C++ CFeatureExtraction::computeDescriptors方法代码示例

double benchmark_computeDescriptor(int N, int num_feats)
{
	CImage img;
	getTestImage(0, img);

	CFeatureExtraction fExt;
	fExt.profiler.enable();
	fExt.options.featsType = featFASTER9;

	for (int i = 0; i < N; i++)
	{
		CFeatureList fs;
		fExt.detectFeatures(img, fs, 0 /*id*/, num_feats);
		fExt.computeDescriptors(img, fs, DESCRIPTOR_TYPE);
	}

	return fExt.profiler.getMeanTime("computeDescriptors");
}

// ------------------------------------------------------
//				Benchmark: Spin descriptor
// ------------------------------------------------------
double feature_extraction_test_Spin_desc( int N, int h )
{
	CTicTac	 tictac;

	// Generate a random image
	CImage  img;
	getTestImage(0,img);

	CFeatureExtraction	fExt;
	CFeatureList		featsHarris;

	fExt.options.SpinImagesOptions.radius				= 13;
	fExt.options.SpinImagesOptions.hist_size_distance	= 10;
	fExt.options.SpinImagesOptions.hist_size_intensity	= 10;

	fExt.detectFeatures( img, featsHarris );

	tictac.Tic();
	for (int i=0;i<N;i++)
		fExt.computeDescriptors( img, featsHarris, descSpinImages );

	const double T = tictac.Tac()/N;
	return T;
}

// ------------------------------------------------------
//				Benchmark: SIFT descriptor only
// ------------------------------------------------------
double feature_extraction_test_SIFT_desc( int N, int h )
{
	CTicTac	 tictac;

	// Generate a random image
	CImage  img;
	getTestImage(0,img);

	CFeatureExtraction	fExt;
	CFeatureList		featsHarris;

	fExt.options.featsType	= featHarris;

	fExt.detectFeatures( img, featsHarris );

	tictac.Tic();
	for (int i=0;i<N;i++)
		fExt.computeDescriptors( img, featsHarris, descSIFT );

	const double T = tictac.Tac()/N;

//	cout << "SIFT desc: " << featsHarris.size();
	return T;
}

// ------------------------------------------------------
//		TestMatchingComparative
// ------------------------------------------------------
void TestMatchingComparative()
{
    // Take two images
    string imgL = myDataDir + string("imL_p01.jpg");		// Left image
	string imgR = myDataDir + string("imR_p01.jpg");		// Right image

    CImage im1, im2;
    im1.loadFromFile( imgL );
    im2.loadFromFile( imgR );

    size_t imW = im1.getWidth();
    size_t imH = im1.getHeight();

    CFeatureExtraction fExt;
    fExt.options.featsType                  = featFAST;
    fExt.options.patchSize                  = 21;
    fExt.options.SIFTOptions.implementation = CFeatureExtraction::Hess;

    // Find FAST features
    CFeatureList list1, list2;
    fExt.detectFeatures( im1, list1, 150 );
    // Compute SIFT & SURF descriptors
    fExt.computeDescriptors( im1, list1, descSIFT );
    fExt.computeDescriptors( im1, list1, descSURF );

    fExt.detectFeatures( im2, list2, 150 );
    // Compute SIFT & SURF descriptors
    fExt.computeDescriptors( im2, list2, descSIFT );
    fExt.computeDescriptors( im2, list2, descSURF );

    CFeatureList::iterator it1, it2;
    for( it1 = list1.begin(); it1 != list1.end(); ++it1 )
        im1.cross( (*it1)->x, (*it1)->y, TColor::red, '+');
    for( it2 = list2.begin(); it2 != list2.end(); ++it2 )
        im2.cross( (*it2)->x, (*it2)->y, TColor::red, '+');

    CDisplayWindow win, win2;
    win.setPos(0,0);
    win2.setPos(0,imH*1.5);
    CImage joinimage, copyjoinimage, copyInfoImage;
    size_t imW2 = 1280;
    size_t imH2 = 150;

    CImage infoimage( imW2, imH2, CH_RGB );

    joinimage.joinImagesHorz( im1, im2 );
    infoimage.filledRectangle( 0, 0, imW2, imH2, TColor(150,150,150) );
    infoimage.textOut( 20, imH2-53, "SAD", TColor::blue );
    infoimage.textOut( 20, imH2-41, "NCC", TColor::blue );
    infoimage.textOut( 20, imH2-29, "SIFT", TColor::blue );
    infoimage.textOut( 20, imH2-17, "SURF", TColor::blue );
    for( it1 = list1.begin(); it1 != list1.end(); ++it1 )
    {
        copyInfoImage = infoimage;
        copyjoinimage = joinimage;
        copyjoinimage.line( (*it1)->x, 0, (*it1)->x, imH, TColor::green );            // Horiz
        copyjoinimage.line( (*it1)->x+imW, 0, (*it1)->x+imW, imH, TColor::green );    // Horiz
        copyjoinimage.line( 0, (*it1)->y, imW+imW, (*it1)->y, TColor::green );        // Epipolar
        copyjoinimage.drawCircle( (*it1)->x, (*it1)->y, 4, TColor::green, 2 );        // Keypoint

        copyInfoImage.update_patch( (*it1)->patch, 0, 0 );
        bool firstMatch = true;
        int cnt = 0;
        int px = 80;
        double minsad = 1.0, maxncc = 0.0;
        float minsiftd = 1.0f, minsurfd = 1.0f;
        int idxsad = 0, idxncc = 0, idxsiftd = 0, idxsurfd = 0;

        for( it2 = list2.begin(); it2 != list2.end(); ++it2 )
        {
            if( fabs((*it1)->y-(*it2)->y) <= 1.0 && (*it1)->x > (*it2)->x )
            {
                    // Compute matching with SAD and Correlation and SIFT/SURF?
                    // Use epipolar constraints
                    // Compute SAD
                    double sad = mrpt::vision::computeSAD( (*it1)->patch, (*it2)->patch );
                    if( sad < minsad )
                    {
                        minsad = sad;
                        idxsad = cnt;
                    }
                    // Compute Correlation
                    double ncc;
                    size_t u, v;
                    mrpt::vision::openCV_cross_correlation( (*it1)->patch, (*it2)->patch, u, v, ncc );
                    if( ncc > maxncc )
                    {
                        maxncc = ncc;
                        idxncc = cnt;
                    }

                    // Compute distance between descriptors SIFT
                    float siftd = (*it1)->descriptorSIFTDistanceTo( *(*it2) );
                    if( siftd < minsiftd )
                    {
                        minsiftd = siftd;
                        idxsiftd = cnt;
//.........这里部分代码省略.........

// ------------------------------------------------------
//				TestExtractFeatures
// ------------------------------------------------------
void TestExtractFeatures()
{
	CDisplayWindow		wind1,wind2,wind3,wind4,wind5;
	CFeatureExtraction	fExt;
	CFeatureList		featsHarris, featsKLT, featsSIFT_Hess, featsSIFT_Lowe, featsSIFT_Vedaldi, featsSURF, featsFAST;
	CImage				img;

	if (!img.loadFromFile(the_img_for_extract_feats ))
	{
		cerr << "Cannot load " << the_img_for_extract_feats  << endl;
		return;
	}
	cout << "Loaded test image: " << endl << the_img_for_extract_feats << endl;
	cout << "--------------------------------------------------------------------------" << endl << endl;

	CTicTac	tictac;

	fExt.options.patchSize = 0;

	cout << "Detect Harris features... [f_harris.txt]" << endl;
	tictac.Tic();
	fExt.options.featsType = featHarris;
	fExt.detectFeatures( img, featsHarris );
	cout << "Detected " << featsHarris.size() << " features in ";
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsHarris.saveToTextFile("f_harris.txt");
	wind1.setWindowTitle("Harris detected features");
	wind1.showImageAndPoints(img, featsHarris);

	cout << "Detect FAST features... [f_fast.txt]" << endl;
	tictac.Tic();
	fExt.options.featsType = featFAST;
	fExt.options.FASTOptions.threshold = 15; //150;
	fExt.options.FASTOptions.min_distance = 4;
	fExt.options.FASTOptions.use_KLT_response = true;
	fExt.detectFeatures( img, featsFAST, 0,  500 /* max num feats */  );
	cout << "Detected " << featsFAST.size() << " features in ";
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsFAST.saveToTextFile("f_fast.txt");
	wind5.setWindowTitle("FAST detected features");
	wind5.showImageAndPoints( img, featsFAST );

	cout << "Computing SIFT descriptors only ... [f_harris+sift.txt]" << endl;
	tictac.Tic();
	fExt.options.SIFTOptions.implementation = CFeatureExtraction::Hess;
	fExt.computeDescriptors( img, featsHarris, descSIFT );
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsHarris.saveToTextFile("f_harris+sift.txt");

	cout << "Extracting KLT features... [f_klt.txt]" << endl;
	tictac.Tic();
	fExt.options.featsType = featKLT;
	fExt.options.KLTOptions.threshold	= 0.05f;
	fExt.options.KLTOptions.radius		= 5;
	fExt.detectFeatures( img, featsKLT, 0, 10 );
	cout << "Detected " << featsKLT.size() << " features in ";
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsKLT.saveToTextFile("f_klt.txt");
	wind2.setWindowTitle("KLT detected features");
	wind2.showImageAndPoints( img, featsKLT );

	cout << "Extracting SIFT features... [f_sift_hess.txt]" << endl;
	tictac.Tic();
	fExt.options.featsType = featSIFT;
	fExt.options.SIFTOptions.implementation = CFeatureExtraction::Hess;
	fExt.detectFeatures( img, featsSIFT_Hess );
	cout << "Detected " << featsSIFT_Hess.size() << " features in ";
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsSIFT_Hess.saveToTextFile("f_sift_hess.txt");
	wind3.setWindowTitle("SIFT Hess detected features");
	wind3.showImageAndPoints( img, featsSIFT_Hess );

	cout << "Extracting SURF features... [f_surf.txt]" << endl;
	tictac.Tic();
	fExt.options.featsType = featSURF;
	fExt.detectFeatures( img, featsSURF );
	cout << "Detected " << featsSURF.size() << " features in ";
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsSURF.saveToTextFile("f_surf.txt");
	wind4.setWindowTitle("SURF detected features");
	wind4.showImageAndPoints( img, featsSURF );

	cout << "Computing spin images descriptors only ... [f_harris+spinimgs.txt]" << endl;
	tictac.Tic();
	fExt.options.SpinImagesOptions.radius = 13;
	fExt.options.SpinImagesOptions.hist_size_distance  = 10;
	fExt.options.SpinImagesOptions.hist_size_intensity = 10;
	fExt.computeDescriptors( img, featsHarris, descSpinImages );
	cout << format("  %.03fms",tictac.Tac()*1000) << endl << endl;
	featsHarris.saveToTextFile("f_harris+spinimgs.txt");
	
	mrpt::system::pause();

	return;
}

/************************************************************************************************
*								Start Function function *
************************************************************************************************/
string PlaceRecognition::startPlaceRecognition(CFeatureExtraction fext)
{
	ofstream training_file;

	CTicTac feature_time;
	feature_time.Tic();

	/// stores the labels for the i'th image instance for training and testing
	/// images
	int training_labels[len_training];
	int testing_labels[len_testing];

	/// The training model is built here all features are extracted in this
	/// part, takes 30 seconds for 900+900 images
	if (!trained_flag)
	{
		for (int i = 0; i < len_training; i++)
		{
			training[i].loadFromFile(training_paths.at(i));
			fext.detectFeatures(training[i], feats_training[i], 0, numFeats);
			fext.computeDescriptors(
				training[i], feats_training[i], desc_to_compute);
		}
		for (int i = 0; i < len_testing; i++)
		{
			testing[i].loadFromFile(testing_paths.at(i));
			fext.detectFeatures(testing[i], feats_testing[i], 0, numFeats);
			fext.computeDescriptors(
				testing[i], feats_testing[i], desc_to_compute);
		}
		trained_flag = true;
		feats_testing_org = feats_testing;

	}  /// end of if feature extraction flag (trained flag)

	CTicTac label_time;
	label_time.Tic();

	computeLabels(training_paths, training_count, training_labels);
	computeLabels(testing_paths, testing_count, testing_labels);

	int len_train_words;
	len_train_words = 0;
	// int len_test_words = 0;
	for (int i = 0; i < len_training; i++)
		len_train_words += feats_training[i].size();

	if (!training_file_written_flag)
	{
		training_words2 = new vector<float>[len_train_words];
		training_words1 = new vector<uint8_t>[len_train_words];
	}
	int training_word_labels[len_train_words];

	CTicTac training_time;
	training_time.Tic();
	training_file.open("training_images_features.txt");

	if (!training_file_written_flag)
	{
		training_file.clear();
		int kount = 0;
		for (int i = 0; i < len_training; i++)
		{
			training_file << feats_training[i].size();
			for (unsigned int j = 0; j < feats_training[i].size(); j++, kount++)
			{
				if (descriptor_selected == 0)
				{
					vector<uint8_t> temp_feat;
					temp_feat =
						feats_training[i].getByID(j).get()->descriptors.SIFT;
					training_words1[kount] =
						feats_training[i].getByID(j).get()->descriptors.SIFT;
					training_word_labels[kount] = training_labels[i];
					for (unsigned int k = 0; k < temp_feat.size(); k++)
						training_file << (int)temp_feat.at(k) << " ";
				}
				else if (descriptor_selected == 1)
				{
					vector<float> temp_feat;
					temp_feat =
						feats_training[i].getByID(j).get()->descriptors.SURF;
					training_words2[kount] =
						feats_training[i].getByID(j).get()->descriptors.SURF;
					training_word_labels[kount] = training_labels[i];

					for (unsigned int k = 0; k < temp_feat.size(); k++)
					{
						training_file << temp_feat.at(k) << " ";
					}
				}
				else if (descriptor_selected == 2)
				{
					vector<float> temp_feat;
					temp_feat =
						feats_training[i].getByID(j).get()->descriptors.SpinImg;
//.........这里部分代码省略.........