generic-library/opencv
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Updated the train method to be an InputArrayOfArrays, so the Python wrapper now generates the correct code (and it works).

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This commit is contained in:
Philipp Wagner 2012-07-29 23:51:12 +02:00
parent b2deab2175
commit f29d73fe86
2 changed files with 10 additions and 11 deletions
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2
modules/contrib/include/opencv2/contrib/contrib.hpp
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@ -925,7 +925,7 @@ namespace cv
virtual ~FaceRecognizer() {} virtual ~FaceRecognizer() {}
// Trains a FaceRecognizer. // Trains a FaceRecognizer.
CV_WRAP virtual void train(InputArray src, InputArray labels) = 0; CV_WRAP virtual void train(InputArrayOfArrays src, InputArray labels) = 0;
// Gets a prediction from a FaceRecognizer. // Gets a prediction from a FaceRecognizer.
virtual int predict(InputArray src) const = 0; virtual int predict(InputArray src) const = 0;

19
modules/contrib/src/facerec.cpp
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@ -124,7 +124,7 @@ public:
// Initializes and computes an Eigenfaces model with images in src and // Initializes and computes an Eigenfaces model with images in src and
// corresponding labels in labels. num_components will be kept for // corresponding labels in labels. num_components will be kept for
// classification. // classification.
Eigenfaces(InputArray src, InputArray labels, Eigenfaces(InputArrayOfArrays src, InputArray labels,
int num_components = 0, double threshold = DBL_MAX) : int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components), _num_components(num_components),
_threshold(threshold) { _threshold(threshold) {
@ -133,7 +133,7 @@ public:
// Computes an Eigenfaces model with images in src and corresponding labels // Computes an Eigenfaces model with images in src and corresponding labels
// in labels. // in labels.
void train(InputArray src, InputArray labels); void train(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src. // Predicts the label of a query image in src.
int predict(InputArray src) const; int predict(InputArray src) const;
@ -177,7 +177,7 @@ public:
// Initializes and computes a Fisherfaces model with images in src and // Initializes and computes a Fisherfaces model with images in src and
// corresponding labels in labels. num_components will be kept for // corresponding labels in labels. num_components will be kept for
// classification. // classification.
Fisherfaces(InputArray src, InputArray labels, Fisherfaces(InputArrayOfArrays src, InputArray labels,
int num_components = 0, double threshold = DBL_MAX) : int num_components = 0, double threshold = DBL_MAX) :
_num_components(num_components), _num_components(num_components),
_threshold(threshold) { _threshold(threshold) {
@ -188,7 +188,7 @@ public:
// Computes a Fisherfaces model with images in src and corresponding labels // Computes a Fisherfaces model with images in src and corresponding labels
// in labels. // in labels.
void train(InputArray src, InputArray labels); void train(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src. // Predicts the label of a query image in src.
int predict(InputArray src) const; int predict(InputArray src) const;
@ -246,7 +246,7 @@ public:
// //
// (radius=1), (neighbors=8) are used in the local binary patterns creation. // (radius=1), (neighbors=8) are used in the local binary patterns creation.
// (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms. // (grid_x=8), (grid_y=8) controls the grid size of the spatial histograms.
LBPH(InputArray src, LBPH(InputArrayOfArrays src,
InputArray labels, InputArray labels,
int radius_=1, int neighbors_=8, int radius_=1, int neighbors_=8,
int gridx=8, int gridy=8, int gridx=8, int gridy=8,
@ -263,7 +263,7 @@ public:
// Computes a LBPH model with images in src and // Computes a LBPH model with images in src and
// corresponding labels in labels. // corresponding labels in labels.
void train(InputArray src, InputArray labels); void train(InputArrayOfArrays src, InputArray labels);
// Predicts the label of a query image in src. // Predicts the label of a query image in src.
int predict(InputArray src) const; int predict(InputArray src) const;
@ -306,11 +306,10 @@ void FaceRecognizer::load(const string& filename) {
fs.release(); fs.release();
} }
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
// Eigenfaces // Eigenfaces
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
void Eigenfaces::train(InputArray _src, InputArray _local_labels) { void Eigenfaces::train(InputArrayOfArrays _src, InputArray _local_labels) {
if(_src.total() == 0) { if(_src.total() == 0) {
string error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); string error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message); CV_Error(CV_StsBadArg, error_message);
@ -417,7 +416,7 @@ void Eigenfaces::save(FileStorage& fs) const {
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
// Fisherfaces // Fisherfaces
//------------------------------------------------------------------------------ //------------------------------------------------------------------------------
void Fisherfaces::train(InputArray src, InputArray _lbls) { void Fisherfaces::train(InputArrayOfArrays src, InputArray _lbls) {
if(src.total() == 0) { if(src.total() == 0) {
string error_message = format("Empty training data was given. You'll need more than one sample to learn a model."); string error_message = format("Empty training data was given. You'll need more than one sample to learn a model.");
CV_Error(CV_StsBadArg, error_message); CV_Error(CV_StsBadArg, error_message);
@ -728,7 +727,7 @@ void LBPH::save(FileStorage& fs) const {
fs << "labels" << _labels; fs << "labels" << _labels;
} }
void LBPH::train(InputArray _src, InputArray _lbls) { void LBPH::train(InputArrayOfArrays _src, InputArray _lbls) {
if(_src.kind() != _InputArray::STD_VECTOR_MAT && _src.kind() != _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<...> >)."; 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<...> >).";
CV_Error(CV_StsBadArg, error_message); CV_Error(CV_StsBadArg, error_message);