face: refactoring of factory methods

modules/face/include/opencv2/face.hpp

@@ -318,7 +318,7 @@ public:
     The suffix const means that prediction does not affect the internal model state, so the method can
     be safely called from within different threads.
      */
-    CV_WRAP virtual void save(const String& filename) const;
+    CV_WRAP virtual void write(const String& filename) const;
 
     /** @brief Loads a FaceRecognizer and its model state.
 
@@ -327,16 +327,16 @@ public:
     FaceRecognizer::load(FileStorage& fs) in turn gets called by
     FaceRecognizer::load(const String& filename), to ease saving a model.
      */
-    CV_WRAP virtual void load(const String& filename);
+    CV_WRAP virtual void read(const String& filename);
 
     /** @overload
     Saves this model to a given FileStorage.
     @param fs The FileStorage to store this FaceRecognizer to.
     */
-    virtual void save(FileStorage& fs) const = 0;
+    virtual void write(FileStorage& fs) const = 0;
 
     /** @overload */
-    virtual void load(const FileStorage& fs) = 0;
+    virtual void read(const FileNode& fn) = 0;
 
     /** @brief Sets string info for the specified model's label.
 

modules/face/include/opencv2/face/bif.hpp

@@ -68,14 +68,14 @@ public:
      */
     CV_WRAP virtual void compute(InputArray image,
                                  OutputArray features) const = 0;
-};
 
-/**
- * @param num_bands The number of filter bands (<=8) used for computing BIF.
- * @param num_rotations The number of image rotations for computing BIF.
- * @returns Object for computing BIF.
- */
-CV_EXPORTS_W cv::Ptr<BIF> createBIF(int num_bands = 8, int num_rotations = 12);
+    /**
+     * @param num_bands The number of filter bands (<=8) used for computing BIF.
+     * @param num_rotations The number of image rotations for computing BIF.
+     * @returns Object for computing BIF.
+     */
+    CV_WRAP static Ptr<BIF> create(int num_bands = 8, int num_rotations = 12);
+};
 
 }  // namespace cv
 }  // namespace face

modules/face/include/opencv2/face/facerec.hpp

@@ -21,82 +21,106 @@ class CV_EXPORTS_W BasicFaceRecognizer : public FaceRecognizer
 {
 public:
     /** @see setNumComponents */
-    CV_WRAP virtual int getNumComponents() const = 0;
+    CV_WRAP int getNumComponents() const;
     /** @copybrief getNumComponents @see getNumComponents */
-    CV_WRAP virtual void setNumComponents(int val) = 0;
+    CV_WRAP void setNumComponents(int val);
     /** @see setThreshold */
-    CV_WRAP virtual double getThreshold() const = 0;
+    CV_WRAP double getThreshold() const;
     /** @copybrief getThreshold @see getThreshold */
-    CV_WRAP virtual void setThreshold(double val) = 0;
-    CV_WRAP virtual std::vector<cv::Mat> getProjections() const = 0;
-    CV_WRAP virtual cv::Mat getLabels() const = 0;
-    CV_WRAP virtual cv::Mat getEigenValues() const = 0;
-    CV_WRAP virtual cv::Mat getEigenVectors() const = 0;
-    CV_WRAP virtual cv::Mat getMean() const = 0;
+    CV_WRAP void setThreshold(double val);
+    CV_WRAP std::vector<cv::Mat> getProjections() const;
+    CV_WRAP cv::Mat getLabels() const;
+    CV_WRAP cv::Mat getEigenValues() const;
+    CV_WRAP cv::Mat getEigenVectors() const;
+    CV_WRAP cv::Mat getMean() const;
+
+    virtual void read(const FileNode& fn);
+    virtual void write(FileStorage& fs) const;
+
+    using FaceRecognizer::read;
+    using FaceRecognizer::write;
+
+protected:
+    int _num_components;
+    double _threshold;
+    std::vector<Mat> _projections;
+    Mat _labels;
+    Mat _eigenvectors;
+    Mat _eigenvalues;
+    Mat _mean;
+};
+
+class CV_EXPORTS_W EigenFaceRecognizer : public BasicFaceRecognizer
+{
+public:
+    /**
+    @param num_components The number of components (read: Eigenfaces) kept for this Principal
+    Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
+    kept for good reconstruction capabilities. It is based on your input data, so experiment with the
+    number. Keeping 80 components should almost always be sufficient.
+    @param threshold The threshold applied in the prediction.
+
+    ### Notes:
+
+    -   Training and prediction must be done on grayscale images, use cvtColor to convert between the
+        color spaces.
+    -   **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
+        SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
+        input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
+        the images.
+    -   This model does not support updating.
+
+    ### Model internal data:
+
+    -   num_components see createEigenFaceRecognizer.
+    -   threshold see createEigenFaceRecognizer.
+    -   eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
+    -   eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
+        eigenvalue).
+    -   mean The sample mean calculated from the training data.
+    -   projections The projections of the training data.
+    -   labels The threshold applied in the prediction. If the distance to the nearest neighbor is
+        larger than the threshold, this method returns -1.
+     */
+    CV_WRAP static Ptr<EigenFaceRecognizer> create(int num_components = 0, double threshold = DBL_MAX);
+};
+
+class CV_EXPORTS_W FisherFaceRecognizer : public BasicFaceRecognizer
+{
+public:
+    /**
+    @param num_components The number of components (read: Fisherfaces) kept for this Linear
+    Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
+    means the number of your classes c (read: subjects, persons you want to recognize). If you leave
+    this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
+    correct number (c-1) automatically.
+    @param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
+    is larger than the threshold, this method returns -1.
+
+    ### Notes:
+
+    -   Training and prediction must be done on grayscale images, use cvtColor to convert between the
+        color spaces.
+    -   **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
+        SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
+        input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
+        the images.
+    -   This model does not support updating.
+
+    ### Model internal data:
+
+    -   num_components see createFisherFaceRecognizer.
+    -   threshold see createFisherFaceRecognizer.
+    -   eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
+    -   eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their
+        eigenvalue).
+    -   mean The sample mean calculated from the training data.
+    -   projections The projections of the training data.
+    -   labels The labels corresponding to the projections.
+     */
+    CV_WRAP static Ptr<FisherFaceRecognizer> create(int num_components = 0, double threshold = DBL_MAX);
 };
 
-/**
-@param num_components The number of components (read: Eigenfaces) kept for this Principal
-Component Analysis. As a hint: There's no rule how many components (read: Eigenfaces) should be
-kept for good reconstruction capabilities. It is based on your input data, so experiment with the
-number. Keeping 80 components should almost always be sufficient.
-@param threshold The threshold applied in the prediction.
-
-### Notes:
-
--   Training and prediction must be done on grayscale images, use cvtColor to convert between the
-    color spaces.
--   **THE EIGENFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
-    SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
-    input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
-    the images.
--   This model does not support updating.
-
-### Model internal data:
-
--   num_components see createEigenFaceRecognizer.
--   threshold see createEigenFaceRecognizer.
--   eigenvalues The eigenvalues for this Principal Component Analysis (ordered descending).
--   eigenvectors The eigenvectors for this Principal Component Analysis (ordered by their
-    eigenvalue).
--   mean The sample mean calculated from the training data.
--   projections The projections of the training data.
--   labels The threshold applied in the prediction. If the distance to the nearest neighbor is
-    larger than the threshold, this method returns -1.
- */
-CV_EXPORTS_W Ptr<BasicFaceRecognizer> createEigenFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
-
-/**
-@param num_components The number of components (read: Fisherfaces) kept for this Linear
-Discriminant Analysis with the Fisherfaces criterion. It's useful to keep all components, that
-means the number of your classes c (read: subjects, persons you want to recognize). If you leave
-this at the default (0) or set it to a value less-equal 0 or greater (c-1), it will be set to the
-correct number (c-1) automatically.
-@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
-is larger than the threshold, this method returns -1.
-
-### Notes:
-
--   Training and prediction must be done on grayscale images, use cvtColor to convert between the
-    color spaces.
--   **THE FISHERFACES METHOD MAKES THE ASSUMPTION, THAT THE TRAINING AND TEST IMAGES ARE OF EQUAL
-    SIZE.** (caps-lock, because I got so many mails asking for this). You have to make sure your
-    input data has the correct shape, else a meaningful exception is thrown. Use resize to resize
-    the images.
--   This model does not support updating.
-
-### Model internal data:
-
--   num_components see createFisherFaceRecognizer.
--   threshold see createFisherFaceRecognizer.
--   eigenvalues The eigenvalues for this Linear Discriminant Analysis (ordered descending).
--   eigenvectors The eigenvectors for this Linear Discriminant Analysis (ordered by their
-    eigenvalue).
--   mean The sample mean calculated from the training data.
--   projections The projections of the training data.
--   labels The labels corresponding to the projections.
- */
-CV_EXPORTS_W Ptr<BasicFaceRecognizer> createFisherFaceRecognizer(int num_components = 0, double threshold = DBL_MAX);
 
 class CV_EXPORTS_W LBPHFaceRecognizer : public FaceRecognizer
 {
@@ -123,41 +147,41 @@ public:
     CV_WRAP virtual void setThreshold(double val) = 0;
     CV_WRAP virtual std::vector<cv::Mat> getHistograms() const = 0;
     CV_WRAP virtual cv::Mat getLabels() const = 0;
-};
 
-/**
-@param radius The radius used for building the Circular Local Binary Pattern. The greater the
-radius, the
-@param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
-appropriate value is to use `8` sample points. Keep in mind: the more sample points you include,
-the higher the computational cost.
-@param grid_x The number of cells in the horizontal direction, 8 is a common value used in
-publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
-feature vector.
-@param grid_y The number of cells in the vertical direction, 8 is a common value used in
-publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
-feature vector.
-@param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
-is larger than the threshold, this method returns -1.
-
-### Notes:
-
--   The Circular Local Binary Patterns (used in training and prediction) expect the data given as
-    grayscale images, use cvtColor to convert between the color spaces.
--   This model supports updating.
-
-### Model internal data:
-
--   radius see createLBPHFaceRecognizer.
--   neighbors see createLBPHFaceRecognizer.
--   grid_x see createLBPHFaceRecognizer.
--   grid_y see createLBPHFaceRecognizer.
--   threshold see createLBPHFaceRecognizer.
--   histograms Local Binary Patterns Histograms calculated from the given training data (empty if
-    none was given).
--   labels Labels corresponding to the calculated Local Binary Patterns Histograms.
- */
-CV_EXPORTS_W Ptr<LBPHFaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
+    /**
+    @param radius The radius used for building the Circular Local Binary Pattern. The greater the
+    radius, the
+    @param neighbors The number of sample points to build a Circular Local Binary Pattern from. An
+    appropriate value is to use `8` sample points. Keep in mind: the more sample points you include,
+    the higher the computational cost.
+    @param grid_x The number of cells in the horizontal direction, 8 is a common value used in
+    publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
+    feature vector.
+    @param grid_y The number of cells in the vertical direction, 8 is a common value used in
+    publications. The more cells, the finer the grid, the higher the dimensionality of the resulting
+    feature vector.
+    @param threshold The threshold applied in the prediction. If the distance to the nearest neighbor
+    is larger than the threshold, this method returns -1.
+
+    ### Notes:
+
+    -   The Circular Local Binary Patterns (used in training and prediction) expect the data given as
+        grayscale images, use cvtColor to convert between the color spaces.
+    -   This model supports updating.
+
+    ### Model internal data:
+
+    -   radius see createLBPHFaceRecognizer.
+    -   neighbors see createLBPHFaceRecognizer.
+    -   grid_x see createLBPHFaceRecognizer.
+    -   grid_y see createLBPHFaceRecognizer.
+    -   threshold see createLBPHFaceRecognizer.
+    -   histograms Local Binary Patterns Histograms calculated from the given training data (empty if
+        none was given).
+    -   labels Labels corresponding to the calculated Local Binary Patterns Histograms.
+     */
+    CV_WRAP static Ptr<LBPHFaceRecognizer> create(int radius=1, int neighbors=8, int grid_x=8, int grid_y=8, double threshold = DBL_MAX);
+};
 
 //! @}
 

modules/face/samples/facerec_demo.cpp

@@ -121,14 +121,14 @@ int main(int argc, const char *argv[]) {
     // 10 principal components (read Eigenfaces), then call
     // the factory method like this:
     //
-    //      cv::createEigenFaceRecognizer(10);
+    //      EigenFaceRecognizer::create(10);
     //
     // If you want to create a FaceRecognizer with a
     // confidennce threshold, call it with:
     //
-    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //      EigenFaceRecognizer::create(10, 123.0);
     //
-    Ptr<BasicFaceRecognizer> model = createEigenFaceRecognizer();
+    Ptr<EigenFaceRecognizer> model = EigenFaceRecognizer::create();
     for( int i = 0; i < nlabels; i++ )
         model->setLabelInfo(i, labelsInfo[i]);
     model->train(images, labels);

modules/face/samples/facerec_eigenfaces.cpp

@@ -115,19 +115,19 @@ int main(int argc, const char *argv[]) {
     // 10 principal components (read Eigenfaces), then call
     // the factory method like this:
     //
-    //      cv::createEigenFaceRecognizer(10);
+    //      EigenFaceRecognizer::create(10);
     //
     // If you want to create a FaceRecognizer with a
     // confidence threshold (e.g. 123.0), call it with:
     //
-    //      cv::createEigenFaceRecognizer(10, 123.0);
+    //      EigenFaceRecognizer::create(10, 123.0);
     //
     // If you want to use _all_ Eigenfaces and have a threshold,
     // then call the method like this:
     //
-    //      cv::createEigenFaceRecognizer(0, 123.0);
+    //      EigenFaceRecognizer::create(0, 123.0);
     //
-    Ptr<BasicFaceRecognizer> model = createEigenFaceRecognizer();
+    Ptr<EigenFaceRecognizer> model = EigenFaceRecognizer::create();
     model->train(images, labels);
     // The following line predicts the label of a given
     // test image:

modules/face/samples/facerec_fisherfaces.cpp

@@ -114,7 +114,7 @@ int main(int argc, const char *argv[]) {
     // If you just want to keep 10 Fisherfaces, then call
     // the factory method like this:
     //
-    //      cv::createFisherFaceRecognizer(10);
+    //      FisherFaceRecognizer::create(10);
     //
     // However it is not useful to discard Fisherfaces! Please
     // always try to use _all_ available Fisherfaces for
@@ -124,9 +124,9 @@ int main(int argc, const char *argv[]) {
     // confidence threshold (e.g. 123.0) and use _all_
     // Fisherfaces, then call it with:
     //
-    //      cv::createFisherFaceRecognizer(0, 123.0);
+    //      FisherFaceRecognizer::create(0, 123.0);
     //
-    Ptr<BasicFaceRecognizer> model = createFisherFaceRecognizer();
+    Ptr<FisherFaceRecognizer> model = FisherFaceRecognizer::create();
     model->train(images, labels);
     // The following line predicts the label of a given
     // test image:

modules/face/samples/facerec_lbph.cpp

@@ -103,7 +103,7 @@ int main(int argc, const char *argv[]) {
     //
     //      cv::createLBPHFaceRecognizer(1,8,8,8,123.0)
     //
-    Ptr<LBPHFaceRecognizer> model = createLBPHFaceRecognizer();
+    Ptr<LBPHFaceRecognizer> model = LBPHFaceRecognizer::create();
     model->train(images, labels);
     // The following line predicts the label of a given
     // test image:

modules/face/samples/facerec_save_load.cpp

@@ -127,7 +127,7 @@ int main(int argc, const char *argv[]) {
     //
     //      cv::createEigenFaceRecognizer(0, 123.0);
     //
-    Ptr<BasicFaceRecognizer> model0 = createEigenFaceRecognizer();
+    Ptr<EigenFaceRecognizer> model0 = EigenFaceRecognizer::create();
     model0->train(images, labels);
     // save the model to eigenfaces_at.yaml
     model0->save("eigenfaces_at.yml");
@@ -135,8 +135,7 @@ int main(int argc, const char *argv[]) {
     //
     // Now create a new Eigenfaces Recognizer
     //
-    Ptr<BasicFaceRecognizer> model1 = createEigenFaceRecognizer();
-    model1->load("eigenfaces_at.yml");
+    Ptr<EigenFaceRecognizer> model1 = Algorithm::load<EigenFaceRecognizer>("eigenfaces_at.yml");
     // The following line predicts the label of a given
     // test image:
     int predictedLabel = model1->predict(testSample);

modules/face/samples/facerec_video.cpp

@@ -79,7 +79,7 @@ int main(int argc, const char *argv[]) {
     int im_width = images[0].cols;
     int im_height = images[0].rows;
     // Create a FaceRecognizer and train it on the given images:
-    Ptr<BasicFaceRecognizer> model = createFisherFaceRecognizer();
+    Ptr<FisherFaceRecognizer> model = FisherFaceRecognizer::create();
     model->train(images, labels);
     // That's it for learning the Face Recognition model. You now
     // need to create the classifier for the task of Face Detection.

modules/face/src/bif.cpp

@@ -216,6 +216,6 @@ void BIFImpl::computeUnit(int unit_idx, const cv::Mat &img,
 
 }  // namespace
 
-cv::Ptr<cv::face::BIF> cv::face::createBIF(int num_bands, int num_rotations) {
+cv::Ptr<cv::face::BIF> cv::face::BIF::create(int num_bands, int num_rotations) {
     return cv::Ptr<cv::face::BIF>(new BIFImpl(num_bands, num_rotations));
 }

modules/face/src/eigen_faces.cpp

@@ -16,7 +16,8 @@
  *   See <http://www.opensource.org/licenses/bsd-license>
  */
 #include "precomp.hpp"
-#include "face_basic.hpp"
+#include <opencv2/face.hpp>
+#include "face_utils.hpp"
 #include <set>
 #include <limits>
 #include <iostream>
@@ -28,14 +29,17 @@ namespace face
 
 // Turk, M., and Pentland, A. "Eigenfaces for recognition.". Journal of
 // Cognitive Neuroscience 3 (1991), 71–86.
-class Eigenfaces : public BasicFaceRecognizerImpl
+class Eigenfaces : public EigenFaceRecognizer
 {
 
 public:
     // Initializes an empty Eigenfaces model.
     Eigenfaces(int num_components = 0, double threshold = DBL_MAX)
-        : BasicFaceRecognizerImpl(num_components, threshold)
-    {}
+        //: BasicFaceRecognizerImpl(num_components, threshold)
+    {
+        _num_components = num_components;
+        _threshold = threshold;
+    }
 
     // Computes an Eigenfaces model with images in src and corresponding labels
     // in labels.
@@ -122,7 +126,7 @@ void Eigenfaces::predict(InputArray _src, Ptr<PredictCollector> collector) const
     }
 }
 
-Ptr<BasicFaceRecognizer> createEigenFaceRecognizer(int num_components, double threshold)
+Ptr<EigenFaceRecognizer> EigenFaceRecognizer::create(int num_components, double threshold)
 {
     return makePtr<Eigenfaces>(num_components, threshold);
 }

modules/face/src/face_basic.cpp

+#include "opencv2/face.hpp"
+#include "face_utils.hpp"
+#include "precomp.hpp"
+
+using namespace cv;
+using namespace face;
+
+int BasicFaceRecognizer::getNumComponents() const
+{
+    return _num_components;
+}
+
+void BasicFaceRecognizer::setNumComponents(int val)
+{
+    _num_components = val;
+}
+
+double BasicFaceRecognizer::getThreshold() const
+{
+    return _threshold;
+}
+
+void BasicFaceRecognizer::setThreshold(double val)
+{
+    _threshold = val;
+}
+
+std::vector<cv::Mat> BasicFaceRecognizer::getProjections() const
+{
+    return _projections;
+}
+
+cv::Mat BasicFaceRecognizer::getLabels() const
+{
+    return _labels;
+}
+
+cv::Mat BasicFaceRecognizer::getEigenValues() const
+{
+    return _eigenvalues;
+}
+
+cv::Mat BasicFaceRecognizer::getEigenVectors() const
+{
+    return _eigenvectors;
+}
+
+cv::Mat BasicFaceRecognizer::getMean() const
+{
+    return _mean;
+}
+
+void BasicFaceRecognizer::read(const FileNode& fs)
+{
+    //read matrices
+    fs["num_components"] >> _num_components;
+    fs["mean"] >> _mean;
+    fs["eigenvalues"] >> _eigenvalues;
+    fs["eigenvectors"] >> _eigenvectors;
+    // read sequences
+    readFileNodeList(fs["projections"], _projections);
+    fs["labels"] >> _labels;
+    const FileNode& fn = fs["labelsInfo"];
+    if (fn.type() == FileNode::SEQ)
+    {
+       _labelsInfo.clear();
+       for (FileNodeIterator it = fn.begin(); it != fn.end();)
+       {
+           LabelInfo item;
+           it >> item;
+           _labelsInfo.insert(std::make_pair(item.label, item.value));
+       }
+    }
+}
+
+void BasicFaceRecognizer::write(FileStorage& fs) const
+{
+    // write matrices
+    fs << "num_components" << _num_components;
+    fs << "mean" << _mean;
+    fs << "eigenvalues" << _eigenvalues;
+    fs << "eigenvectors" << _eigenvectors;
+    // write sequences
+    writeFileNodeList(fs, "projections", _projections);
+    fs << "labels" << _labels;
+    fs << "labelsInfo" << "[";
+    for (std::map<int, String>::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++)
+        fs << LabelInfo(it->first, it->second);
+    fs << "]";
+}

modules/face/src/face_basic.hpp → modules/face/src/face_utils.hpp

-#ifndef __OPENCV_FACE_BASIC_HPP
-#define __OPENCV_FACE_BASIC_HPP
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
 
-#include "opencv2/face.hpp"
-#include "precomp.hpp"
+#ifndef __OPENCV_FACE_UTILS_HPP
+#define __OPENCV_FACE_UTILS_HPP
 
-#include <set>
-#include <limits>
-#include <iostream>
+#include "precomp.hpp"
 
 using namespace cv;
 
-// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
-template<typename _Tp>
-inline void readFileNodeList(const FileNode& fn, std::vector<_Tp>& result) {
-    if (fn.type() == FileNode::SEQ) {
-        for (FileNodeIterator it = fn.begin(); it != fn.end();) {
-            _Tp item;
-            it >> item;
-            result.push_back(item);
-        }
-    }
-}
-
-// Writes the a list of given items to a cv::FileStorage.
-template<typename _Tp>
-inline void writeFileNodeList(FileStorage& fs, const String& name,
-                              const std::vector<_Tp>& items) {
-    // typedefs
-    typedef typename std::vector<_Tp>::const_iterator constVecIterator;
-    // write the elements in item to fs
-    fs << name << "[";
-    for (constVecIterator it = items.begin(); it != items.end(); ++it) {
-        fs << *it;
-    }
-    fs << "]";
-}
-
 inline 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) {
@@ -70,6 +43,32 @@ inline Mat asRowMatrix(InputArrayOfArrays src, int rtype, double alpha=1, double
     return data;
 }
 
+// Reads a sequence from a FileNode::SEQ with type _Tp into a result vector.
+template<typename _Tp>
+inline void readFileNodeList(const FileNode& fn, std::vector<_Tp>& result) {
+    if (fn.type() == FileNode::SEQ) {
+        for (FileNodeIterator it = fn.begin(); it != fn.end();) {
+            _Tp item;
+            it >> item;
+            result.push_back(item);
+        }
+    }
+}
+
+// Writes the a list of given items to a cv::FileStorage.
+template<typename _Tp>
+inline void writeFileNodeList(FileStorage& fs, const String& name,
+                              const std::vector<_Tp>& items) {
+    // typedefs
+    typedef typename std::vector<_Tp>::const_iterator constVecIterator;
+    // write the elements in item to fs
+    fs << name << "[";
+    for (constVecIterator it = items.begin(); it != items.end(); ++it) {
+        fs << *it;
+    }
+    fs << "]";
+}
+
 // Utility structure to load/save face label info (a pair of int and string) via FileStorage
 struct LabelInfo
 {
@@ -106,70 +105,4 @@ inline void read(const cv::FileNode& node, LabelInfo& x, const LabelInfo& defaul
         x.read(node);
 }
 
-
-class BasicFaceRecognizerImpl : public cv::face::BasicFaceRecognizer
-{
-public:
-    BasicFaceRecognizerImpl(int num_components = 0, double threshold = DBL_MAX)
-        : _num_components(num_components), _threshold(threshold)
-    {}
-
-    void load(const FileStorage& fs)
-    {
-        //read matrices
-        fs["num_components"] >> _num_components;
-        fs["mean"] >> _mean;
-        fs["eigenvalues"] >> _eigenvalues;
-        fs["eigenvectors"] >> _eigenvectors;
-        // read sequences
-        readFileNodeList(fs["projections"], _projections);
-        fs["labels"] >> _labels;
-        const FileNode& fn = fs["labelsInfo"];
-        if (fn.type() == FileNode::SEQ)
-        {
-           _labelsInfo.clear();
-           for (FileNodeIterator it = fn.begin(); it != fn.end();)
-           {
-               LabelInfo item;
-               it >> item;
-               _labelsInfo.insert(std::make_pair(item.label, item.value));
-           }
-        }
-    }
-
-    void save(FileStorage& fs) const
-    {
-        // write matrices
-        fs << "num_components" << _num_components;
-        fs << "mean" << _mean;
-        fs << "eigenvalues" << _eigenvalues;
-        fs << "eigenvectors" << _eigenvectors;
-        // write sequences
-        writeFileNodeList(fs, "projections", _projections);
-        fs << "labels" << _labels;
-        fs << "labelsInfo" << "[";
-        for (std::map<int, String>::const_iterator it = _labelsInfo.begin(); it != _labelsInfo.end(); it++)
-            fs << LabelInfo(it->first, it->second);
-        fs << "]";
-    }
-
-    CV_IMPL_PROPERTY(int, NumComponents, _num_components)
-    CV_IMPL_PROPERTY(double, Threshold, _threshold)
-    CV_IMPL_PROPERTY_RO(std::vector<cv::Mat>, Projections, _projections)
-    CV_IMPL_PROPERTY_RO(cv::Mat, Labels, _labels)
-    CV_IMPL_PROPERTY_RO(cv::Mat, EigenValues, _eigenvalues)
-    CV_IMPL_PROPERTY_RO(cv::Mat, EigenVectors, _eigenvectors)
-    CV_IMPL_PROPERTY_RO(cv::Mat, Mean, _mean)
-
-protected:
-    int _num_components;
-    double _threshold;
-    std::vector<Mat> _projections;
-    Mat _labels;
-    Mat _eigenvectors;
-    Mat _eigenvalues;
-    Mat _mean;
-};
-
-#endif // __OPENCV_FACE_BASIC_HPP
-
+#endif

modules/face/src/facerec.cpp

@@ -54,21 +54,21 @@ void FaceRecognizer::update(InputArrayOfArrays src, InputArray labels)
     CV_Error(Error::StsNotImplemented, error_msg);
 }
 
-void FaceRecognizer::load(const String &filename)
+void FaceRecognizer::read(const String &filename)
 {
     FileStorage fs(filename, FileStorage::READ);
     if (!fs.isOpened())
         CV_Error(Error::StsError, "File can't be opened for reading!");
-    this->load(fs);
+    this->read(fs.root());
     fs.release();
 }
 
-void FaceRecognizer::save(const String &filename) const
+void FaceRecognizer::write(const String &filename) const
 {
     FileStorage fs(filename, FileStorage::WRITE);
     if (!fs.isOpened())
         CV_Error(Error::StsError, "File can't be opened for writing!");
-    this->save(fs);
+    this->write(fs);
     fs.release();
 }
 

modules/face/src/fisher_faces.cpp

@@ -16,7 +16,8 @@
  *   See <http://www.opensource.org/licenses/bsd-license>
  */
 #include "precomp.hpp"
-#include "face_basic.hpp"
+#include <opencv2/face.hpp>
+#include "face_utils.hpp"
 
 namespace cv { namespace face {
 
@@ -24,13 +25,16 @@ namespace cv { namespace face {
 // faces: Recognition using class specific linear projection.". IEEE
 // Transactions on Pattern Analysis and Machine Intelligence 19, 7 (1997),
 // 711–720.
-class Fisherfaces: public BasicFaceRecognizerImpl
+class Fisherfaces: public FisherFaceRecognizer
 {
 public:
     // Initializes an empty Fisherfaces model.
     Fisherfaces(int num_components = 0, double threshold = DBL_MAX)
-        : BasicFaceRecognizerImpl(num_components, threshold)
-    { }
+        //: BasicFaceRecognizer(num_components, threshold)
+    {
+        _num_components = num_components;
+        _threshold = threshold;
+    }
 
     // Computes a Fisherfaces model with images in src and corresponding labels
     // in labels.
@@ -142,7 +146,7 @@ void Fisherfaces::predict(InputArray _src, Ptr<PredictCollector> collector) cons
     }
 }
 
-Ptr<BasicFaceRecognizer> createFisherFaceRecognizer(int num_components, double threshold)
+Ptr<FisherFaceRecognizer> FisherFaceRecognizer::create(int num_components, double threshold)
 {
     return makePtr<Fisherfaces>(num_components, threshold);
 }

modules/face/src/lbph_faces.cpp

@@ -17,7 +17,7 @@
  */
 #include "precomp.hpp"
 #include "opencv2/face.hpp"
-#include "face_basic.hpp"
+#include "face_utils.hpp"
 
 namespace cv { namespace face {
 
@@ -46,8 +46,8 @@ private:
 
 
 public:
-    using FaceRecognizer::save;
-    using FaceRecognizer::load;
+    using FaceRecognizer::read;
+    using FaceRecognizer::write;
 
     // Initializes this LBPH Model. The current implementation is rather fixed
     // as it uses the Extended Local Binary Patterns per default.
@@ -94,11 +94,11 @@ public:
     // Send all predict results to caller side for custom result handling
     void predict(InputArray src, Ptr<PredictCollector> collector) const;
 
-    // See FaceRecognizer::load.
-    void load(const FileStorage& fs);
+    // See FaceRecognizer::write.
+    void read(const FileNode& fn);
 
     // See FaceRecognizer::save.
-    void save(FileStorage& fs) const;
+    void write(FileStorage& fs) const;
 
     CV_IMPL_PROPERTY(int, GridX, _grid_x)
     CV_IMPL_PROPERTY(int, GridY, _grid_y)
@@ -110,7 +110,7 @@ public:
 };
 
 
-void LBPH::load(const FileStorage& fs) {
+void LBPH::read(const FileNode& fs) {
     fs["radius"] >> _radius;
     fs["neighbors"] >> _neighbors;
     fs["grid_x"] >> _grid_x;
@@ -132,7 +132,7 @@ void LBPH::load(const FileStorage& fs) {
 }
 
 // See FaceRecognizer::save.
-void LBPH::save(FileStorage& fs) const {
+void LBPH::write(FileStorage& fs) const {
     fs << "radius" << _radius;
     fs << "neighbors" << _neighbors;
     fs << "grid_x" << _grid_x;
@@ -407,11 +407,10 @@ void LBPH::predict(InputArray _src, Ptr<PredictCollector> collector) const {
     }
 }
 
-Ptr<LBPHFaceRecognizer> createLBPHFaceRecognizer(int radius, int neighbors,
+Ptr<LBPHFaceRecognizer> LBPHFaceRecognizer::create(int radius, int neighbors,
                                              int grid_x, int grid_y, double threshold)
 {
     return makePtr<LBPH>(radius, neighbors, grid_x, grid_y, threshold);
 }
 
-
 }}

modules/face/src/precomp.hpp

@@ -49,10 +49,8 @@
 #include "opencv2/core/private.hpp"
 #include "opencv2/core/persistence.hpp"
 #include <map>
-#include <iostream>
 #include <set>
 #include <limits>
-#include <iostream>
 
 #endif
 

modules/face/test/test_bif.cpp

@@ -40,27 +40,27 @@ the use of this software, even if advised of the possibility of such damage.
 
 TEST(CV_Face_BIF, can_create_default) {
     cv::Ptr<cv::face::BIF> bif;
-    EXPECT_NO_THROW(bif = cv::face::createBIF());
+    EXPECT_NO_THROW(bif = cv::face::BIF::create());
     EXPECT_FALSE(bif.empty());
 }
 
 TEST(CV_Face_BIF, fails_when_zero_bands) {
-    EXPECT_ANY_THROW(cv::face::createBIF(0));
+    EXPECT_ANY_THROW(cv::face::BIF::create(0));
 }
 
 TEST(CV_Face_BIF, fails_when_too_many_bands) {
-    EXPECT_ANY_THROW(cv::face::createBIF(9));
+    EXPECT_ANY_THROW(cv::face::BIF::create(9));
 }
 
 TEST(CV_Face_BIF, fails_when_zero_rotations) {
-    EXPECT_ANY_THROW(cv::face::createBIF(8, 0));
+    EXPECT_ANY_THROW(cv::face::BIF::create(8, 0));
 }
 
 TEST(CV_Face_BIF, can_compute) {
     cv::Mat image(60, 60, CV_32F);
     cv::theRNG().fill(image, cv::RNG::UNIFORM, -1, 1);
 
-    cv::Ptr<cv::face::BIF> bif = cv::face::createBIF();
+    cv::Ptr<cv::face::BIF> bif = cv::face::BIF::create();
     cv::Mat fea;
     EXPECT_NO_THROW(bif->compute(image, fea));
     EXPECT_EQ(cv::Size(1, 13188), fea.size());