RobHess的SIFT源码分析：kdtree.h和kdtree.c文件
SIFT源码分析系列文章的索引在这里：RobHess的SIFT源码分析：综述
kdtree.h和kdtree.c这两个文件中实现了k-d树的建立以及用BBF(Best Bin First)算法搜索匹配点的函数。
如果你需要对两个图片中的特征点进行匹配，就要用到这两个文件。
关于k-d树的理解，参考这篇文章，写的挺好：http://blog.csdn.net/ijuliet/article/details/4471311
kdtree.h文件
/**@file Functions and structures for maintaining a k-d tree database of image features. For more information, refer to: Beis, J. S. and Lowe, D. G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In <EM>Conference on Computer Vision and Pattern Recognition (CVPR)</EM> (2003), pp. 1000--1006. Copyright (C) 2006-2010 Rob Hess <[email protected]> @version 1.1.2-20100521 */ /* 此文件中包含K-D树的建立与搜索函数的声明 */ #ifndef KDTREE_H #define KDTREE_H #include "cxcore.h" /********************************* Structures ********************************/ struct feature; /*K-D树中的结点结构*/ /** a node in a k-d tree */ struct kd_node { int ki; /**< partition key index */ //分割位置(枢轴)的维数索引(哪一维是分割位置)，取值为1-128 double kv; /**< partition key value */ //枢轴的值(所有特征向量在枢轴索引维数上的分量的中值) int leaf; /**< 1 if node is a leaf, 0 otherwise */ //是否叶子结点的标志 struct feature* features; /**< features at this node */ //此结点对应的特征点集合(数组) int n; /**< number of features */ //特征点的个数 struct kd_node* kd_left; /**< left child */ //左子树 struct kd_node* kd_right; /**< right child */ //右子树 }; /*************************** Function Prototypes *****************************/ /*根据给定的特征点集合建立k-d树 参数： features：特征点数组，注意：此函数将会改变features数组中元素的排列顺序 n：特征点个数 返回值：建立好的k-d树的树根指针 */ /** A function to build a k-d tree database from keypoints in an array. @param features an array of features; <EM>this function rearranges the order of the features in this array, so you should take appropriate measures if you are relying on the order of the features (e.g. call this function before order is important)</EM> @param n the number of features in \a features @return Returns the root of a kd tree built from \a features. */ extern struct kd_node* kdtree_build( struct feature* features, int n ); /*用BBF算法在k-d树中查找指定特征点的k个最近邻特征点 参数： kd_root：图像特征的k-d树的树根 feat：目标特征点 k：近邻个数 nbrs：k个近邻特征点的指针数组，按到目标特征点的距离升序排列 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(*nbrs) max_nn_chks：搜索的最大次数，超过此值不再搜索 返回值：存储在nbrs中的近邻个数，返回-1表示失败 */ /** Finds an image feature's approximate k nearest neighbors in a kd tree using Best Bin First search. @param kd_root root of an image feature kd tree @param feat image feature for whose neighbors to search @param k number of neighbors to find @param nbrs pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance; memory for this array is allocated by this function and must be freed by the caller using free(*nbrs) @param max_nn_chks search is cut off after examining this many tree entries @return Returns the number of neighbors found and stored in \a nbrs, or -1 on error. */ extern int kdtree_bbf_knn( struct kd_node* kd_root, struct feature* feat, int k, struct feature*** nbrs, int max_nn_chks ); /** Finds an image feature's approximate k nearest neighbors within a specified spatial region in a kd tree using Best Bin First search. @param kd_root root of an image feature kd tree @param feat image feature for whose neighbors to search @param k number of neighbors to find @param nbrs pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance; memory for this array is allocated by this function and must be freed by the caller using free(*nbrs) @param max_nn_chks search is cut off after examining this many tree entries @param rect rectangular region in which to search for neighbors @param model if true, spatial search is based on kdtree features' model locations; otherwise it is based on their image locations @return Returns the number of neighbors found and stored in \a nbrs (in case \a k neighbors could not be found before examining \a max_nn_checks keypoint entries). */ extern int kdtree_bbf_spatial_knn( struct kd_node* kd_root, struct feature* feat, int k, struct feature*** nbrs, int max_nn_chks, CvRect rect, int model ); /*释放k-d树占用的存储空间 */ /** De-allocates memory held by a kd tree @param kd_root pointer to the root of a kd tree */ extern void kdtree_release( struct kd_node* kd_root ); #endif
kdtree.c文件
/* Functions and structures for maintaining a k-d tree database of image features. For more information, refer to: Beis, J. S. and Lowe, D. G. Shape indexing using approximate nearest-neighbor search in high-dimensional spaces. In <EM>Conference on Computer Vision and Pattern Recognition (CVPR)</EM> (2003), pp. 1000--1006. Copyright (C) 2006-2010 Rob Hess <[email protected]> @version 1.1.2-20100521 */ /* 此文件中有k-d树的建立和BBF查找函数的实现 */ #include "kdtree.h" #include "minpq.h" #include "imgfeatures.h" #include "utils.h" #include <cxcore.h> #include <stdio.h> //BBF中用到的结构，可存储当前点到目标点的距离 //在kd树搜索过程中，此类型数据会被赋值给feature结构的feature_data成员 struct bbf_data { double d; //此特征点到目标点的欧式距离值 void* old_data; //保存此特征点的feature_data域的以前的值 }; /************************ 未暴露接口的一些本地函数的声明 **************************/ /************************* Local Function Prototypes *************************/ //用给定的特征点集初始化k-d树节点 static struct kd_node* kd_node_init( struct feature*, int ); //扩展指定的k-d树节点及其左右孩子 static void expand_kd_node_subtree( struct kd_node* ); //确定输入节点的枢轴索引和枢轴值 static void assign_part_key( struct kd_node* ); //找到输入数组的中值 static double median_select( double*, int ); //找到输入数组中第r小的数 static double rank_select( double*, int, int ); //用插入法对输入数组进行升序排序 static void insertion_sort( double*, int ); //根据给定的枢轴值分割数组，使数组前部分小于pivot，后部分大于pivot static int partition_array( double*, int, double ); //在指定的k-d树节点上划分特征点集 static void partition_features( struct kd_node* ); //从给定结点搜索k-d树直到叶节点，搜索过程中将未搜索的节点根据优先级放入队列 static struct kd_node* explore_to_leaf( struct kd_node*, struct feature*,struct min_pq* ); //插入一个特征点到最近邻数组，使数组中的点按到目标点的距离升序排列 static int insert_into_nbr_array( struct feature*, struct feature**, int, int ); //判断给定点是否在某矩形中 static int within_rect( CvPoint2D64f, CvRect ); /******************** 已在kdtree.h中声明的函数 **********************/ /******************** Functions prototyped in kdtree.h **********************/ /*根据给定的特征点集合建立k-d树 参数： features：特征点数组，注意：此函数将会改变features数组中元素的排列顺序 n：特征点个数 返回值：建立好的k-d树的树根指针 */ /* A function to build a k-d tree database from keypoints in an array. @param features an array of features @param n the number of features in features @return Returns the root of a kd tree built from features or NULL on error. */ struct kd_node* kdtree_build( struct feature* features, int n ) { struct kd_node* kd_root; //输入参数检查 if( ! features || n <= 0 ) { fprintf( stderr, "Warning: kdtree_build(): no features, %s, line %d\n", __FILE__, __LINE__ ); return NULL; } //调用函数，用给定的特征点集初始化k-d树节点，返回值作为树根 kd_root = kd_node_init( features, n ); //调用函数，扩展根节点kd_root及其左右孩子 expand_kd_node_subtree( kd_root ); return kd_root; } /*用BBF算法在k-d树中查找指定特征点的k个最近邻特征点 参数： kd_root：图像特征的k-d树的树根 feat：目标特征点 k：近邻个数 nbrs：k个近邻特征点的指针数组，按到目标特征点的距离升序排列 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(*nbrs) max_nn_chks：搜索的最大次数，超过此值不再搜索 返回值：存储在nbrs中的近邻个数，返回-1表示失败 */ /* Finds an image feature's approximate k nearest neighbors in a kd tree using Best Bin First search. @param kd_root root of an image feature kd tree @param feat image feature for whose neighbors to search @param k number of neighbors to find @param nbrs pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance @param max_nn_chks search is cut off after examining this many tree entries @return Returns the number of neighbors found and stored in nbrs, or -1 on error. */ int kdtree_bbf_knn( struct kd_node* kd_root, struct feature* feat, int k, struct feature*** nbrs, int max_nn_chks ) { struct kd_node* expl; //expl是当前搜索节点 struct min_pq* min_pq; //优先级队列 struct feature* tree_feat, ** _nbrs; //tree_feat是单个SIFT特征，_nbrs中存放着查找出来的近邻特征节点 struct bbf_data* bbf_data; //bbf_data是一个用来存放临时特征数据和特征间距离的缓存结构 int i, t = 0, n = 0; //t是搜索的最大次数，n是当前最近邻数组中的元素个数 //输入参数检查 if( ! nbrs || ! feat || ! kd_root ) { fprintf( stderr, "Warning: NULL pointer error, %s, line %d\n", __FILE__, __LINE__ ); return -1; } _nbrs = calloc( k, sizeof( struct feature* ) ); //给查找结果分配相应大小的内存 min_pq = minpq_init(); //min_pq队列初始化，分配默认大小的空间 minpq_insert( min_pq, kd_root, 0 ); //将根节点先插入到min_pq优先级队列中 //min_pq队列没有回溯完且未达到搜索最大次数 while( min_pq->n > 0 && t < max_nn_chks ) { //从min_pq中提取(并移除)优先级最高的节点，赋值给当前节点expl expl = (struct kd_node*)minpq_extract_min( min_pq ); if( ! expl ) { //出错处理 fprintf( stderr, "Warning: PQ unexpectedly empty, %s line %d\n",__FILE__, __LINE__ ); goto fail; } //从当前搜索节点expl一直搜索到叶子节点，搜索过程中将未搜索的节点根据优先级放入队列，返回值为叶子节点 expl = explore_to_leaf( expl, feat, min_pq ); if( ! expl ) { //出错处理 fprintf( stderr, "Warning: PQ unexpectedly empty, %s line %d\n",__FILE__, __LINE__ ); goto fail; } //比较查找最近邻 for( i = 0; i < expl->n; i++ ) { tree_feat = &expl->features[i];//第i个特征点的指针 bbf_data = malloc( sizeof( struct bbf_data ) );//新建bbf结构 if( ! bbf_data ) { //出错处理 fprintf( stderr, "Warning: unable to allocate memory," " %s line %d\n", __FILE__, __LINE__ ); goto fail; } bbf_data->old_data = tree_feat->feature_data;//保存第i个特征点的feature_data域以前的值 bbf_data->d = descr_dist_sq(feat, tree_feat);//当前搜索点和目标点之间的欧氏距离 tree_feat->feature_data = bbf_data;//将bbf结构赋给此特征点的feature_data域 //判断并插入符合条件的特征点到最近邻数组_nbrs中,插入成功返回1 //当最近邻数组中元素个数已达到k时，继续插入元素个数不会增加，但会更新元素的值 n += insert_into_nbr_array( tree_feat, _nbrs, n, k ); } t++;//搜索次数 } minpq_release( &min_pq );//释放优先队列 //对于最近邻数组中的特征点，恢复其feature_data域的值 for( i = 0; i < n; i++ ) { bbf_data = _nbrs[i]->feature_data; _nbrs[i]->feature_data = bbf_data->old_data;//将之前的数据赋值给feature_data域 free( bbf_data ); } *nbrs = _nbrs; return n; //失败处理 fail: minpq_release( &min_pq ); //对于最近邻数组中的特征点，恢复其feature_data域的值 for( i = 0; i < n; i++ ) { bbf_data = _nbrs[i]->feature_data; _nbrs[i]->feature_data = bbf_data->old_data; free( bbf_data ); } free( _nbrs ); *nbrs = NULL; return -1; } /* Finds an image feature's approximate k nearest neighbors within a specified spatial region in a kd tree using Best Bin First search. @param kd_root root of an image feature kd tree @param feat image feature for whose neighbors to search @param k number of neighbors to find @param nbrs pointer to an array in which to store pointers to neighbors in order of increasing descriptor distance @param max_nn_chks search is cut off after examining this many tree entries @param rect rectangular region in which to search for neighbors @param model if true, spatial search is based on kdtree features' model locations; otherwise it is based on their image locations @return Returns the number of neighbors found and stored in \a nbrs (in case \a k neighbors could not be found before examining \a max_nn_checks keypoint entries). */ int kdtree_bbf_spatial_knn( struct kd_node* kd_root, struct feature* feat, int k, struct feature*** nbrs, int max_nn_chks, CvRect rect, int model ) { struct feature** all_nbrs, ** sp_nbrs; CvPoint2D64f pt; int i, n, t = 0; n = kdtree_bbf_knn( kd_root, feat, max_nn_chks, &all_nbrs, max_nn_chks ); sp_nbrs = calloc( k, sizeof( struct feature* ) ); for( i = 0; i < n; i++ ) { if( model ) pt = all_nbrs[i]->mdl_pt; else pt = all_nbrs[i]->img_pt; if( within_rect( pt, rect ) ) { sp_nbrs[t++] = all_nbrs[i]; if( t == k ) goto end; } } end: free( all_nbrs ); *nbrs = sp_nbrs; return t; } /*释放k-d树占用的存储空间 */ /* De-allocates memory held by a kd tree @param kd_root pointer to the root of a kd tree */ void kdtree_release( struct kd_node* kd_root ) { if( ! kd_root ) return; kdtree_release( kd_root->kd_left ); kdtree_release( kd_root->kd_right ); free( kd_root ); } /************************ 未暴露接口的一些本地函数 **************************/ /************************ Functions prototyped here **************************/ /*用给定的特征点集初始化k-d树节点 参数： features：特征点集 n：特征点个数 返回值：k-d树节点指针 */ /* Initializes a kd tree node with a set of features. The node is not expanded, and no ordering is imposed on the features. @param features an array of image features @param n number of features @return Returns an unexpanded kd-tree node. */ static struct kd_node* kd_node_init( struct feature* features, int n ) { struct kd_node* kd_node; kd_node = malloc( sizeof( struct kd_node ) );//分配内存 memset( kd_node, 0, sizeof( struct kd_node ) ); kd_node->ki = -1;//枢轴索引 kd_node->features = features;//节点对应的特征点集 kd_node->n = n;//特征点的个数 return kd_node; } /*递归的扩展指定的k-d树节点及其左右孩子 */ /* Recursively expands a specified kd tree node into a tree whose leaves contain one entry each. @param kd_node an unexpanded node in a kd tree */ static void expand_kd_node_subtree( struct kd_node* kd_node ) { //基本情况：叶子节点 /* base case: leaf node */ if( kd_node->n == 1 || kd_node->n == 0 ) { kd_node->leaf = 1;//叶节点标志位设为1 return; } //调用函数，确定节点的枢轴索引和枢轴值 assign_part_key( kd_node ); //在指定k-d树节点上划分特征点集(即根据指定节点的ki和kv值来划分特征点集) partition_features( kd_node ); //继续扩展左右孩子 if( kd_node->kd_left ) expand_kd_node_subtree( kd_node->kd_left ); if( kd_node->kd_right ) expand_kd_node_subtree( kd_node->kd_right ); } /*确定输入节点的枢轴索引和枢轴值 参数：kd_node：输入的k-d树节点 函数执行完后将给kd_node的ki和kv成员复制 */ /* Determines the descriptor index at which and the value with which to partition a kd tree node's features. @param kd_node a kd tree node */ static void assign_part_key( struct kd_node* kd_node ) { struct feature* features; //枢轴的值kv，均值mean，方差var，方差最大值var_max double kv, x, mean, var, var_max = 0; double* tmp; int d, n, i, j, ki = 0; //枢轴索引ki features = kd_node->features; n = kd_node->n;//结点个数 d = features[0].d;//特征向量的维数 //枢轴的索引值就是方差最大的那一维的维数,即n个128维的特征向量中，若第k维的方差最大，则k就是枢轴(分割位置) /* partition key index is that along which descriptors have most variance */ for( j = 0; j < d; j++ ) { mean = var = 0; //求第j维的均值 for( i = 0; i < n; i++ ) mean += features[i].descr[j]; mean /= n; //求第j维的方差 for( i = 0; i < n; i++ ) { x = features[i].descr[j] - mean; var += x * x; } var /= n; //找到最大方差的维数 if( var > var_max ) { ki = j;//最大方差的维数就是枢轴 var_max = var; } } //枢轴的值就是所有特征向量的ki维的中值(按ki维排序后中间的那个值) /* partition key value is median of descriptor values at ki */ tmp = calloc( n, sizeof( double ) ); for( i = 0; i < n; i++ ) tmp[i] = features[i].descr[ki]; //调用函数，找tmp数组的中值 kv = median_select( tmp, n ); free( tmp ); kd_node->ki = ki;//枢轴的维数索引 kd_node->kv = kv;//枢轴的值 } /*找到输入数组的中值 参数： array：输入数组，元素顺序将会被改动 n：元素个数 返回值：中值 */ /* Finds the median value of an array. The array's elements are re-ordered by this function. @param array an array; the order of its elelemts is reordered @param n number of elements in array @return Returns the median value of array. */ static double median_select( double* array, int n ) { //调用函数，找array数组中的第(n-1)/2小的数，即中值 return rank_select( array, n, (n - 1) / 2 ); } /*找到输入数组中第r小的数 参数： array：输入数组，元素顺序将会被改动 n：元素个数 r：找第r小元素 返回值：第r小的元素值 */ /* Finds the element of a specified rank in an array using the linear time median-of-medians algorithm by Blum, Floyd, Pratt, Rivest, and Tarjan. The elements of the array are re-ordered by this function. @param array an array; the order of its elelemts is reordered @param n number of elements in array @param r the zero-based rank of the element to be selected @return Returns the element from array with zero-based rank r. */ static double rank_select( double* array, int n, int r ) { double* tmp, med; int gr_5, gr_tot, rem_elts, i, j; /* base case */ if( n == 1 ) return array[0]; //将数组分成5个一组，共gr_tot组 /* divide array into groups of 5 and sort them */ gr_5 = n / 5; //组的个数-1，n/5向下取整 gr_tot = cvCeil( n / 5.0 ); //组的个数，n/5向上取整 rem_elts = n % 5;//最后一组中的元素个数 tmp = array; //对每组进行插入排序 for( i = 0; i < gr_5; i++ ) { insertion_sort( tmp, 5 ); tmp += 5; } //最后一组 insertion_sort( tmp, rem_elts ); //找中值的中值 /* recursively find the median of the medians of the groups of 5 */ tmp = calloc( gr_tot, sizeof( double ) ); //将每个5元组中的中值(即下标为2,2+5,...的元素)复制到temp数组 for( i = 0, j = 2; i < gr_5; i++, j += 5 ) tmp[i] = array[j]; //最后一组的中值 if( rem_elts ) tmp[i++] = array[n - 1 - rem_elts/2]; //找temp中的中值med，即中值的中值 med = rank_select( tmp, i, ( i - 1 ) / 2 ); free( tmp ); //利用中值的中值划分数组，看划分结果是否是第r小的数，若不是则递归调用rank_select重新选择 /* partition around median of medians and recursively select if necessary */ j = partition_array( array, n, med );//划分数组，返回med在新数组中的索引 if( r == j )//结果是第r小的数 return med; else if( r < j )//第r小的数在前半部分 return rank_select( array, j, r ); else//第r小的数在后半部分 { array += j+1; return rank_select( array, ( n - j - 1 ), ( r - j - 1 ) ); } } /*用插入法对输入数组进行升序排序 参数： array：输入数组 n：元素个数 */ /* Sorts an array in place into increasing order using insertion sort. @param array an array @param n number of elements */ static void insertion_sort( double* array, int n ) { double k; int i, j; for( i = 1; i < n; i++ ) { k = array[i]; j = i-1; while( j >= 0 && array[j] > k ) { array[j+1] = array[j]; j -= 1; } array[j+1] = k; } } /*根据给定的枢轴值分割数组，使数组前部分小于pivot，后部分大于pivot 参数： array：输入数组 n：数组的元素个数 pivot：枢轴值 返回值：分割后枢轴的下标 */ /* Partitions an array around a specified value. @param array an array @param n number of elements @param pivot value around which to partition @return Returns index of the pivot after partitioning */ static int partition_array( double* array, int n, double pivot ) { double tmp; int p, i, j; i = -1; for( j = 0; j < n; j++ ) if( array[j] <= pivot ) { tmp = array[++i]; array[i] = array[j]; array[j] = tmp; if( array[i] == pivot ) p = i;//p保存枢轴的下标 } //将枢轴和最后一个小于枢轴的数对换 array[p] = array[i]; array[i] = pivot; return i; } /*在指定的k-d树节点上划分特征点集 使得特征点集的前半部分是第ki维小于枢轴的点，后半部分是第ki维大于枢轴的点 */ /* Partitions the features at a specified kd tree node to create its two children. @param kd_node a kd tree node whose partition key is set */ static void partition_features( struct kd_node* kd_node ) { struct feature* features, tmp; double kv; int n, ki, p, i, j = -1; features = kd_node->features;//特征点数组 n = kd_node->n;//特征点个数 //printf("%d\n",n); ki = kd_node->ki;//枢轴的维数索引(哪一维是枢轴) kv = kd_node->kv;//枢轴的值 for( i = 0; i < n; i++ ) { //若第i个特征点的特征向量的第ki维的值小于kv if( features[i].descr[ki] <= kv ) { tmp = features[++j]; features[j] = features[i]; features[i] = tmp; if( features[j].descr[ki] == kv ) p = j;//p保存枢轴所在的位置 } } //将枢轴features[p]和最后一个小于枢轴的点features[j]对换 tmp = features[p]; features[p] = features[j]; features[j] = tmp; //此后，枢轴的位置下标为j //若所有特征点落在同一侧，则此节点成为叶节点 /* if all records fall on same side of partition, make node a leaf */ if( j == n - 1 ) { kd_node->leaf = 1; return; } //初始化左孩子的根节点，左孩子共j+1个特征点 kd_node->kd_left = kd_node_init( features, j + 1 ); //初始化右孩子的根节点，右孩子共n-j-1个特征点 kd_node->kd_right = kd_node_init( features + ( j + 1 ), ( n - j - 1 ) ); } /*从给定结点搜索k-d树直到叶节点，搜索过程中将未搜索的节点根据优先级放入队列 优先级队列和搜索路径是同时生成的，这也是BBF算法的精髓所在：在二叉搜索的时 候将搜索路径另一侧的分支加入到优先级队列中，供回溯时查找。而优先级队列的排 序就是根据目标特征与分割超平面的距离ABS(kv - feat->descr[ki]) 参数： kd_node：要搜索的子树的树根 feat：目标特征点 min_pq：优先级队列 返回值：叶子节点的指针 */ /* Explores a kd tree from a given node to a leaf. Branching decisions are made at each node based on the descriptor of a given feature. Each node examined but not explored is put into a priority queue to be explored later, keyed based on the distance from its partition key value to the given feature's desctiptor. @param kd_node root of the subtree to be explored @param feat feature upon which branching decisions are based @param min_pq a minimizing priority queue into which tree nodes are placed as described above @return Returns a pointer to the leaf node at which exploration ends or NULL on error. */ static struct kd_node* explore_to_leaf( struct kd_node* kd_node, struct feature* feat, struct min_pq* min_pq ) { //unexpl中存放着优先级队列的候选节点(还未搜索的节点)，expl为当前搜索节点 struct kd_node* unexpl, * expl = kd_node; double kv;//分割枢轴的值 int ki;//分割枢轴的维数索引 //一直搜索到叶子节点，搜索过程中将未搜索的节点根据优先级放入队列 while( expl && ! expl->leaf ) { ki = expl->ki; kv = expl->kv; //枢轴的维数索引大于特征点的维数，出错 if( ki >= feat->d ) { fprintf( stderr, "Warning: comparing imcompatible descriptors, %s" " line %d\n", __FILE__, __LINE__ ); return NULL; } //目标特征点ki维的数据小于等于kv，进入左子树搜索 if( feat->descr[ki] <= kv ) { unexpl = expl->kd_right;//候选搜索节点是expl的右子树的根 expl = expl->kd_left;//当前搜索节点是expl的左子树的根 } else//目标特征点ki维的数据大于kv，进入右子树搜索 { unexpl = expl->kd_left;//候选搜索节点是expl的左子树 expl = expl->kd_right;//当前搜索节点是expl的右子树 } //将候选节点unexpl根据目标特征点ki维与其父节点的距离插入到优先队列中，距离越小，优先级越大 if( minpq_insert( min_pq, unexpl, ABS( kv - feat->descr[ki] ) ) ) { fprintf( stderr, "Warning: unable to insert into PQ, %s, line %d\n",__FILE__, __LINE__ ); return NULL; } } return expl;//返回叶子节点的指针 } /*插入一个特征点到最近邻数组，使数组中的点按到目标点的距离升序排列 参数： feat：要插入的特征点，其feature_data域应是指向bbf_data结构的指针，其中的d值时feat和目标点的距离的平方 nbrs：最近邻数组 n：已在最近邻数组中的元素个数 k：最近邻数组元素个数的最大值 返回值：若feat成功插入，返回1，否则返回0 */ /* Inserts a feature into the nearest-neighbor array so that the array remains in order of increasing descriptor distance from the search feature. @param feat feature to be inderted into the array; it's feature_data field should be a pointer to a bbf_data with d equal to the squared descriptor distance between feat and the search feature @param nbrs array of nearest neighbors neighbors @param n number of elements already in nbrs and @param k maximum number of elements in nbrs @return If feat was successfully inserted into nbrs, returns 1; otherwise returns 0. */ static int insert_into_nbr_array( struct feature* feat, struct feature** nbrs, int n, int k ) { struct bbf_data* fdata, * ndata;//fdata是要插入的点的bbf结构，ndata是最近邻数组中的点的bbf结构 double dn, df; //dn是最近邻数组中特征点的bbf结构中的距离值，df是要插入的特征点的bbf结构中的距离值 int i, ret = 0; //原最近邻数组为空 if( n == 0 ) { nbrs[0] = feat; return 1;//插入成功，返回1 } /* check at end of array */ fdata = (struct bbf_data*)feat->feature_data;//要插入点的bbf结构 df = fdata->d;//要插入的特征点的bbf结构中的距离值 ndata = (struct bbf_data*)nbrs[n-1]->feature_data;//最近邻数组中的点的bbf结构 dn = ndata->d;//最近邻数组中最后一个特征点的bbf结构中的距离值 //df>=dn，说明要插入在最近邻数组的末尾 if( df >= dn ) { //最近邻数组中元素个数已达到最大值，无法插入 if( n == k ) { feat->feature_data = fdata->old_data;//不明白这里是干什么？ free( fdata ); return 0;//插入失败，返回0 } nbrs[n] = feat;//插入到末尾 return 1;//插入成功，返回1 } //运行到此处说明插入位置不在数组末尾 /* find the right place in the array */ if( n < k )//最近邻数组中元素个数小于最大值，可插入 { nbrs[n] = nbrs[n-1];//原数组最后一个点后移 ret = 1;//插入结果设为1 } else//最近邻数组中元素个数大于或等于最大值，无法插入，插入结果ret还是0 {//其实也不是无法插入，而是最近邻数组中元素个数不变，但值会更新 nbrs[n-1]->feature_data = ndata->old_data; free( ndata ); } i = n-2; //在最近邻数组中查找要插入的位置 while( i >= 0 ) { ndata = (struct bbf_data*)nbrs[i]->feature_data; dn = ndata->d; if( dn <= df )//找到插入点 break; nbrs[i+1] = nbrs[i];//一次后移 i--; } i++; nbrs[i] = feat;//插入 return ret;//返回结果 } /*判断给定点是否在某矩形中 */ /* Determines whether a given point lies within a specified rectangular region @param pt point @param rect rectangular region @return Returns 1 if pt is inside rect or 0 otherwise */ static int within_rect( CvPoint2D64f pt, CvRect rect ) { if( pt.x < rect.x || pt.y < rect.y ) return 0; if( pt.x > rect.x + rect.width || pt.y > rect.y + rect.height ) return 0; return 1; }