/*
Computes a canonical orientation for each image feature in an array. Based
on Section 5 of Lowe's paper. This function adds features to the array when
there is more than one dominant orientation at a given feature location.
@param features an array of image features
@param gauss_pyr Gaussian scale space pyramid
*/
static void calc_feature_oris( CvSeq* features, IplImage*** gauss_pyr )
{
struct feature* feat;
struct detection_data* ddata;
double* hist;
double omax;
int i, j, n = features->total;
for( i = 0; i < n; i++ )
{
feat = malloc( sizeof( struct feature ) );
cvSeqPopFront( features, feat );
ddata = feat_detection_data( feat );
/*Each sample added to the hstogram is weighted by its gradient magnitude and by a gausian-weighted circular
window with a sigma that is 1.5 times that of the scale of the keypoint*/
hist = ori_hist( gauss_pyr[ddata->octv][ddata->intvl],
ddata->r, ddata->c, SIFT_ORI_HIST_BINS,
cvRound( SIFT_ORI_RADIUS * ddata->scl_octv ),//主方向分配时使用的区域半径
SIFT_ORI_SIG_FCTR * ddata->scl_octv );//特征点主方向分配时高斯平滑σ为1.5倍特征点所在的尺度
for( j = 0; j < SIFT_ORI_SMOOTH_PASSES; j++ )
smooth_ori_hist( hist, SIFT_ORI_HIST_BINS );//使用高斯函数对直方图进行平滑，减少突变的影响
omax = dominant_ori( hist, SIFT_ORI_HIST_BINS );//在直方图中找到主方向的梯度
add_good_ori_features( features, hist, SIFT_ORI_HIST_BINS,//将大于某一个梯度大小阈值的特征向量加入到直方图中去
omax * SIFT_ORI_PEAK_RATIO, feat );
free( ddata );
free( feat );
free( hist );
}
}
/*
Computes a canonical orientation for each image feature in an array. Based
on Section 5 of Lowe's paper. This function adds features to the array when
there is more than one dominant orientation at a given feature location.
@param features an array of image features
@param gauss_pyr Gaussian scale space pyramid
*/
void calc_feature_oris( CvSeq* features, IplImage*** gauss_pyr )
{
struct feature* feat;
struct detection_data* ddata;
double* hist;
double omax;
int i, j, n = features->total;
for( i = 0; i < n; i++ )
{
feat = (feature* )malloc( sizeof( struct feature ) );
cvSeqPopFront( features, feat );
ddata = feat_detection_data( feat );
hist = ori_hist( gauss_pyr[ddata->octv][ddata->intvl],
ddata->r, ddata->c, SIFT_ORI_HIST_BINS,
cvRound( SIFT_ORI_RADIUS * ddata->scl_octv ),
SIFT_ORI_SIG_FCTR * ddata->scl_octv );
for( j = 0; j < SIFT_ORI_SMOOTH_PASSES; j++ )
smooth_ori_hist( hist, SIFT_ORI_HIST_BINS );
omax = dominant_ori( hist, SIFT_ORI_HIST_BINS );
add_good_ori_features( features, hist, SIFT_ORI_HIST_BINS,
omax * SIFT_ORI_PEAK_RATIO, feat );
free( ddata );
free( feat );
free( hist );
}
}
/*
Computes a canonical orientation for each image feature in an array. Based
on Section 5 of Lowe's paper. This function adds features to the array when
there is more than one dominant orientation at a given feature location.
@param features an array of image features
@param gauss_pyr Gaussian scale space pyramid
*/
static void calc_feature_oris( CvSeq* features, IplImage*** gauss_pyr )
{
struct feature* feat;
struct detection_data* ddata;
double* hist;//存放梯度直方图的数组
double omax;
int i, j, n = features->total;//特征点个数
//遍历特征点序列
for( i = 0; i < n; i++ )
{
//给每个特征点分配feature结构大小的内存
feat = malloc( sizeof( struct feature ) );
//移除列首元素，放到feat中
cvSeqPopFront( features, feat );
//调用宏feat_detection_data来提取参数feat中的feature_data成员并转换为detection_data类型的指针
//detection_data数据中存放有此特征点的行列坐标和尺度，以及所在的层和组
ddata = feat_detection_data( feat );
//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给hist
hist = ori_hist( gauss_pyr[ddata->octv][ddata->intvl], //特征点所在的图像
ddata->r, ddata->c, //特征点的行列坐标
SIFT_ORI_HIST_BINS, //默认的梯度直方图的bin(柱子)个数
cvRound( SIFT_ORI_RADIUS * ddata->scl_octv ),//特征点方向赋值过程中，搜索邻域的半径为：3 * 1.5 * σ
SIFT_ORI_SIG_FCTR * ddata->scl_octv ); //计算直翻图时梯度幅值的高斯权重的初始值
//对梯度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,SIFT_ORI_SMOOTH_PASSES指定了平滑次数
for( j = 0; j < SIFT_ORI_SMOOTH_PASSES; j++ )
smooth_ori_hist( hist, SIFT_ORI_HIST_BINS );
//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,返回给omax
omax = dominant_ori( hist, SIFT_ORI_HIST_BINS );
/*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
传入的特征点指针feat是已经从特征点序列features中移除的，所以即使此特征点没有辅方向(第二个大于幅值阈值的方向)
在函数add_good_ori_features中也会执行一次克隆feat，对其方向进行插值修正，并插入特征点序列的操作
幅值阈值一般设置为当前特征点的梯度直方图的最大bin值的80% */
add_good_ori_features( features, hist, SIFT_ORI_HIST_BINS,
omax * SIFT_ORI_PEAK_RATIO, feat );
//释放内存
free( ddata );
free( feat );
free( hist );
}
}