SIFT_VS2008.rar_ sift_vs2008_gsl_sf.h_sift_sift vs2008_sift_vs2

/* Functions for detecting SIFT image features. For more information, refer to: Lowe, D. Distinctive image features from scale-invariant keypoints. <EM>International Journal of Computer Vision, 60</EM>, 2 (2004), pp.91--110. Copyright (C) 2006 Rob Hess <hess@eecs.oregonstate.edu> Note: The SIFT algorithm is patented in the United States and cannot be used in commercial products without a license from the University of British Columbia. For more information, refer to the file LICENSE.ubc that accompanied this distribution. @version 1.1.1-20070330 */ #include "sift.h" #include "imgfeatures.h" #include "utils.h" #include "cxcore.h" #include "cv.h" /************************* Local Function Prototypes *************************/ IplImage* create_init_img( IplImage*, int, double ); IplImage* convert_to_gray32( IplImage* ); IplImage*** build_gauss_pyr( IplImage*, int, int, double ); IplImage* downsample( IplImage* ); IplImage*** build_dog_pyr( IplImage***, int, int ); CvSeq* scale_space_extrema( IplImage***, int, int, double, int, CvMemStorage*); int is_extremum( IplImage***, int, int, int, int ); struct feature* interp_extremum( IplImage***, int, int, int, int, int, double); void interp_step( IplImage***, int, int, int, int, double*, double*, double* ); CvMat* deriv_3D( IplImage***, int, int, int, int ); CvMat* hessian_3D( IplImage***, int, int, int, int ); double interp_contr( IplImage***, int, int, int, int, double, double, double ); struct feature* new_feature( void ); int is_too_edge_like( IplImage*, int, int, int ); void calc_feature_scales( CvSeq*, double, int ); void adjust_for_img_dbl( CvSeq* ); void calc_feature_oris( CvSeq*, IplImage*** ); double* ori_hist( IplImage*, int, int, int, int, double ); int calc_grad_mag_ori( IplImage*, int, int, double*, double* ); void smooth_ori_hist( double*, int ); double dominant_ori( double*, int ); void add_good_ori_features( CvSeq*, double*, int, double, struct feature* ); struct feature* clone_feature( struct feature* ); void compute_descriptors( CvSeq*, IplImage***, int, int ); double*** descr_hist( IplImage*, int, int, double, double, int, int ); void interp_hist_entry( double***, double, double, double, double, int, int); void hist_to_descr( double***, int, int, struct feature* ); void normalize_descr( struct feature* ); int feature_cmp( void*, void*, void* ); void release_descr_hist( double****, int ); void release_pyr( IplImage****, int, int ); /*********************** Functions prototyped in sift.h **********************/ /** Finds SIFT features in an image using default parameter values. All detected features are stored in the array pointed to by \a feat. @param img the image in which to detect features @param feat a pointer to an array in which to store detected features @return Returns the number of features stored in \a feat or -1 on failure @see _sift_features() */ int sift_features( IplImage* img, struct feature** feat ) { return _sift_features( img, feat, SIFT_INTVLS, SIFT_SIGMA, SIFT_CONTR_THR, SIFT_CURV_THR, SIFT_IMG_DBL, SIFT_DESCR_WIDTH, SIFT_DESCR_HIST_BINS ); } /** Finds SIFT features in an image using user-specified parameter values. All detected features are stored in the array pointed to by \a feat. @param img the image in which to detect features @param fea a pointer to an array in which to store detected features @param intvls the number of intervals sampled per octave of scale space @param sigma the amount of Gaussian smoothing applied to each image level before building the scale space representation for an octave @param cont_thr a threshold on the value of the scale space function \f$\left|D(\hat{x})\right|\f$, where \f$\hat{x}\f$ is a vector specifying feature location and scale, used to reject unstable features; assumes pixel values in the range [0, 1] @param curv_thr threshold on a feature's ratio of principle curvatures used to reject features that are too edge-like @param img_dbl should be 1 if image doubling prior to scale space construction is desired or 0 if not @param descr_width the width, \f$n\f$, of the \f$n \times n\f$ array of orientation histograms used to compute a feature's descriptor @param descr_hist_bins the number of orientations in each of the histograms in the array used to compute a feature's descriptor @return Returns the number of keypoints stored in \a feat or -1 on failure @see sift_keypoints() */ int _sift_features( IplImage* img, struct feature** feat, int intvls, double sigma, double contr_thr, int curv_thr, int img_dbl, int descr_width, int descr_hist_bins ) { IplImage* init_img; IplImage*** gauss_pyr, *** dog_pyr; CvMemStorage* storage; CvSeq* features; int octvs, i, n = 0; /* check arguments */ if( ! img ) fatal_error( "NULL pointer error, %s, line %d", __FILE__, __LINE__ ); if( ! feat ) fatal_error( "NULL pointer error, %s, line %d", __FILE__, __LINE__ ); /* build scale space pyramid; smallest dimension of top level is ~4 pixels */ init_img = create_init_img( img, img_dbl, sigma ); octvs = log( MIN( init_img->width, init_img->height ) ) / log(2) - 2; gauss_pyr = build_gauss_pyr( init_img, octvs, intvls, sigma ); dog_pyr = build_dog_pyr( gauss_pyr, octvs, intvls ); storage = cvCreateMemStorage( 0 ); features = scale_space_extrema( dog_pyr, octvs, intvls, contr_thr, curv_thr, storage ); calc_feature_scales( features, sigma, intvls ); if( img_dbl ) adjust_for_img_dbl( features ); calc_feature_oris( features, gauss_pyr ); compute_descriptors( features, gauss_pyr, descr_width, descr_hist_bins ); /* sort features by decreasing scale and move from CvSeq to array */ cvSeqSort( features, (CvCmpFunc)feature_cmp, NULL ); n = features->total; *feat = calloc( n, sizeof(struct feature) ); *feat = cvCvtSeqToArray( features, *feat, CV_WHOLE_SEQ ); for( i = 0; i < n; i++ ) { free( (*feat)[i].feature_data ); (*feat)[i].feature_data = NULL; } cvReleaseMemStorage( &storage ); cvReleaseImage( &init_img ); release_pyr( &gauss_pyr, octvs, intvls + 3 ); release_pyr( &dog_pyr, octvs, intvls + 2 ); return n; } /************************ Functions prototyped here **************************/ /* Converts an image to 8-bit grayscale and Gaussian-smooths it. The image is optionally doubled in size prior to smoothing. @param img input image @param img_dbl if true, image is doubled in size prior to smoothing @param sigma total std of Gaussian smoothing */ IplImage* create_init_img( IplImage* img, int img_dbl, double sigma ) { IplImage* gray, * dbl; float sig_diff; gray = convert_to_gray32( img ); if( img_dbl ) { sig_diff = sqrt( sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA * 4 ); dbl = cvCreateImage( cvSize( img->width*2, img->height*2 ), IPL_DEPTH_32F, 1 ); cvResize( gray, dbl, CV_INTER_CUBIC ); cvSmooth( dbl, dbl, CV_GAUSSIAN, 0, 0, sig_diff, sig_diff );//去除噪声 cvReleaseImage( &gray ); return dbl; } else { sig_diff = sqrt( sigma * sigma - SIFT_INIT_SIGMA * SIFT_INIT_SIGMA ); cvSmooth( gray, gray, CV_GAUSSIAN, 0, 0, sig_diff, sig_diff ); return gray; } } /* Converts an image to 32-bit grayscale @param img a 3-channel 8-bit color (BGR) or 8-bit gray image @return Returns a 32-bit grayscale image */ IplImage* convert_to_gray32( IplImage* img ) { IplImage* gray8, * gray32; int r, c; gray8 = cvCreateImage( cvGetSize(img), IPL_DEPTH_8U, 1 ); gray32 = cvCreateImage( cvGetSize(img), IPL_DEPTH_32F, 1 ); if( img->nChannels == 1 ) gray8 = cvClone( img ); else cvCvtColor( img, gray8, CV_RGB2GRAY ); cvConvertScale( gray8, gray32, 1.0 / 255.0, 0 ); cvReleaseImage( &gray8 ); return gray32; } /* Builds Gaussian scale space pyramid from an image @param base base image of the pyramid @param octv