C++ CFeatureList::clear方法代码示例

// ------------------------------------------------------------------------------------
//								convertCvSeqInCFeatureList
// ------------------------------------------------------------------------------------
void CFeatureExtraction::convertCvSeqInCFeatureList( void* features_, CFeatureList &list, unsigned int init_ID, const TImageROI &ROI ) const
{
#if MRPT_HAS_OPENCV && MRPT_HAS_SIFT_HESS
	CvSeq* features = reinterpret_cast<CvSeq*>( features_ );

	// Is there a defined ROI?
	bool usingROI = false;
	if( ROI.xMin != 0 || ROI.xMax != 0 || ROI.yMin != 0 || ROI.yMax != 0 )
		usingROI = true;

	int n = features->total;
	ASSERT_(n > 0);

	list.clear();
	struct feature* thisFeat;
	for( int k = 0; k < n; k++ )
	{
		thisFeat			= (feature*)cvGetSeqElem( features, k );
		CFeaturePtr feat	= CFeature::Create();
		feat->ID			= (TFeatureID)(k + init_ID);
		feat->x				= usingROI ? thisFeat->x + ROI.xMin : thisFeat->x;
		feat->y				= usingROI ? thisFeat->y + ROI.yMin : thisFeat->y;
		feat->type			= featSIFT;
		feat->orientation	= thisFeat->ori;
		feat->scale			= thisFeat->scl;
		feat->descriptors.SIFT.resize( thisFeat->d );
		for( int i = 0; i < thisFeat->d; i++ )
			feat->descriptors.SIFT[i] = (unsigned char)thisFeat->descr[i];

		list.push_back(feat);
	} // end for
#else
	THROW_EXCEPTION("Method not available since MRPT has been compiled without OpenCV")
#endif //MRPT_HAS_OPENCV
}

/************************************************************************************************
*							extractFeaturesKLT
************************************************************************************************/
void CFeatureExtraction::extractFeaturesKLT(
		const mrpt::utils::CImage			&inImg,
		CFeatureList			&feats,
		unsigned int			init_ID,
		unsigned int			nDesiredFeatures,
		const TImageROI			&ROI) const
{
//#define VERBOSE_TIMING

#ifdef VERBOSE_TIMING
	CTicTac tictac;
#endif
		MRPT_START

		#if MRPT_HAS_OPENCV
        const unsigned int MAX_COUNT = 300;

		// -----------------------------------------------------------------
		// Create OpenCV Local Variables
		// -----------------------------------------------------------------
		int				count = 0;
		int				nPts;

#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif
		const cv::Mat img( cv::cvarrToMat( inImg.getAs<IplImage>() ) );

#ifdef VERBOSE_TIMING
		cout << "[KLT] Attach: " << tictac.Tac()*1000.0f << endl;
#endif
		const CImage inImg_gray( inImg, FAST_REF_OR_CONVERT_TO_GRAY );
		const cv::Mat cGrey( cv::cvarrToMat( inImg_gray.getAs<IplImage>() ) );

		nDesiredFeatures <= 0 ? nPts = MAX_COUNT : nPts = nDesiredFeatures;

#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif

#ifdef VERBOSE_TIMING
		cout << "[KLT] Create: " << tictac.Tac()*1000.0f << endl;
#endif
		count = nPts;										// Number of points to find

		// -----------------------------------------------------------------
		// Select good features with subpixel accuracy (USING HARRIS OR KLT)
		// -----------------------------------------------------------------
		const bool use_harris = ( options.featsType == featHarris );

#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif
		std::vector<cv::Point2f> points;
		cv::goodFeaturesToTrack(
			cGrey,points, nPts, 
			(double)options.harrisOptions.threshold,    // for rejecting weak local maxima ( with min_eig < threshold*max(eig_image) )
			(double)options.harrisOptions.min_distance, // minimum distance between features
			cv::noArray(), // mask
			3, // blocksize
			use_harris, /* harris */
			options.harrisOptions.k 
			);
#ifdef VERBOSE_TIMING
		cout << "[KLT] Find feats: " << tictac.Tac()*1000.0f << endl;
#endif

		if( nDesiredFeatures > 0 && count < nPts )
			cout << "\n[WARNING][selectGoodFeaturesKLT]: Only " << count << " of " << nDesiredFeatures << " points could be extracted in the image." << endl;

		if( options.FIND_SUBPIXEL )
		{
#ifdef VERBOSE_TIMING
			tictac.Tic();
#endif
			// Subpixel interpolation
			cv::cornerSubPix(cGrey,points,
				cv::Size(3,3), cv::Size(-1,-1),
				cv::TermCriteria( CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10, 0.05 ));

#ifdef VERBOSE_TIMING
			cout << "[KLT] subpixel: " << tictac.Tac()*1000.0f << endl;
#endif
		}

		// -----------------------------------------------------------------
		// Fill output structure
		// -----------------------------------------------------------------
#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif

		feats.clear();
		unsigned int	borderFeats = 0;
		unsigned int	nCFeats		= init_ID;
		int				i			= 0;
		const int		limit		= min( nPts, count );
//.........这里部分代码省略.........

//.........这里部分代码省略.........
				// The descriptor:
				aux.extractRow(i, (*itFeat)->descriptors.SIFT, 4);
			}
			remove(filImg);
			remove(filOut);
#else
    THROW_EXCEPTION("Unfortunately, this SIFT Implementation only runs in Windows OS, try Hess implementation");
#endif
			break;
		} // end case Binary in C#
	case VedaldiBinary:
		{
		
		// --------------------------------------------------------------------------------------
		//		Binary by Vedaldi: NOT IMPLEMENTED YET. Input in PGM format
		// --------------------------------------------------------------------------------------
#ifdef MRPT_OS_WINDOWS
		THROW_EXCEPTION("Usage of Vedaldi Binary not implemented yet, please, try another one");
#else
	    THROW_EXCEPTION("Unfortunately, this SIFT Implementation only runs in Windows OS, try Hess implementation");
#endif
		break;
		} // end case Binary by Vedaldi
// --------------------------------------------------------------------------------------
//		Binary by David Lowe
// --------------------------------------------------------------------------------------
	case LoweBinary:			// Binary by Lowe
		{
		
#ifdef MRPT_OS_WINDOWS
			char			filImg[2000],filOut[2000];
			char			paramImg[2000];

			feats.clear();

			GetTempPathA(1000,filOut);	os::strcat(filOut,1000,"temp_out.txt");			// OUTPUT FILE
			GetTempPathA(1000,filImg);	os::strcat(filImg,1000,"temp_img.pgm");			// INPUT IMAGE (PGM) FOR ORIGINAL BINARY BY LOWE

			bool valid = img_grayscale.saveToFile( filImg );
			if(!valid)
				THROW_EXCEPTION( "An error occurred when saving input image into a .pgm file");

			// CONVERT TO UNCOMPRESSED RAW PGM (TODO: Solve in a better way this issue)
			os::strcpy( paramImg,1000, format( "cmd /C gmic.exe %s -o %s -quiet", filImg, filImg ).c_str() );

			bool ret = mrpt::system::launchProcess( paramImg );

			if(!ret)
				THROW_EXCEPTION("[extractFeaturesSIFT] Could not launch external process... (gmic.exe)");

			// ------------------------------------
			// Version  with "CreateProcess":
			// ------------------------------------
			os::strcpy(paramImg,1000,"cmd /C siftWin32.exe <"); os::strcat(paramImg,1000,filImg);
			os::strcat(paramImg,1000," >"); os::strcat(paramImg,1000,filOut);

			ret = mrpt::system::launchProcess( paramImg );

			if(!ret)
				THROW_EXCEPTION("[extractFeaturesSIFT] Could not launch external process... (siftWin32.exe)");

			// ------------------------------------
			// Process Results
			// ------------------------------------
			unsigned int dLen, nFeats;
			FILE *f = os::fopen( filOut, "rt");

// N_fast = 9, 10, 12
void  CFeatureExtraction::extractFeaturesFASTER_N(
	const int					N_fast,
	const mrpt::utils::CImage	& inImg,
	CFeatureList			    & feats,
	unsigned int			    init_ID,
	unsigned int			    nDesiredFeatures,
	const TImageROI			    & ROI )  const
{
	MRPT_START

#if MRPT_HAS_OPENCV
	// Make sure we operate on a gray-scale version of the image:
	const CImage inImg_gray( inImg, FAST_REF_OR_CONVERT_TO_GRAY );

	const IplImage *IPL = inImg_gray.getAs<IplImage>();

	TSimpleFeatureList corners;
	TFeatureType type_of_this_feature;

	switch (N_fast)
	{
	case 9:  fast_corner_detect_9 (IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER9; break;
	case 10: fast_corner_detect_10(IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER10; break;
	case 12: fast_corner_detect_12(IPL,corners, options.FASTOptions.threshold, 0, NULL); type_of_this_feature=featFASTER12; break;
	default:
		THROW_EXCEPTION("Only the 9,10,12 FASTER detectors are implemented.")
		break;
	};

	// *All* the features have been extracted.
	const size_t N = corners.size();

	// Now:
	//  1) Sort them by "response": It's ~100 times faster to sort a list of
	//      indices "sorted_indices" than sorting directly the actual list of features "corners"
	std::vector<size_t> sorted_indices(N);
	for (size_t i=0;i<N;i++)  sorted_indices[i]=i;

	// Use KLT response
	if (options.FASTOptions.use_KLT_response ||
		nDesiredFeatures!=0 // If the user wants us to limit the number of features, we need to do it according to some quality measure
		)
	{
		const int KLT_half_win = 4;
		const int max_x = inImg_gray.getWidth() - 1 - KLT_half_win;
		const int max_y = inImg_gray.getHeight() - 1 - KLT_half_win;

		for (size_t i=0;i<N;i++)
		{
			const int x = corners[i].pt.x;
			const int y = corners[i].pt.y;
			if (x>KLT_half_win && y>KLT_half_win && x<=max_x && y<=max_y)
					corners[i].response = inImg_gray.KLT_response(x,y,KLT_half_win);
			else	corners[i].response = -100;
		}

		std::sort( sorted_indices.begin(), sorted_indices.end(), KeypointResponseSorter<TSimpleFeatureList>(corners) );
	}
	else
	{
		for (size_t i=0;i<N;i++)
			corners[i].response = 0;
	}

	//  2) Filter by "min-distance" (in options.FASTOptions.min_distance)
	//  3) Convert to MRPT CFeatureList format.
	// Steps 2 & 3 are done together in the while() below.
	// The "min-distance" filter is done by means of a 2D binary matrix where each cell is marked when one
	// feature falls within it. This is not exactly the same than a pure "min-distance" but is pretty close
	// and for large numbers of features is much faster than brute force search of kd-trees.
	// (An intermediate approach would be the creation of a mask image updated for each accepted feature, etc.)

	const bool do_filter_min_dist = options.FASTOptions.min_distance>1;

	// Used half the min-distance since we'll later mark as occupied the ranges [i-1,i+1] for a feature at "i"
	const unsigned int occupied_grid_cell_size = options.FASTOptions.min_distance/2.0;
	const float occupied_grid_cell_size_inv = 1.0f/occupied_grid_cell_size;

	unsigned int grid_lx = !do_filter_min_dist ? 1 : (unsigned int)(1 + inImg.getWidth() * occupied_grid_cell_size_inv);
	unsigned int grid_ly = !do_filter_min_dist ? 1 : (unsigned int)(1 + inImg.getHeight() * occupied_grid_cell_size_inv );

	mrpt::math::CMatrixBool  occupied_sections(grid_lx,grid_ly);  // See the comments above for an explanation.
	occupied_sections.fillAll(false);


	unsigned int	nMax		= (nDesiredFeatures!=0 && N > nDesiredFeatures) ? nDesiredFeatures : N;
	const int 		offset		= (int)this->options.patchSize/2 + 1;
	const int		size_2		= options.patchSize/2;
	const size_t 	imgH		= inImg.getHeight();
	const size_t 	imgW		= inImg.getWidth();
	unsigned int	i			= 0;
	unsigned int	cont		= 0;
	TFeatureID		nextID		= init_ID;

    if( !options.addNewFeatures )
        feats.clear();


	while( cont != nMax && i!=N )
//.........这里部分代码省略.........

/************************************************************************************************
*								extractFeaturesSURF  									        *
************************************************************************************************/
void  CFeatureExtraction::extractFeaturesSURF(
		const mrpt::utils::CImage		&inImg,
		CFeatureList			&feats,
		unsigned int			init_ID,
		unsigned int			nDesiredFeatures,
		const TImageROI			&ROI) const
{
	MRPT_UNUSED_PARAM(ROI);
#if MRPT_HAS_OPENCV && MRPT_OPENCV_VERSION_NUM >= 0x111

	const CImage img_grayscale(inImg, FAST_REF_OR_CONVERT_TO_GRAY);
	const IplImage* cGrey = img_grayscale.getAs<IplImage>();

	CvSeq *kp	=	NULL;
	CvSeq *desc	=	NULL;
	CvMemStorage *storage = cvCreateMemStorage(0);

	// Extract the SURF points:
	CvSURFParams surf_params = cvSURFParams(options.SURFOptions.hessianThreshold, options.SURFOptions.rotation_invariant ? 1:0);
	surf_params.nOctaves = options.SURFOptions.nOctaves;
	surf_params.nOctaveLayers = options.SURFOptions.nLayersPerOctave;

	cvExtractSURF( cGrey, NULL, &kp, &desc, storage, surf_params);

	// -----------------------------------------------------------------
	// MRPT Wrapping
	// -----------------------------------------------------------------
	feats.clear();
	unsigned int	nCFeats		= init_ID;
	int				limit;
	int				offset		= (int)this->options.patchSize/2 + 1;
	unsigned int	imgH		= inImg.getHeight();
	unsigned int	imgW		= inImg.getWidth();

	if( nDesiredFeatures == 0 )
		limit = kp->total;
	else
		limit = (int)nDesiredFeatures < kp->total ? (int)nDesiredFeatures : kp->total;

	for( int i = 0; i < limit; i++ )
	{
		// Get the OpenCV SURF point
		CvSURFPoint *point;
		CFeaturePtr ft = CFeature::Create();
		point = (CvSURFPoint*)cvGetSeqElem( kp, i );

		const int xBorderInf = (int)floor( point->pt.x - options.patchSize/2 );
		const int xBorderSup = (int)floor( point->pt.x + options.patchSize/2 );
		const int yBorderInf = (int)floor( point->pt.y - options.patchSize/2 );
		const int yBorderSup = (int)floor( point->pt.y + options.patchSize/2 );

		if( options.patchSize == 0 || ( (xBorderSup < (int)imgW) && (xBorderInf > 0) && (yBorderSup < (int)imgH) && (yBorderInf > 0) ) )
		{
			ft->type		= featSURF;
			ft->x			= point->pt.x;				// X position
			ft->y			= point->pt.y;				// Y position
			ft->orientation = point->dir;				// Orientation
			ft->scale		= point->size*1.2/9;		// Scale
			ft->ID			= nCFeats++;				// Feature ID into extraction
			ft->patchSize	= options.patchSize;		// The size of the feature patch

			if( options.patchSize > 0 )
			{
				inImg.extract_patch(
					ft->patch,
					round( ft->x ) - offset,
					round( ft->y ) - offset,
					options.patchSize,
					options.patchSize );				// Image patch surronding the feature
			}

			// Get the SURF descriptor
			float* d = (float*)cvGetSeqElem( desc, i );
			ft->descriptors.SURF.resize( options.SURFOptions.rotation_invariant ? 128 : 64 );
			std::vector<float>::iterator itDesc;
			unsigned int k;

			for( k = 0, itDesc = ft->descriptors.SURF.begin(); k < ft->descriptors.SURF.size(); k++, itDesc++ )
				*itDesc = d[k];

			feats.push_back( ft );

		} // end if
	} // end for

	cvReleaseMemStorage(&storage); // Free memory

#else
	THROW_EXCEPTION("Method not available since either MRPT has been compiled without OpenCV or OpenCV version is incorrect (Required 1.1.0)")
#endif //MRPT_HAS_OPENCV
} // end extractFeaturesSURF

//.........这里部分代码省略.........
		{
#ifdef VERBOSE_TIMING
			tictac.Tic();
#endif
			cvGoodFeaturesToTrack( cGrey, eig, temp, &points[0], &count,	// input and output data
				(double)options.KLTOptions.threshold,						// for rejecting weak local maxima ( with min_eig < threshold*max(eig_image) )
				(double)options.KLTOptions.min_distance,					// minimum distance between features
				mask ? (*mask) : static_cast<const CvMat*>(NULL),			// ROI
				options.KLTOptions.radius,									// size of the block of pixels used
				0,															// use Kanade Lucas Tomasi
				0.04 );														// un-used parameter
#ifdef VERBOSE_TIMING
			cout << "[KLT]: Find feats: " << tictac.Tac()*1000.0f << endl;
#endif
		}

		if( nDesiredFeatures > 0 && count < nPts )
			cout << "\n[WARNING][selectGoodFeaturesKLT]: Only " << count << " of " << nDesiredFeatures << " points could be extracted in the image." << endl;

		if( options.FIND_SUBPIXEL )
		{
#ifdef VERBOSE_TIMING
			tictac.Tic();
#endif
			// Subpixel interpolation
			cvFindCornerSubPix( cGrey, &points[0], count,
				cvSize(3,3), cvSize(-1,-1),
				cvTermCriteria( CV_TERMCRIT_ITER|CV_TERMCRIT_EPS, 10, 0.05 ));

#ifdef VERBOSE_TIMING
			cout << "[KLT] subpixel: " << tictac.Tac()*1000.0f << endl;
#endif
		}

		// -----------------------------------------------------------------
		// Fill output structure
		// -----------------------------------------------------------------
#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif

		feats.clear();
		unsigned int	borderFeats = 0;
		unsigned int	nCFeats		= init_ID;
		int				i			= 0;
		const int		limit		= min( nPts, count );
		int				offset		= (int)this->options.patchSize/2 + 1;
		unsigned int	imgH		= inImg.getHeight();
		unsigned int	imgW		= inImg.getWidth();

		while( i < limit )
		{
			const int xBorderInf = (int)floor( points[i].x - options.patchSize/2 );
			const int xBorderSup = (int)floor( points[i].x + options.patchSize/2 );
			const int yBorderInf = (int)floor( points[i].y - options.patchSize/2 );
			const int yBorderSup = (int)floor( points[i].y + options.patchSize/2 );

			if( options.patchSize==0 || ( (xBorderSup < (int)imgW) && (xBorderInf > 0) && (yBorderSup < (int)imgH) && (yBorderInf > 0) ) )
			{
				CFeaturePtr ft = CFeature::Create();

				ft->type		= featKLT;
				ft->x			= points[i].x;				// X position
				ft->y			= points[i].y;				// Y position
				ft->track_status = status_TRACKED;		    // Feature Status
				ft->response	= 0.0;						// A value proportional to the quality of the feature (unused yet)
				ft->ID			= nCFeats++;				// Feature ID into extraction
				ft->patchSize	= options.patchSize;		// The size of the feature patch

				if( options.patchSize > 0 )
				{
					inImg.extract_patch(
						ft->patch,
						round( ft->x ) - offset,
						round( ft->y ) - offset,
						options.patchSize,
						options.patchSize );				// Image patch surronding the feature
				}

				feats.push_back( ft );

			} // end if
			else
				borderFeats++;

			i++;
		} // end while

#ifdef VERBOSE_TIMING
		cout << "[KLT] Create output: " << tictac.Tac()*1000.0f << endl;
#endif


		#else
			THROW_EXCEPTION("The MRPT has been compiled with MRPT_HAS_OPENCV=0 !");
		#endif

		MRPT_END

} // end of function

void CFeatureExtraction::extractFeaturesAKAZE(
	const mrpt::img::CImage& inImg, CFeatureList& feats, unsigned int init_ID,
	unsigned int nDesiredFeatures, const TImageROI& ROI) const
{
	MRPT_UNUSED_PARAM(ROI);
	MRPT_START
#if MRPT_HAS_OPENCV
#if MRPT_OPENCV_VERSION_NUM < 0x300
	THROW_EXCEPTION("This function requires OpenCV > 3.0.0");
#else

	using namespace cv;
	vector<KeyPoint> cv_feats;  // The opencv keypoint output vector
	// Make sure we operate on a gray-scale version of the image:
	const CImage inImg_gray(inImg, FAST_REF_OR_CONVERT_TO_GRAY);

#if MRPT_OPENCV_VERSION_NUM >= 0x300

	const Mat theImg = cvarrToMat(inImg_gray.getAs<IplImage>());
	Ptr<AKAZE> akaze = AKAZE::create(
		options.AKAZEOptions.descriptor_type,
		options.AKAZEOptions.descriptor_size,
		options.AKAZEOptions.descriptor_channels,
		options.AKAZEOptions.threshold, options.AKAZEOptions.nOctaves,
		options.AKAZEOptions.nOctaveLayers, options.AKAZEOptions.diffusivity);

	akaze->detect(theImg, cv_feats);

	// *All* the features have been extracted.
	const size_t N = cv_feats.size();

#endif
	// sort the AKAZE features by line length
	for (size_t i = 0; i < N; i++)
	{
		for (size_t j = i + 1; j < N; j++)
		{
			if (cv_feats.at(j).response > cv_feats.at(i).response)
			{
				KeyPoint temp_point = cv_feats.at(i);
				cv_feats.at(i) = cv_feats.at(j);
				cv_feats.at(j) = temp_point;
			}
		}
	}

	unsigned int nMax =
		(nDesiredFeatures != 0 && N > nDesiredFeatures) ? nDesiredFeatures : N;
	const int offset = (int)this->options.patchSize / 2 + 1;
	const size_t size_2 = options.patchSize / 2;
	const size_t imgH = inImg.getHeight();
	const size_t imgW = inImg.getWidth();
	unsigned int i = 0;
	unsigned int cont = 0;
	TFeatureID nextID = init_ID;

	if (!options.addNewFeatures) feats.clear();

	while (cont != nMax && i != N)
	{
		// Take the next feature from the ordered list of good features:
		const KeyPoint& kp = cv_feats[i];
		i++;

		// Patch out of the image??
		const int xBorderInf = (int)floor(kp.pt.x - size_2);
		const int xBorderSup = (int)floor(kp.pt.x + size_2);
		const int yBorderInf = (int)floor(kp.pt.y - size_2);
		const int yBorderSup = (int)floor(kp.pt.y + size_2);

		if (!(xBorderSup < (int)imgW && xBorderInf > 0 &&
			  yBorderSup < (int)imgH && yBorderInf > 0))
			continue;  // nope, skip.

		// All tests passed: add new feature:
		CFeature::Ptr ft = std::make_shared<CFeature>();
		ft->type = featAKAZE;
		ft->ID = nextID++;
		ft->x = kp.pt.x;
		ft->y = kp.pt.y;
		ft->response = kp.response;
		ft->orientation = kp.angle;
		ft->scale = kp.octave;
		ft->patchSize = options.patchSize;  // The size of the feature patch

		if (options.patchSize > 0)
		{
			inImg.extract_patch(
				ft->patch, round(ft->x) - offset, round(ft->y) - offset,
				options.patchSize,
				options.patchSize);  // Image patch surronding the feature
		}
		feats.push_back(ft);
		++cont;
		// cout << ft->x << "  " << ft->y << endl;
	}

#endif
#endif
	MRPT_END
//.........这里部分代码省略.........