenfaces(int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {}
// Initializes and computes an Eigenfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Eigenfaces(InputArray src, InputArray labels,
int num_components = 0) :
_num_components(num_components) {
int num_components = 0,
double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {
train(src, labels);
}
@ -135,6 +139,9 @@ public:
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
@ -152,6 +159,7 @@ class Fisherfaces: public FaceRecognizer
{
private:
int _num_components;
double _threshold;
Mat _eigenvectors;
Mat _eigenvalues;
Mat _mean;
@ -163,16 +171,19 @@ public:
using FaceRecognizer::load;
// Initializes an empty Fisherfaces model.
Fisherfaces(int num_components = 0) :
_num_components(num_components) {}
Fisherfaces(int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {}
// Initializes and computes a Fisherfaces model with images in src and
// corresponding labels in labels. num_components will be kept for
// classification.
Fisherfaces(InputArray src,
InputArray labels,
int num_components = 0) :
_num_components(num_components) {
int num_components = 0,
double threshold = DBL_MAX) :
_num_components(num_components),
_threshold(threshold) {
train(src, labels);
}
@ -185,6 +196,9 @@ public:
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
virtual void load(const FileStorage& fs);
@ -207,6 +221,7 @@ private:
int _grid_y;
int _radius;
int _neighbors;
double _threshold;
vector<Mat> _histograms;
Mat _labels;
@ -220,11 +235,12 @@ public:
//
// radius, neighbors are used in the local binary patterns creation.
// grid_x, grid_y control the grid size of the spatial histograms.
LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8) :
LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {}
_neighbors(neighbors),
_threshold(threshold) {}
// Initializes and computes this LBPH Model. The current implementation is
// rather fixed as it uses the Extended Local Binary Patterns per default.
@ -234,11 +250,13 @@ public:
LBPH(InputArray src,
InputArray labels,
int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8) :
int grid_x=8, int grid_y=8,
double threshold = DBL_MAX) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {
_neighbors(neighbors),
_threshold(threshold) {
train(src, labels);
}
@ -251,6 +269,9 @@ public:
// Predicts the label of a query image in src.
int predict(InputArray src) const;
// Predicts the label and confidence for a given sample.
void predict(InputArray _src, int &label, double &dist) const;
// See FaceRecognizer::load.
void load(const FileStorage& fs);
@ -293,10 +314,10 @@ void FaceRecognizer::load(const string& filename) {
void Eigenfaces::train(InputArray _src, InputArray _local_labels) {
if(_src.total() == 0) {
string error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
error(Exception(CV_StsUnsupportedFormat, error_message, "Eigenfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
} else if(_local_labels.getMat().type() != CV_32SC1) {
string error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _local_labels.type());
error(Exception(CV_StsUnsupportedFormat, error_message, "Eigenfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// get labels
Mat labels = _local_labels.getMat();
@ -307,7 +328,7 @@ void Eigenfaces::train(InputArray _src, InputArray _local_labels) {
// assert there are as much samples as labels
if(static_cast<int>(labels.total()) != n) {
string error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", n, labels.total());
error(Exception(CV_StsBadArg, error_message, "Eigenfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// clip number of components to be valid
if((_num_components <= 0) || (_num_components > n))
@ -326,31 +347,37 @@ void Eigenfaces::train(InputArray _src, InputArray _local_labels) {
}
}
int Eigenfaces::predict(InputArray _src) const {
void Eigenfaces::predict(InputArray _src, int &minClass, double &minDist) const {
// get data
Mat src = _src.getMat();
// make sure the user is passing correct data
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
string error_message = "This Eigenfaces model is not computed yet. Did you call Eigenfaces::train?";
error(cv::Exception(CV_StsError, error_message, "Eigenfaces::predict", __FILE__, __LINE__));
CV_Error(CV_StsError, error_message);
} else if(_eigenvectors.rows != static_cast<int>(src.total())) {
// check data alignment just for clearer exception messages
string error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
error(cv::Exception(CV_StsError, error_message, "Eigenfaces::predict", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
double minDist = DBL_MAX;
int minClass = -1;
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
}
int Eigenfaces::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
void Eigenfaces::load(const FileStorage& fs) {
@ -381,10 +408,10 @@ void Eigenfaces::save(FileStorage& fs) const {
void Fisherfaces::train(InputArray src, InputArray _lbls) {
if(src.total() == 0) {
string error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
error(cv::Exception(CV_StsUnsupportedFormat, error_message, "cv::Eigenfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
} else if(_lbls.getMat().type() != CV_32SC1) {
string error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type());
error(cv::Exception(CV_StsUnsupportedFormat, error_message, "cv::Fisherfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// get data
Mat labels = _lbls.getMat();
@ -393,11 +420,11 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
int N = data.rows;
// make sure labels are passed in correct shape
if(labels.total() != (size_t) N) {
string error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
error(cv::Exception(CV_StsBadArg, error_message, "Fisherfaces::train", __FILE__, __LINE__));
string error_message = format("The number of samples (src) must equal the number of labels (labels)! len(src)=%d, len(labels)=%d.", N, labels.total());
CV_Error(CV_StsBadArg, error_message);
} else if(labels.rows != 1 && labels.cols != 1) {
string error_message = format("Expected the labels in a matrix with one row or column! Given dimensions are rows=%s, cols=%d.", labels.rows, labels.cols);
error(cv::Exception(CV_StsBadArg, error_message, "Fisherfaces::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// Get the number of unique classes
// TODO Provide a cv::Mat version?
@ -427,32 +454,37 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
}
}
int Fisherfaces::predict(InputArray _src) const {
void Fisherfaces::predict(InputArray _src, int &minClass, double &minDist) const {
Mat src = _src.getMat();
// check data alignment just for clearer exception messages
if(_projections.empty()) {
// throw error if no data (or simply return -1?)
string error_message = "This Fisherfaces model is not computed yet. Did you call Fisherfaces::train?";
error(cv::Exception(CV_StsError, error_message, "Fisherfaces::predict", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
} else if(src.total() != (size_t) _eigenvectors.rows) {
string error_message = format("Wrong input image size. Reason: Training and Test images must be of equal size! Expected an image with %d elements, but got %d.", _eigenvectors.rows, src.total());
error(cv::Exception(CV_StsError, error_message, "Fisherfaces::predict", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
// find 1-nearest neighbor
double minDist = DBL_MAX;
int minClass = -1;
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _projections.size(); sampleIdx++) {
double dist = norm(_projections[sampleIdx], q, NORM_L2);
if(dist < minDist) {
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
}
int Fisherfaces::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
// See FaceRecognizer::load.
void Fisherfaces::load(const FileStorage& fs) {
@ -675,13 +707,13 @@ void LBPH::save(FileStorage& fs) const {
void LBPH::train(InputArray _src, InputArray _lbls) {
if(_src.kind() != _InputArray::STD_VECTOR_MAT && _src.kind() != _InputArray::STD_VECTOR_VECTOR) {
string error_message = "The images are expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).";
error(Exception(CV_StsBadArg, error_message, "LBPH::train", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
} else if(_src.total() == 0) {
string error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
error(Exception(CV_StsUnsupportedFormat, error_message, "LBPH::train", __FILE__, __LINE__));
CV_Error(CV_StsUnsupportedFormat, error_message);
} else if(_lbls.getMat().type() != CV_32SC1) {
string error_message = format("Labels must be given as integer (CV_32SC1). Expected %d, but was %d.", CV_32SC1, _lbls.type());
error(Exception(CV_StsUnsupportedFormat, error_message, "LBPH::train", __FILE__, __LINE__));
CV_Error(CV_StsUnsupportedFormat, error_message);
}
// get the vector of matrices
vector<Mat> src;
@ -689,8 +721,9 @@ void LBPH::train(InputArray _src, InputArray _lbls) {
// turn the label matrix into a vector
Mat labels = _lbls.getMat();
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
if(labels.total() != src.size())
if(labels.total() != src.size()) {
CV_Error(CV_StsUnsupportedFormat, "The number of labels must equal the number of samples.");
}
// store given labels
labels.copyTo(_labels);
// store the spatial histograms of the original data
@ -709,7 +742,7 @@ void LBPH::train(InputArray _src, InputArray _lbls) {
}
}
int LBPH::predict(InputArray _src) const {
void LBPH::predict(InputArray _src, int &minClass, double &minDist) const {
Mat src = _src.getMat();
// get the spatial histogram from input image
Mat lbp_image = elbp(src, _radius, _neighbors);
@ -720,37 +753,44 @@ int LBPH::predict(InputArray _src) const {
_grid_y, /* grid size y */
true /* normed histograms */);
// find 1-nearest neighbor
double minDist = DBL_MAX;
int minClass = -1;
minDist = DBL_MAX;
minClass = -1;
for(size_t sampleIdx = 0; sampleIdx < _histograms.size(); sampleIdx++) {
double dist = compareHist(_histograms[sampleIdx], query, CV_COMP_CHISQR);
if(dist < minDist) {
if((dist < minDist) && (dist < _threshold)) {
minDist = dist;
minClass = _labels.at<int>(sampleIdx);
}
}
return minClass;
}
int LBPH::predict(InputArray _src) const {
int label;
double dummy;
predict(_src, label, dummy);
return label;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
{
return new Eigenfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components)
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
{
return new Eigenfaces(num_components);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components)
{
return new Fisherfaces(num_components);
return new Fisherfaces(num_components, threshold);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y)
int grid_x, int grid_y, double threshold)
{
return new LBPH(radius, neighbors, grid_x, grid_y);
return new LBPH(radius, neighbors, grid_x, grid_y, threshold);
}
CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
@ -759,6 +799,7 @@ CV_INIT_ALGORITHM(Eigenfaces, "FaceRecognizer.Eigenfaces",
CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
obj.info()->addParam(obj, "ncomponents", obj._num_components);
obj.info()->addParam(obj, "threshold", obj._threshold);
obj.info()->addParam(obj, "projections", obj._projections, true);
obj.info()->addParam(obj, "labels", obj._labels, true);
obj.info()->addParam(obj, "eigenvectors", obj._eigenvectors, true);
@ -770,6 +811,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
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, "threshold", obj._threshold);
obj.info()->addParam(obj, "histograms", obj._histograms, true);
obj.info()->addParam(obj, "labels", obj._labels, true));

23
modules/contrib/src/lda.cpp
View File

@ -48,7 +48,7 @@ static Mat argsort(InputArray _src, bool ascending=true)
Mat src = _src.getMat();
if (src.rows != 1 && src.cols != 1) {
string error_message = "Wrong shape of input matrix! Expected a matrix with one row or column.";
error(cv::Exception(CV_StsBadArg, error_message, "argsort", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
int flags = CV_SORT_EVERY_ROW+(ascending ? CV_SORT_ASCENDING : CV_SORT_DESCENDING);
Mat sorted_indices;
@ -60,7 +60,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure the input data is a vector of matrices or vector of vector
if(src.kind() != _InputArray::STD_VECTOR_MAT && src.kind() != _InputArray::STD_VECTOR_VECTOR) {
string error_message = "The data is expected as InputArray::STD_VECTOR_MAT (a std::vector<Mat>) or _InputArray::STD_VECTOR_VECTOR (a std::vector< vector<...> >).";
error(cv::Exception(CV_StsBadArg, error_message, "asRowMatrix", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// number of samples
size_t n = src.total();
@ -76,7 +76,7 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
// make sure data can be reshaped, throw exception if not!
if(src.getMat(i).total() != d) {
string error_message = format("Wrong number of elements in matrix #%d! Expected %d was %d.", i, d, src.getMat(i).total());
error(cv::Exception(CV_StsBadArg, error_message, "cv::asRowMatrix", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// get a hold of the current row
Mat xi = data.row(i);
@ -91,8 +91,9 @@ static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
}
static void sortMatrixColumnsByIndices(InputArray _src, InputArray _indices, OutputArray _dst) {
if(_indices.getMat().type() != CV_32SC1)
if(_indices.getMat().type() != CV_32SC1) {
CV_Error(CV_StsUnsupportedFormat, "cv::sortColumnsByIndices only works on integer indices!");
}
Mat src = _src.getMat();
vector<int> indices = _indices.getMat();
_dst.create(src.rows, src.cols, src.type());
@ -183,12 +184,12 @@ Mat subspaceProject(InputArray _W, InputArray _mean, InputArray _src) {
// make sure the data has the correct shape
if(W.rows != d) {
string error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
error(cv::Exception(CV_StsBadArg, error_message, "cv::subspace::project", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// make sure mean is correct if not empty
if(!mean.empty() && (mean.total() != (size_t) d)) {
string error_message = format("Wrong mean shape for the given data matrix. Expected %d, but was %d.", d, mean.total());
error(cv::Exception(CV_StsBadArg, error_message, "cv::subspace::project", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// create temporary matrices
Mat X, Y;
@ -221,12 +222,12 @@ Mat subspaceReconstruct(InputArray _W, InputArray _mean, InputArray _src)
// make sure the data has the correct shape
if(W.cols != d) {
string error_message = format("Wrong shapes for given matrices. Was size(src) = (%d,%d), size(W) = (%d,%d).", src.rows, src.cols, W.rows, W.cols);
error(cv::Exception(CV_StsBadArg, error_message, "cv::subspaceReconstruct", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// make sure mean is correct if not empty
if(!mean.empty() && (mean.total() != (size_t) W.rows)) {
string error_message = format("Wrong mean shape for the given eigenvector matrix. Expected %d, but was %d.", W.cols, mean.total());
error(cv::Exception(CV_StsBadArg, error_message, "cv::subspaceReconstruct", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// initalize temporary matrices
Mat X, Y;
@ -999,12 +1000,12 @@ void LDA::lda(InputArray _src, InputArray _lbls) {
// want to separate from each other then?
if(C == 1) {
string error_message = "At least two classes are needed to perform a LDA. Reason: Only one class was given!";
error(cv::Exception(CV_StsBadArg, error_message, "cv::LDA::lda", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// throw error if less labels, than samples
if (labels.size() != static_cast<size_t>(N)) {
string error_message = format("The number of samples must equal the number of labels. Given %d labels, %d samples. ", labels.size(), N);
error(cv::Exception(CV_StsBadArg, error_message, "LDA::lda", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
}
// warn if within-classes scatter matrix becomes singular
if (N < D) {
@ -1087,7 +1088,7 @@ void LDA::compute(InputArray _src, InputArray _lbls) {
break;
default:
string error_message= format("InputArray Datatype %d is not supported.", _src.kind());
error(cv::Exception(CV_StsBadArg, error_message, "LDA::compute", __FILE__, __LINE__));
CV_Error(CV_StsBadArg, error_message);
break;
}
}

61
samples/cpp/facerec_demo.cpp
View File

@ -41,7 +41,7 @@ static Mat toGrayscale(InputArray _src) {
static void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
std::ifstream file(filename.c_str(), ifstream::in);
if (!file) {
string error_message = "No valid input file was given, please check the given filename.";
string error_message = "No valid input file was given, please check the given filename.";
CV_Error(CV_StsBadArg, error_message);
}
string line, path, classlabel;
@ -58,7 +58,7 @@ static void read_csv(const string& filename, vector<Mat>& images, vector<int>& l
int main(int argc, const char *argv[]) {
// Check for valid command line arguments, print usage
// if no arguments were given.
// if no arguments were given.
if (argc != 2) {
cout << "usage: " << argv[0] << " <csv.ext>" << endl;
exit(1);
@ -79,8 +79,8 @@ int main(int argc, const char *argv[]) {
}
// Quit if there are not enough images for this demo.
if(images.size() <= 1) {
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
string error_message = "This demo needs at least 2 images to work. Please add more images to your data set!";
CV_Error(CV_StsError, error_message);
}
// Get the height from the first image. We'll need this
// later in code to reshape the images to their original
@ -102,30 +102,51 @@ int main(int argc, const char *argv[]) {
// 10 principal components (read Eigenfaces), then call
// the factory method like this:
//
// cv::createEigenFaceRecognizer(10);
// cv::createEigenFaceRecognizer(10);
//
// If you want to create a FaceRecognizer with a
// confidennce threshold, call it with:
//
// cv::createEigenFaceRecognizer(10, 123.0);
//
Ptr<FaceRecognizer> model = createEigenFaceRecognizer();
model->train(images, labels);
// The following line predicts the label of a given
// test image. In this example no thresholding is
// done.
int predicted = model->predict(testSample);
// Show the prediction and actual class of the given
// sample:
string result_message = format("Predicted class=%d / Actual class=%d.", predicted, testLabel);
// test image:
int predictedLabel = model->predict(testSample);
//
// To get the confidence of a prediction call it with:
//
// model with:
// int predictedLabel = -1;
// double confidence = 0.0;
// model->predict(testSample, predictedLabel, confidence);
//
string result_message = format("Predicted class = %d / Actual class = %d.", predictedLabel, testLabel);
cout << result_message << endl;
// Sometimes you'll need to get some internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// Here is how to get the eigenvalues of this Eigenfaces model:
// Sometimes you'll need to get/set internal model data,
// which isn't exposed by the public cv::FaceRecognizer.
// Since each cv::FaceRecognizer is derived from a
// cv::Algorithm, you can query the data.
//
// First we'll use it to set the threshold of the FaceRecognizer
// without retraining the model:
//
model->set("threshold", 0.0);
// Now the threshold is of this model is 0.0. A prediction
// now returns -1, as it's impossible to have a distance
// below it
//
predictedLabel = model->predict(testSample);
cout << "Predicted class = " << predictedLabel << endl;
// Now here is how to get the eigenvalues of this Eigenfaces model:
Mat eigenvalues = model->getMat("eigenvalues");
// And we can do the same to display the Eigenvectors ("Eigenfaces"):
// And we can do the same to display the Eigenvectors (read Eigenfaces):
Mat W = model->getMat("eigenvectors");
// From this we will display the (at most) first 10 Eigenfaces:
for (int i = 0; i < min(10, W.cols); i++) {
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
string msg = format("Eigenvalue #%d = %.5f", i, eigenvalues.at<double>(i));
cout << msg << endl;
// get eigenvector #i
Mat ev = W.col(i).clone();
// Reshape to original size & normalize to [0...255] for imshow.