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odu_finder.h

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#include <fstream>
#include <iostream>
#include <list>
#include <opencv/cv.h>
#include <opencv/highgui.h>
#include <Eigen/StdVector>

#include <vocabulary_tree/simple_kmeans.h>
#include <vocabulary_tree/vocabulary_tree.h>
#include <vocabulary_tree/database.h>
#include <vocabulary_tree/tree_builder.h>
#include <vocabulary_tree/simple_kmeans.h>

#include <siftfast/siftfast.h>

#include <common.h>
using namespace odu_finder;

typedef Eigen::Matrix<float, 1, 128> Feature;
typedef std::vector<Feature, Eigen::aligned_allocator<Feature> > FeatureVector;

const int COLORS = 13;

class DocumentInfo
{
private:
        bool delete_document;
public:
        vt::Document* document;
        std::string name;
        DocumentInfo();
        DocumentInfo(vt::Document* document, std::string& name);
        ~DocumentInfo();
        void write (std::ostream& out);
        void read(std::istream& in);
};

class ODUFinder
{
public:
        enum VisualizationMode { FRAMES, SEQUENCES };

        // LOGGING
        std::ofstream logger;
  bool enable_logging_;
        int frame_number;
        std::map<std::string, int> stat_summary_map;
    bool extract_roi_;

        // VISUALIZATION
        IplImage *camera_image, *template_image, *image ,*image_roi;
        CvScalar color_table[COLORS];
        std::vector<unsigned int> cluster_sizes;
        std::string output_image_topic_;
        VisualizationMode visualization_mode_;
        std::vector<std::string> sequence_buffer;

        // DATABASE
        std::vector<vt::Document> docs;
        vt::TreeBuilder<Feature> tree_builder;
        vt::VocabularyTree<Feature> tree;
        vt::Database* db;
        std::vector<std::string> image_names;
        std::map<int, DocumentInfo*> documents_map;

        // RECOGNITION
        size_t camera_keypoints_count;
    //map of DocumentIDs and their scores in the database
        std::map<uint32_t, float> matches_map;
        std::list<int> sliding_window;
        std::map<int, int> last_templates;

        // PARAMETERS
        std::string command, database_location, images_directory, images_for_visualization_directory;
        int votes_count, tree_k, tree_levels, min_cluster_size, object_id;
        double unknown_object_threshold;
        int enable_clustering, enable_incremental_learning, enable_visualization, sliding_window_size, templates_to_show;
        double radius_adaptation_r_min, radius_adaptation_r_max, radius_adaptation_K, radius_adaptation_A;
        int count_templates;
public:
        ODUFinder();

        ~ODUFinder();
  
  void set_visualization(bool enable_visualization_in);
 
  std::string process_image(IplImage* camera_image);
  
        int start();

        void build_database(std::string directory);

        void process_images(std::string directory);

        void save_database_without_tree(std::string& directory);

        void save_database(std::string& directory);

        void load_database(std::string& directory);
  
        void add_image_to_database(vt::Document& doc, std::string& name);
  
        void write_stat_summary();

        void extract_roi (IplImage *image , std::vector<KeypointExt*> camera_keypoints);


protected:
        void trace_directory(const char* dir, const char* prefix, std::vector<FeatureVector>& images, bool onlySaveImages = false);

        void visualize(IplImage *camera_image_in, DocumentInfo** template_document_info, std::vector<KeypointExt*> *camera_keypoints);

        void save_result_for_sequence(std::string &best_template_filename);

        void visualize_sequence();

        void clear_sequence_buffer();

 
        void update_matches_map(vt::Matches& matches, size_t size);

        void process_file(std::string& filename, std::vector<FeatureVector>& images, bool onlySaveImages = false);

        Keypoint extract_keypoints(IplImage *image, bool frames_only = false);
};

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objects_of_daily_use_finder
Author(s): Vladimir Haltakov, Dejan Pangercic
autogenerated on Sun Nov 25 18:50:06 2012