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Several exceptions added to the available FaceRecognizer classes and helper methods, so wrong input data is reported to the user. facerec_demo.cpp updated to latest cv::Algorithm changes and commented.

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This commit is contained in:
Philipp Wagner
2012-06-10 11:57:33 +00:00
parent 6727e4cb6d
commit ee1b671279
4 changed files with 282 additions and 142 deletions
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191
modules/contrib/src/facerec.cpp
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@@ -22,7 +22,7 @@ namespace cv
{
using std::set;
// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
template<typename _Tp>
inline void readFileNodeList(const FileNode& fn, vector<_Tp>& result) {
@@ -48,26 +48,42 @@ inline void writeFileNodeList(FileStorage& fs, const string& name,
}
fs << "]";
}
static Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double beta=0)
{
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<...> >).";
error(Exception(CV_StsBadArg, error_message, "asRowMatrix", __FILE__, __LINE__));
}
// number of samples
int n = (int) src.total();
// return empty matrix if no data given
size_t n = src.total();
// return empty matrix if no matrices given
if(n == 0)
return Mat();
// dimensionality of samples
int d = (int)src.getMat(0).total();
// dimensionality of (reshaped) samples
size_t d = src.getMat(0).total();
// create data matrix
Mat data(n, d, rtype);
// copy data
for(int i = 0; i < n; i++) {
// now copy data
for(unsigned int i = 0; i < n; i++) {
// 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(Exception(CV_StsBadArg, error_message, "cv::asRowMatrix", __FILE__, __LINE__));
}
// get a hold of the current row
Mat xi = data.row(i);
src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta);
// make reshape happy by cloning for non-continuous matrices
if(src.getMat(i).isContinuous()) {
src.getMat(i).reshape(1, 1).convertTo(xi, rtype, alpha, beta);
} else {
src.getMat(i).clone().reshape(1, 1).convertTo(xi, rtype, alpha, beta);
}
}
return data;
}
// Removes duplicate elements in a given vector.
template<typename _Tp>
inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
@@ -82,7 +98,7 @@ inline vector<_Tp> remove_dups(const vector<_Tp>& src) {
return elems;
}
// Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
// Cognitive Neuroscience 3 (1991), 71–86.
class Eigenfaces : public FaceRecognizer
@@ -124,10 +140,10 @@ public:
// See FaceRecognizer::save.
void save(FileStorage& fs) const;
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),
@@ -160,7 +176,7 @@ public:
train(src, labels);
}
~Fisherfaces() { }
~Fisherfaces() {}
// Computes a Fisherfaces model with images in src and corresponding labels
// in labels.
@@ -180,10 +196,6 @@ public:
// Face Recognition based on Local Binary Patterns.
//
// TODO Allow to change the distance metric.
// TODO Allow to change LBP computation (Extended LBP used right now).
// TODO Optimize, Optimize, Optimize!
//
// Ahonen T, Hadid A. and Pietikäinen M. "Face description with local binary
// patterns: Application to face recognition." IEEE Transactions on Pattern
// Analysis and Machine Intelligence, 28(12):2037-2041.
@@ -208,11 +220,11 @@ 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) :
_grid_x(grid_x_),
_grid_y(grid_y_),
_radius(radius_),
_neighbors(neighbors_) {}
LBPH(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {}
// Initializes and computes this LBPH Model. The current implementation is
// rather fixed as it uses the Extended Local Binary Patterns per default.
@@ -221,12 +233,12 @@ public:
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArray src,
InputArray labels,
int radius_=1, int neighbors_=8,
int grid_x_=8, int grid_y_=8) :
_grid_x(grid_x_),
_grid_y(grid_y_),
_radius(radius_),
_neighbors(neighbors_) {
int radius=1, int neighbors=8,
int grid_x=8, int grid_y=8) :
_grid_x(grid_x),
_grid_y(grid_y),
_radius(radius),
_neighbors(neighbors) {
train(src, labels);
}
@@ -278,22 +290,25 @@ void FaceRecognizer::load(const string& filename) {
//------------------------------------------------------------------------------
// Eigenfaces
//------------------------------------------------------------------------------
void Eigenfaces::train(InputArray src, InputArray _lbls) {
// assert type
if(_lbls.getMat().type() != CV_32SC1)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
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__));
} 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__));
}
// get labels
Mat labels = _lbls.getMat();
CV_Assert( labels.type() == CV_32S && (labels.cols == 1 || labels.rows == 1));
Mat labels = _local_labels.getMat();
// observations in row
Mat data = asRowMatrix(src, CV_64FC1);
Mat data = asRowMatrix(_src, CV_64FC1);
// number of samples
int n = data.rows;
// dimensionality of data
//int d = data.cols;
int n = data.rows;
// assert there are as much samples as labels
if((size_t)n != labels.total())
CV_Error(CV_StsBadArg, "The number of samples must equal the number of 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__));
}
// clip number of components to be valid
if((_num_components <= 0) || (_num_components > n))
_num_components = n;
@@ -307,13 +322,23 @@ void Eigenfaces::train(InputArray src, InputArray _lbls) {
// save projections
for(int sampleIdx = 0; sampleIdx < data.rows; sampleIdx++) {
Mat p = subspaceProject(_eigenvectors, _mean, data.row(sampleIdx));
this->_projections.push_back(p);
_projections.push_back(p);
}
}
int Eigenfaces::predict(InputArray _src) 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__));
} 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__));
}
// project into PCA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
double minDist = DBL_MAX;
@@ -354,25 +379,31 @@ void Eigenfaces::save(FileStorage& fs) const {
// Fisherfaces
//------------------------------------------------------------------------------
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).");
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__));
} 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__));
}
// get data
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.total() != (size_t)N)
CV_Error(CV_StsUnsupportedFormat, "Labels must be given as integer (CV_32SC1).");
// compute the Fisherfaces
// number of samples
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__));
} 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__));
}
// Get the number of unique classes
// TODO Provide a cv::Mat version?
vector<int> ll;
labels.copyTo(ll);
int C = (int)remove_dups(ll).size(); // number of unique classes
int C = (int) remove_dups(ll).size();
// clip number of components to be a valid number
if((_num_components <= 0) || (_num_components > (C-1)))
_num_components = (C-1);
@@ -398,6 +429,15 @@ void Fisherfaces::train(InputArray src, InputArray _lbls) {
int Fisherfaces::predict(InputArray _src) 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__));
} 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__));
}
// project into LDA subspace
Mat q = subspaceProject(_eigenvectors, _mean, src.reshape(1,1));
// find 1-nearest neighbor
@@ -531,7 +571,7 @@ histc_(const Mat& src, int minVal=0, int maxVal=255, bool normed=false)
// Establish the number of bins.
int histSize = maxVal-minVal+1;
// Set the ranges.
float range[] = { static_cast<float>(minVal), static_cast<float>(maxVal) };
float range[] = { static_cast<float>(minVal), static_cast<float>(maxVal+1) };
const float* histRange = { range };
// calc histogram
calcHist(&src, 1, 0, Mat(), result, 1, &histSize, &histRange, true, false);
@@ -570,7 +610,7 @@ static Mat histc(InputArray _src, int minVal, int maxVal, bool normed)
return Mat();
}
static Mat spatial_histogram(InputArray _src, int numPatterns,
int grid_x, int grid_y, bool normed)
{
@@ -602,7 +642,7 @@ static Mat spatial_histogram(InputArray _src, int numPatterns,
}
//------------------------------------------------------------------------------
// cv::elbp, cv::olbp, cv::varlbp wrapper
// wrapper to cv::elbp (extended local binary patterns)
//------------------------------------------------------------------------------
static Mat elbp(InputArray src, int radius, int neighbors) {
@@ -610,7 +650,7 @@ static Mat elbp(InputArray src, int radius, int neighbors) {
elbp(src, dst, radius, neighbors);
return dst;
}
void LBPH::load(const FileStorage& fs) {
fs["radius"] >> _radius;
fs["neighbors"] >> _neighbors;
@@ -633,8 +673,16 @@ 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)
CV_Error(CV_StsUnsupportedFormat, "LBPH::train expects InputArray::STD_VECTOR_MAT or _InputArray::STD_VECTOR_VECTOR.");
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__));
} 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__));
} 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__));
}
// get the vector of matrices
vector<Mat> src;
_src.getMatVector(src);
@@ -661,7 +709,6 @@ void LBPH::train(InputArray _src, InputArray _lbls) {
}
}
int LBPH::predict(InputArray _src) const {
Mat src = _src.getMat();
// get the spatial histogram from input image
@@ -684,24 +731,24 @@ int LBPH::predict(InputArray _src) const {
}
return minClass;
}
Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components)
{
return new Eigenfaces(num_components);
}
Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components)
{
return new Fisherfaces(num_components);
}
Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
int grid_x, int grid_y)
{
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);
@@ -716,8 +763,8 @@ CV_INIT_ALGORITHM(Fisherfaces, "FaceRecognizer.Fisherfaces",
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));
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);
@@ -725,7 +772,7 @@ CV_INIT_ALGORITHM(LBPH, "FaceRecognizer.LBPH",
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();