/*
Functions?for?detecting?SIFT?image?features.

For?more?information?refer?to:

Lowe?D.??Distinctive?image?features?from?scale-invariant?keypoints.
International?Journal?of?Computer?Vision?60?2?（2004）
pp.91--110.

Copyright?（C）?2006-2010??Rob?Hess?

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特征點(diǎn)檢測的實(shí)現(xiàn)
*/

#include?“sift.h“
#include?“imgfeatures.h“
#include?“utils.h“

#include?
#include?

/************************?未暴露接口的一些本地函數(shù)的聲明?**************************/
/*************************?Local?Function?Prototypes?*************************/

//將原圖轉(zhuǎn)換為32位灰度圖并歸一化，然后進(jìn)行一次高斯平滑，并根據(jù)參數(shù)img_dbl決定是否將圖像尺寸放大為原圖的2倍
static?IplImage*?create_init_img（?IplImage*?int?double?）;
//將輸入圖像轉(zhuǎn)換為32位灰度圖并進(jìn)行歸一化
static?IplImage*?convert_to_gray32（?IplImage*?）;
//根據(jù)輸入?yún)?shù)建立高斯金字塔
static?IplImage***?build_gauss_pyr（?IplImage*?int?int?double?）;
//對(duì)輸入圖像做下采樣生成其四分之一大小的圖像（每個(gè)維度上減半），使用最近鄰差值方法
static?IplImage*?downsample（?IplImage*?）;
//通過對(duì)高斯金字塔中每相鄰兩層圖像相減來建立高斯差分金字塔
static?IplImage***?build_dog_pyr（?IplImage***?int?int?）;
//在尺度空間中檢測極值點(diǎn)，通過插值精確定位，去除低對(duì)比度的點(diǎn)，去除邊緣點(diǎn)，返回檢測到的特征點(diǎn)序列
static?CvSeq*?scale_space_extrema（?IplImage***?int?int?double?int?CvMemStorage*）;
//通過在尺度空間中將一個(gè)像素點(diǎn)的值與其周圍3*3*3鄰域內(nèi)的點(diǎn)比較來決定此點(diǎn)是否極值點(diǎn)（極大值或極小都行）
static?int?is_extremum（?IplImage***?int?int?int?int?）;
//通過亞像素級(jí)插值進(jìn)行極值點(diǎn)精確定位（修正極值點(diǎn)坐標(biāo)），并去除低對(duì)比度的極值點(diǎn)，將修正后的特征點(diǎn)組成feature結(jié)構(gòu)返回
static?struct?feature*?interp_extremum（?IplImage***?int?int?int?int?int?double）;
//進(jìn)行一次極值點(diǎn)差值，計(jì)算x，y，σ方向（層方向）上的子像素偏移量（增量）
static?void?interp_step（?IplImage***?int?int?int?int?double*?double*?double*?）;
//在DoG金字塔中計(jì)算某點(diǎn)的x方向、y方向以及尺度方向上的偏導(dǎo)數(shù)
static?CvMat*?deriv_3D（?IplImage***?int?int?int?int?）;
//在DoG金字塔中計(jì)算某點(diǎn)的3*3海森矩陣
static?CvMat*?hessian_3D（?IplImage***?int?int?int?int?）;
//計(jì)算被插值點(diǎn)的對(duì)比度：D?+?0.5?*?dD^T?*?X
static?double?interp_contr（?IplImage***?int?int?int?int?double?double?double?）;
//為一個(gè)feature結(jié)構(gòu)分配空間并初始化
static?struct?feature*?new_feature（?void?）;
//去除邊緣響應(yīng)，即通過計(jì)算主曲率比值判斷某點(diǎn)是否邊緣點(diǎn)
static?int?is_too_edge_like（?IplImage*?int?int?int?）;
//計(jì)算特征點(diǎn)序列中每個(gè)特征點(diǎn)的尺度
static?void?calc_feature_scales（?CvSeq*?double?int?）;
//將特征點(diǎn)序列中每個(gè)特征點(diǎn)的坐標(biāo)減半（當(dāng)設(shè)置了將圖像放大為原圖的2倍時(shí)，特征點(diǎn)檢測完之后調(diào)用）
static?void?adjust_for_img_dbl（?CvSeq*?）;
//計(jì)算每個(gè)特征點(diǎn)的梯度直方圖，找出其主方向，若一個(gè)特征點(diǎn)有不止一個(gè)主方向，將其分為兩個(gè)特征點(diǎn)
static?void?calc_feature_oris（?CvSeq*?IplImage***?）;
//計(jì)算指定像素點(diǎn)的梯度方向直方圖，返回存放直方圖的數(shù)組
static?double*?ori_hist（?IplImage*?int?int?int?int?double?）;
//計(jì)算指定點(diǎn)的梯度的幅值magnitude和方向orientation
static?int?calc_grad_mag_ori（?IplImage*?int?int?double*?double*?）;
//對(duì)梯度方向直方圖進(jìn)行高斯平滑，彌補(bǔ)因沒有仿射不變性而產(chǎn)生的特征點(diǎn)不穩(wěn)定的問題
static?void?smooth_ori_hist（?double*?int?）;
//查找梯度直方圖中主方向的梯度幅值，即查找直方圖中最大bin的值
static?double?dominant_ori（?double*?int?）;
//若當(dāng)前特征點(diǎn)的