C++ utils::CImage类代码示例

void buildPyramid_templ(
	CImagePyramid &obj,
	mrpt::utils::CImage &img,
	const size_t nOctaves,
	const bool smooth_halves,
	const bool convert_grayscale)
{
	ASSERT_ABOVE_(nOctaves,0)

	//TImageSize  img_size = img.getSize();
	obj.images.resize(nOctaves);

	// First octave: Just copy the image:
	if (convert_grayscale && img.isColor())
	{
		// In this case we have to convert to grayscale, so FASTLOAD doesn't really matter:
		img.grayscale(obj.images[0]);
	}
	else
	{
		// No need to convert to grayscale OR image already is grayscale:
		if (FASTLOAD)
		     obj.images[0].copyFastFrom(img);  // Fast copy -> "move", destroying source.
		else obj.images[0] = img;  // Normal copy
	}

	// Rest of octaves, if any:
	for (size_t o=1;o<nOctaves;o++)
	{
		if (smooth_halves)
		     obj.images[o-1].scaleHalfSmooth(obj.images[o]);
		else obj.images[o-1].scaleHalf(obj.images[o]);
	}
}

void CFormPlayVideo::drawHorzRules(mrpt::utils::CImage& img)
{
	if (!cbDrawStereoRules->IsChecked()) return;

	img.forceLoad();
	const size_t Ay = edHorzRuleSpace->GetValue();
	const size_t h = img.getHeight();
	const size_t w = img.getWidth();

	for (size_t y = Ay; y < h; y += Ay)
		img.line(0, y, w - 1, y, mrpt::utils::TColor::white());
}

/* ----------------------------------------------------------
						writeImage
   ---------------------------------------------------------- */
bool CVideoFileWriter::writeImage(const mrpt::utils::CImage& img) const
{
	if (!m_video.get())
		return false;

	if ((size_t)m_img_size.x!=img.getWidth() || (size_t)m_img_size.y!=img.getHeight())
	{
		std::cout << format("[CVideoFileWriter::writeImage] Error: video frame size is %ix%i but image is %ux%u", m_img_size.x,m_img_size.y,(unsigned)img.getWidth(),(unsigned)img.getHeight() ) << std::endl;
		return false;
	}

#if MRPT_HAS_OPENCV
	return 0!= cvWriteFrame( M_WRITER, img.getAs<IplImage>() );
#else
	return false;
#endif
}

/************************************************************************************************
*							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.........

// 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.........

/* --------------------------------------------------------
					retrieveFrame
   -------------------------------------------------------- */
bool CFFMPEG_InputStream::retrieveFrame( mrpt::utils::CImage &out_img )
{
#if MRPT_HAS_FFMPEG
	if (!this->isOpen()) return false;

	TFFMPEGContext *ctx = MY_FFMPEG_STATE;

    AVPacket        packet;
    int             frameFinished;

    while(av_read_frame(ctx->pFormatCtx, &packet)>=0)
    {
        // Is this a packet from the video stream?
        if(packet.stream_index==ctx->videoStream)
        {
            // Decode video frame
#if LIBAVCODEC_VERSION_MAJOR>52 || (LIBAVCODEC_VERSION_MAJOR==52 && LIBAVCODEC_VERSION_MINOR>=72)
            avcodec_decode_video2(
				ctx->pCodecCtx,
				ctx->pFrame,
				&frameFinished,
                &packet);
#else
            avcodec_decode_video(
				ctx->pCodecCtx,
				ctx->pFrame,
				&frameFinished,
                packet.data,
                packet.size);
#endif
            // Did we get a video frame?
            if(frameFinished)
            {
                // Convert the image from its native format to RGB:
				ctx->img_convert_ctx = sws_getCachedContext(
					ctx->img_convert_ctx,
					ctx->pCodecCtx->width,
					ctx->pCodecCtx->height,
					ctx->pCodecCtx->pix_fmt,
					ctx->pCodecCtx->width,
					ctx->pCodecCtx->height,
					m_grab_as_grayscale ? PIX_FMT_GRAY8 : PIX_FMT_BGR24,   // BGR vs. RGB for OpenCV
					SWS_BICUBIC,
					NULL, NULL, NULL);

				sws_scale(
					ctx->img_convert_ctx,
					ctx->pFrame->data,
					ctx->pFrame->linesize,0,
					ctx->pCodecCtx->height,
					ctx->pFrameRGB->data,
					ctx->pFrameRGB->linesize);

				/*	JL: Old code (deprecated)
				img_convert(
					(AVPicture *)ctx->pFrameRGB,
					m_grab_as_grayscale ? PIX_FMT_GRAY8 : PIX_FMT_BGR24,   // BGR vs. RGB for OpenCV
                    (AVPicture*)ctx->pFrame,
                    ctx->pCodecCtx->pix_fmt,
                    ctx->pCodecCtx->width,
                    ctx->pCodecCtx->height
                    ); */

				//std::cout << "[retrieveFrame] Generating image: " << ctx->pCodecCtx->width << "x" << ctx->pCodecCtx->height << std::endl;
				//std::cout << "  linsize: " << ctx->pFrameRGB->linesize[0] << std::endl;

				if( ctx->pFrameRGB->linesize[0]!= ((m_grab_as_grayscale ? 1:3)*ctx->pCodecCtx->width) )
					THROW_EXCEPTION("FIXME: linesize!=width case not handled yet.")

				out_img.loadFromMemoryBuffer(
					ctx->pCodecCtx->width,
					ctx->pCodecCtx->height,
					!m_grab_as_grayscale, // Color
					ctx->pFrameRGB->data[0]
					);

				// Free the packet that was allocated by av_read_frame
				av_free_packet(&packet);
				return true;
            }
        }

        // Free the packet that was allocated by av_read_frame
        av_free_packet(&packet);
    }

    return false; // Error reading/ EOF
#else
	return false;
#endif
}

/**  Stage2 operations:
  *   - Detect features on each image and on each scale.
  */
void CStereoOdometryEstimator::stage2_detect_features(
		CStereoOdometryEstimator::TImagePairData::img_data_t	& img_data,
		mrpt::utils::CImage										& gui_image,
		bool													update_dyn_thresholds )
{
	using namespace mrpt::vision;

	m_profiler.enter("_stg2");

	// :: Resize output containers:
	const size_t nOctaves = img_data.pyr.images.size();
	ASSERTDEB_(nOctaves>0)

	vector<size_t> nFeatsPassingKLTPerOctave(nOctaves);
    img_data.pyr_feats.resize(nOctaves);
    img_data.pyr_feats_index.resize(nOctaves);
    img_data.pyr_feats_kps.resize(nOctaves);
    img_data.pyr_feats_desc.resize(nOctaves);

	vector<size_t> kps_to_detect(nOctaves);			// number of kps to detect in each octave
	kps_to_detect[0] = size_t(params_detect.orb_nfeats*(2*nOctaves)/(std::pow(2,nOctaves)-1));
	for( size_t octave = 1; octave < nOctaves; ++octave )
		kps_to_detect[octave] = size_t(round(kps_to_detect[0]/std::pow(2,octave)));

	// :: For the GUI thread
	m_next_gui_info->stats_feats_per_octave.resize(nOctaves); // Reserve size for stats
    m_next_gui_info->stats_FAST_thresholds_per_octave.resize(nOctaves);

	// :: Detection parameters
	// FASTER METHOD --------------------
	// - Evaluate the KLT response of all features to discard those in texture-less zones
    const unsigned int KLT_win	= params_detect.KLT_win;
    const double minimum_KLT_response	= params_detect.minimum_KLT_response;
	// ----------------------------------

	// size_t num_feats_this_octave; 

	// :: Main loop
	for( size_t octave = 0; octave < nOctaves; ++octave )
	{
		// - Image information
        Mat input_im = cv::cvarrToMat(img_data.pyr.images[octave].getAs<IplImage>());
		const mrpt::utils::TImageSize img_size = img_data.pyr.images[octave].getSize();

		// - Profile section name
		const std::string sProfileName = mrpt::format("stg2.detect.oct=%u",static_cast<unsigned int>(octave));

		// - Auxiliar parameters that will store preliminar extracted information (before NMS)
		TKeyPointList	feats_vector;
		Mat				desc_aux;

		// ***********************************
		// KLT method (use ORB feature vector, no descriptor)
		// ***********************************
		if( params_detect.detect_method == TDetectParams::dmKLT )
		{
			m_profiler.enter(sProfileName.c_str());

			// detect Shi&Tomasi keypoints
			goodFeaturesToTrack(
				input_im,					// image
				feats_vector,				// output feature vector
				kps_to_detect[octave],		// params_detect.orb_nfeats,	// number of features to detect
				0.01,						// quality level
				20);						// minimum distance
			
			desc_aux = Mat();				// no descriptor

			m_profiler.leave(sProfileName.c_str());
		}
		// ***********************************
		// ORB method
		// ***********************************
		else if( params_detect.detect_method == TDetectParams::dmORB )
		{
			// ** NOTE ** in this case, nOctaves should be 1 (set in stage1)
			const size_t n_feats_to_extract = 
				params_detect.non_maximal_suppression ? 
					1.5*params_detect.orb_nfeats : 
					params_detect.orb_nfeats; // if non-max-sup is ON extract more features to get approx the number of desired output feats.

			m_profiler.enter(sProfileName.c_str());
			
#if CV_MAJOR_VERSION < 3  // OpenCV < 3.0.0
			ORB orbDetector( 
				n_feats_to_extract,			// number of ORB features to extract
				1.2,						// scale difference
				params_detect.orb_nlevels,  // number of levels
				31,							// edgeThreshold
				0,							// firstLevel
				2,							// WTA_K
				ORB::HARRIS_SCORE,			// scoreType
                31);						// patchSize

			// detect keypoints and descriptors
			orbDetector( input_im, Mat(), feats_vector, desc_aux );  // all the scales in the same call
#else
//.........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

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

#ifdef VERBOSE_TIMING
	CTicTac tictac;
#endif
		MRPT_START

		#if MRPT_HAS_OPENCV
        const unsigned int MAX_COUNT = 300;

		// Reinterpret opencv formal arguments
		CvMatrix *mask = reinterpret_cast<CvMatrix*>(mask_);

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

		CvImage img, cGrey;

#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif
		img.attach( const_cast<IplImage*>(inImg.getAs<IplImage>()), false );	// Attach Image as IplImage and do not use ref counter
#ifdef VERBOSE_TIMING
		cout << "[KLT] Attach: " << tictac.Tac()*1000.0f << endl;
#endif
		if( img.channels() == 1 )
			cGrey = img;										// Input image is already 'grayscale'
		else
		{
			cGrey.create( cvGetSize( img ), 8, 1);
			cvCvtColor( img, cGrey, CV_BGR2GRAY );				// Convert input image into 'grayscale'
		}

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

		std::vector<CvPoint2D32f> points(nPts);

		CvImage eig, temp;									// temporary and auxiliary images

#ifdef VERBOSE_TIMING
		tictac.Tic();
#endif
		eig.create( cvGetSize( cGrey ), 32, 1 );
		temp.create( cvGetSize( cGrey ), 32, 1 );
#ifdef VERBOSE_TIMING
		cout << "[KLT] Create: " << tictac.Tac()*1000.0f << endl;
#endif
		count = nPts;										// Number of points to find


#if 0	// Temporary debug
		{
			static int i=0;
			cvSaveImage( format("debug_map_%05i.bmp",++i).c_str(), cGrey);
		}
#endif
		// -----------------------------------------------------------------
		// Select good features with subpixel accuracy (USING HARRIS OR KLT)
		// -----------------------------------------------------------------
		if( options.featsType == featHarris )
		{
#ifdef VERBOSE_TIMING
			tictac.Tic();
#endif
			cvGoodFeaturesToTrack( cGrey, eig, temp, &points[0], &count,	// input and output data
				(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
				mask ? (*mask) : static_cast<const CvMat*>(NULL),			// ROI
				(double)options.harrisOptions.radius,						// size of the block of pixels used
				1,															// use Harris
				options.harrisOptions.k );									// k factor for the Harris algorithm
#ifdef VERBOSE_TIMING
			cout << "[KLT] Find feats: " << tictac.Tac()*1000.0f << endl;
#endif
		}
		else
		{
#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
//.........这里部分代码省略.........

 inline void setPixel(const openni::RGB888Pixel& src, mrpt::utils::CImage& rgb  , int x, int y){ rgb.setPixel(x, y, (src.r << 16) + (src.g << 8) + src.b); }

 inline void resize(mrpt::utils::CImage& rgb  , int w, int h){ rgb.resize(w, h, CH_RGB, true); }