added face recognition algorithms, LDA, colormaps (all by Philipp Wagner)

modules/contrib/include/opencv2/contrib/contrib.hpp

@@ -845,6 +845,131 @@ namespace cv
         bool get_uv(double x, double y, int&u, int&v);
         void create_map(int M, int N, int R, int S, double ro0, double smin);
     };
+    
+    CV_EXPORTS Mat subspaceProject(InputArray W, InputArray mean, InputArray src);
+    CV_EXPORTS Mat subspaceReconstruct(InputArray W, InputArray mean, InputArray src);
+    
+    class CV_EXPORTS LDA
+    {
+    public:
+        // Initializes a LDA with num_components (default 0) and specifies how
+        // samples are aligned (default dataAsRow=true).
+        LDA(int num_components = 0) :
+            _num_components(num_components) {};
+
+        // Initializes and performs a Discriminant Analysis with Fisher's
+        // Optimization Criterion on given data in src and corresponding labels
+        // in labels. If 0 (or less) number of components are given, they are
+        // automatically determined for given data in computation.
+        LDA(const Mat& src, vector<int> labels,
+                int num_components = 0) :
+                    _num_components(num_components)
+        {
+            this->compute(src, labels); //! compute eigenvectors and eigenvalues
+        }
+
+        // Initializes and performs a Discriminant Analysis with Fisher's
+        // Optimization Criterion on given data in src and corresponding labels
+        // in labels. If 0 (or less) number of components are given, they are
+        // automatically determined for given data in computation.
+        LDA(InputArray src, InputArray labels,
+                int num_components = 0) :
+                    _num_components(num_components)
+        {
+            this->compute(src, labels); //! compute eigenvectors and eigenvalues
+        }
+
+        // Serializes this object to a given filename.
+        void save(const string& filename) const;
+
+        // Deserializes this object from a given filename.
+        void load(const string& filename);
+
+        // Serializes this object to a given cv::FileStorage.
+        void save(FileStorage& fs) const;
+
+            // Deserializes this object from a given cv::FileStorage.
+        void load(const FileStorage& node);
+
+        // Destructor.
+        ~LDA() {}
+
+        //! Compute the discriminants for data in src and labels.
+        void compute(InputArray src, InputArray labels);
+
+        // Projects samples into the LDA subspace.
+        Mat project(InputArray src);
+
+        // Reconstructs projections from the LDA subspace.
+        Mat reconstruct(InputArray src);
+
+        // Returns the eigenvectors of this LDA.
+        Mat eigenvectors() const { return _eigenvectors; };
+
+        // Returns the eigenvalues of this LDA.
+        Mat eigenvalues() const { return _eigenvalues; }
+        
+    protected:
+        bool _dataAsRow;
+        int _num_components;
+        Mat _eigenvectors;
+        Mat _eigenvalues;
+
+        void lda(InputArray src, InputArray labels);
+    };
+    
+    class CV_EXPORTS FaceRecognizer
+    {
+    public:
+        //! virtual destructor
+        virtual ~FaceRecognizer() {}
+
+        // Trains a FaceRecognizer.
+        virtual void train(InputArray src, InputArray labels) = 0;
+
+        // Gets a prediction from a FaceRecognizer.
+        virtual int predict(InputArray src) const = 0;
+
+        // Serializes this object to a given filename.
+        virtual void save(const string& filename) const;
+
+        // Deserializes this object from a given filename.
+        virtual void load(const string& filename);
+
+        // Serializes this object to a given cv::FileStorage.
+        virtual void save(FileStorage& fs) const = 0;
+
+        // Deserializes this object from a given cv::FileStorage.
+        virtual void load(const FileStorage& fs) = 0;
+        
+        // Returns eigenvectors (if any)
+        virtual Mat eigenvectors() const { return Mat(); }
+    };
+    
+    CV_EXPORTS Ptr<FaceRecognizer> createEigenFaceRecognizer(int num_components = 0);
+    CV_EXPORTS Ptr<FaceRecognizer> createFisherFaceRecognizer(int num_components = 0);
+    CV_EXPORTS Ptr<FaceRecognizer> createLBPHFaceRecognizer(int radius=1, int neighbors=8,
+                                                            int grid_x=8, int grid_y=8);
+    
+    enum
+    {
+        COLORMAP_AUTUMN = 0,
+        COLORMAP_BONE = 1,
+        COLORMAP_JET = 2,
+        COLORMAP_WINTER = 3,
+        COLORMAP_RAINBOW = 4,
+        COLORMAP_OCEAN = 5,
+        COLORMAP_SUMMER = 6,
+        COLORMAP_SPRING = 7,
+        COLORMAP_COOL = 8,
+        COLORMAP_HSV = 9,
+        COLORMAP_PINK = 10,
+        COLORMAP_HOT = 11,
+        COLORMAP_MKPJ1 = 12,
+        COLORMAP_MKPJ2 = 13
+    };
+    
+    CV_EXPORTS void applyColorMap(InputArray src, OutputArray dst, int colormap);
 }
 
 

samples/cpp/facerec_demo.cpp

+/*
+ * Copyright (c) 2011. Philipp Wagner <bytefish[at]gmx[dot]de>.
+ * Released to public domain under terms of the BSD Simplified license.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *   * Redistributions of source code must retain the above copyright
+ *     notice, this list of conditions and the following disclaimer.
+ *   * Redistributions in binary form must reproduce the above copyright
+ *     notice, this list of conditions and the following disclaimer in the
+ *     documentation and/or other materials provided with the distribution.
+ *   * Neither the name of the organization nor the names of its contributors
+ *     may be used to endorse or promote products derived from this software
+ *     without specific prior written permission.
+ *
+ *   See <http://www.opensource.org/licenses/bsd-license>
+ */
+
+#include "opencv2/opencv.hpp"
+
+#include <iostream>
+#include <fstream>
+#include <sstream>
+
+using namespace cv;
+using namespace std;
+
+void read_csv(const string& filename, vector<Mat>& images, vector<int>& labels, char separator = ';') {
+    std::ifstream file(filename.c_str(), ifstream::in);
+    if (!file)
+        throw std::exception();
+    string line, path, classlabel;
+    while (getline(file, line)) {
+        stringstream liness(line);
+        getline(liness, path, separator);
+        getline(liness, classlabel);
+        images.push_back(imread(path, 0));
+        labels.push_back(atoi(classlabel.c_str()));
+    }
+}
+
+int main(int argc, const char *argv[]) {
+    // check for command line arguments
+    if (argc != 2) {
+        cout << "usage: " << argv[0] << " <csv.ext>" << endl;
+        exit(1);
+    }
+    // path to your CSV
+    string fn_csv = string(argv[1]);
+    // images and corresponding labels
+    vector<Mat> images;
+    vector<int> labels;
+    // read in the data
+    try {
+        read_csv(fn_csv, images, labels);
+    } catch (exception& e) {
+        cerr << "Error opening file \"" << fn_csv << "\"." << endl;
+        exit(1);
+    }
+    // get width and height
+    //int width = images[0].cols;
+    int height = images[0].rows;
+    // get test instances
+    Mat testSample = images[images.size() - 1];
+    int testLabel = labels[labels.size() - 1];
+    // ... and delete last element
+    images.pop_back();
+    labels.pop_back();
+    // build the Fisherfaces model
+    Ptr<FaceRecognizer> model = createFisherFaceRecognizer();
+    model->train(images, labels);
+    // test model
+    int predicted = model->predict(testSample);
+    cout << "predicted class = " << predicted << endl;
+    cout << "actual class = " << testLabel << endl;
+    // get the eigenvectors
+    Mat W = model->eigenvectors();
+    // show first 10 fisherfaces
+    for (int i = 0; i < min(10, W.cols); i++) {
+        // get eigenvector #i
+        Mat ev = W.col(i).clone();
+        // reshape to original site
+        Mat grayscale, cgrayscale;
+        cvtColor(ev.reshape(1, height), grayscale, COLOR_BGR2GRAY);
+        // show image (with Jet colormap)
+        applyColorMap(grayscale, cgrayscale, COLORMAP_JET);
+        imshow(format("%d", i), cgrayscale);
+    }
+    waitKey(0);
+    return 0;
+}