示例#1
0
  /*
   * Destroys annotator
   */
  TyErrorId destroy()
  {
    outInfo("destroy");

    detector.release();
    extractor.release();

    return UIMA_ERR_NONE;
  }
void App::releaseAllAlgs()
{
    mog_cpu = cv::BackgroundSubtractorMOG();
    mog_gpu.release();

    //mog2_cpu = cv::BackgroundSubtractorMOG2();
    //mog2_gpu.release();

    fgd_gpu.release();
    fgd_cpu.release();

    //gmg_gpu.release();
    //gmg_cpu.release();

    vibe_gpu.release();
}
/*****************************************************************************
 // MAIN
 */
int main(int argc, const char * argv[])
{
    
    
    
    //*************************************************************************
    // 1. Read the input files
    // This code reads the arguments from the input variable argv which is supposed to contain the
    // path of the input and reference database.
    std::string teachdb_folder, querydb_folder;
    if (argc > 2)
    {
        std::string command = argv[2];
        std::string type   = argv[1];
        if(type.compare("-SIFT")== 0)
        {
            _ftype = SIFT;
            std::cout << "NFT with SIFT feature detector and extractor." << std::endl;
        }
        else if(type.compare("-SURF")== 0)
        {
            _ftype = SURF;
            std::cout << "NFT with SURF feature detector and extractor." << std::endl;
        }
        else if(type.compare("-ORB")== 0)
        {
            _ftype = ORB;
            std::cout << "NFT with ORB feature detector and extractor." << std::endl;
        }
        
        
        if(command.compare("-file") == 0)
        {
            if(argc > 4)
            {
                teachdb_folder = argv[3];
                querydb_folder = argv[4];
                run_video = false;
            }
            else
            {
                std::cout << "No folder with query or reference images has been specified" << std::endl;
                std::cout << "Call: ./HCI571X_Feature_Matching -file folder_reference folder_query" << std::endl;
                system("pause");
                exit(0);
            }
            
        }
        else if(command.compare("-video") == 0)
        {
            run_video = true;
            if(argc > 4)
            {
                teachdb_folder = argv[3];
                device_id = atoi(argv[4]);
            }
        }
    }
    else
    {
        std::cout << "No command has been specified. use -file or -video" << std::endl;
        system("pause");
        exit(0);
    }
    
    
    
    // Read the filenames inside the teach database directory.
    std::vector<std::string> ref_filename;
    readDirFiles(teachdb_folder, &ref_filename);
    
    
    // Read the filenames inside the query database directory.
    std::vector<std::string> query_filename;
    readDirFiles(querydb_folder, &query_filename);
    
    
    //*************************************************************************
    // 2. Create a detector and a descriptor extractor
    // In this case, the SIFT detector and extractor are used
    
    // Corner detector
    if(_ftype == SIFT)_detector = new cv::SiftFeatureDetector(_num_feature, _octaves, _contrast_threshold, _edge_threshold, _sigma);
    else if(_ftype == SURF)_detector = new cv::SurfFeatureDetector( _hessianThreshold, _surf_Octaves, _surf_OctaveLayers, _surf_extended, _surf_upright );
    else if(_ftype == ORB)_detector = new cv::OrbFeatureDetector(1000);
    
    
    // Corner extractor
    if(_ftype == SIFT) _extractor = new cv::SiftDescriptorExtractor(_num_feature, _octaves, _contrast_threshold, _edge_threshold, _sigma);
    else if(_ftype == SURF) _extractor = new cv::SurfDescriptorExtractor( _hessianThreshold, _surf_Octaves, _surf_OctaveLayers, _surf_extended, _surf_upright );
    else if(_ftype == ORB)_extractor = new cv::OrbDescriptorExtractor(1000);
    

	// Check whether files are in the database list. 
	if(ref_filename.size() == 0)
	{
		std::cout << "STOP: no files in the reference database!!! Specify a folder or a set of files." << std::cout;
		system("pause");
		return -1;
	}

    //*************************************************************************
    // 3. Init the database
    // The code reads all the images in ref_filename, detect keypoints, extract descriptors and
    // stores them in the datbase variables.
    init_database(std::string(teachdb_folder), ref_filename);
    
    
    //*************************************************************************
    // 4. The data of the database _descriptorsRefDB is added to the featue matcher
    // and the mathcer is trained
    _matcher.add(_descriptorsRefDB);
    _matcher.train();
    
    // Read the number of reference images in the database
    _num_ref_images = _matcher.getTrainDescriptors().size();
    
    
    //*************************************************************************
    // 5. Here we run the matching.
    // for images from files
    if(!run_video)
    {
        if(_mtype == KNN)run_matching( querydb_folder, query_filename);
        else if(_mtype == BRUTEFORCE) run_bf_matching(querydb_folder, query_filename);
        else
        {
            std::cout << "No matching type specified. Specify a matching type" << std::endl;
            system("pause");
        }
        
    }
    else
        // and image from a video camera
    {
        if(_mtype == KNN)run_matching( device_id);
        else if(_mtype == BRUTEFORCE)  run_bf_matching(device_id);
        else
        {
            std::cout << "No matching type specified. Specify a matching type" << std::endl;
            system("pause");
        }
        
        
    }
    
    //*************************************************************************
    // 6. Cleanup: release the keypoint detector and feature descriptor extractor
    _extractor.release();
    _detector.release();
    
    
    return 1;
}