基于SIFT算法的图像拼接.rar【vs2010+opencv2.4.10工程】

/* 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) 2006-2010 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 1.1.2-20100521 */ /* 此文件最重要 包含SIFT特征点检测的实现 */ #include "sift.h" #include "imgfeatures.h" #include "utils.h" #include "opencv\cxcore.h" #include "opencv\cv.h" /************************ 未暴露接口的一些本地函数的声明 **************************/ /************************* Local Function Prototypes *************************/ //将原图转换为32位灰度图并归一化，然后进行一次高斯平滑，并根据参数img_dbl决定是否将图像尺寸放大为原图的2倍 static IplImage* create_init_img( IplImage*, int, double ); //将输入图像转换为32位灰度图,并进行归一化 static IplImage* convert_to_gray32( IplImage* ); //根据输入参数建立高斯金字塔 static IplImage*** build_gauss_pyr( IplImage*, int, int, double ); //对输入图像做下采样生成其四分之一大小的图像(每个维度上减半)，使用最近邻差值方法 static IplImage* downsample( IplImage* ); //通过对高斯金字塔中每相邻两层图像相减来建立高斯差分金字塔 static IplImage*** build_dog_pyr( IplImage***, int, int ); //在尺度空间中检测极值点，通过插值精确定位，去除低对比度的点，去除边缘点，返回检测到的特征点序列 static CvSeq* scale_space_extrema( IplImage***, int, int, double, int, CvMemStorage*); //通过在尺度空间中将一个像素点的值与其周围3*3*3邻域内的点比较来决定此点是否极值点(极大值或极小都行) static int is_extremum( IplImage***, int, int, int, int ); //通过亚像素级插值进行极值点精确定位(修正极值点坐标)，并去除低对比度的极值点，将修正后的特征点组成feature结构返回 static struct feature* interp_extremum( IplImage***, int, int, int, int, int, double); //进行一次极值点差值，计算x，y，σ方向(层方向)上的子像素偏移量(增量) static void interp_step( IplImage***, int, int, int, int, double*, double*, double* ); //在DoG金字塔中计算某点的x方向、y方向以及尺度方向上的偏导数 static CvMat* deriv_3D( IplImage***, int, int, int, int ); //在DoG金字塔中计算某点的3*3海森矩阵 static CvMat* hessian_3D( IplImage***, int, int, int, int ); //计算被插值点的对比度：D + 0.5 * dD^T * X static double interp_contr( IplImage***, int, int, int, int, double, double, double ); //为一个feature结构分配空间并初始化 static struct feature* new_feature( void ); //去除边缘响应，即通过计算主曲率比值判断某点是否边缘点 static int is_too_edge_like( IplImage*, int, int, int ); //计算特征点序列中每个特征点的尺度 static void calc_feature_scales( CvSeq*, double, int ); //将特征点序列中每个特征点的坐标减半(当设置了将图像放大为原图的2倍时，特征点检测完之后调用) static void adjust_for_img_dbl( CvSeq* ); //计算每个特征点的梯度直方图，找出其主方向，若一个特征点有不止一个主方向，将其分为两个特征点 static void calc_feature_oris( CvSeq*, IplImage*** ); //计算指定像素点的梯度方向直方图，返回存放直方图的数组 static double* ori_hist( IplImage*, int, int, int, int, double ); //计算指定点的梯度的幅值magnitude和方向orientation static int calc_grad_mag_ori( IplImage*, int, int, double*, double* ); //对梯度方向直方图进行高斯平滑，弥补因没有仿射不变性而产生的特征点不稳定的问题 static void smooth_ori_hist( double*, int ); //查找梯度直方图中主方向的梯度幅值，即查找直方图中最大bin的值 static double dominant_ori( double*, int ); //若当前特征点的直方图中某个bin的值大于给定的阈值，则新生成一个特征点并添加到特征点序列末尾 static void add_good_ori_features( CvSeq*, double*, int, double, struct feature* ); //对输入的feature结构特征点做深拷贝，返回克隆生成的特征点的指针 static struct feature* clone_feature( struct feature* ); //计算特征点序列中每个特征点的特征描述子向量 static void compute_descriptors( CvSeq*, IplImage***, int, int ); //计算特征点附近区域的方向直方图，此直方图在计算特征描述子中要用到，返回值是一个d*d*n的三维数组 static double*** descr_hist( IplImage*, int, int, double, double, int, int ); static void interp_hist_entry( double***, double, double, double, double, int, int); //将某特征点的方向直方图转换为特征描述子向量，对特征描述子归一化并将所有元素转化为整型，存入指定特征点中 static void hist_to_descr( double***, int, int, struct feature* ); //归一化特征点的特征描述子，即将特征描述子数组中每个元素除以特征描述子的模 static void normalize_descr( struct feature* ); //比较函数，将特征点按尺度的降序排列，用在序列排序函数CvSeqSort中 static int feature_cmp( void*, void*, void* ); //释放计算特征描述子过程中用到的方向直方图的内存空间 static void release_descr_hist( double****, int ); //释放金字塔图像组的存储空间 static void release_pyr( IplImage****, int, int ); /*********************** Functions prototyped in sift.h **********************/ /*使用默认参数在图像中提取SIFT特征点 参数： img：图像指针 feat：用来存储特征点的feature数组的指针 此数组的内存将在本函数中被分配，使用完后必须在调用出释放：free(*feat) 返回值：提取的特征点个数，若返回-1表明提取失败 */ /** 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 ) { //调用_sift_features()函数进行特征点检测 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 ); } /*使用用户指定的参数在图像中提取SIFT特征点 参数： img：输入图像 feat：存储特征点的数组的指针 此数组的内存将在本函数中被分配，使用完后必须在调用出释放：free(*feat) intvls：每组的层数 sigma：初始高斯平滑参数σ contr_thr：对比度阈值，针对归一化后的图像，用来去除不稳定特征 curv_thr：去除边缘的特征的主曲率阈值 img_dbl：是否将图像放大为之前的两倍 descr_width：特征描述过程中，计算方向直方图时，将特征点附近划分为descr_width*descr_width个区域，每个区域生成一个直方图 descr_hist_bins：特征描述过程中，每个直方图中bin的个数 返回值：提取的特征点个数，若返回-1表明提取失败 */ /** 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 feat 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 )