#include?“stdafx.h“
#include?“cvsurf.h“
//#include?
#include?
using?namespace?std;

#define?ScanOctave?（3）
#define?FilterScale?（4）
#define?SamplingStep?（1）

CV_INLINE?CvSURFPoint?cvSURFPoint（?int?x?int?y?int?laplacian?int?size?int?octave?int?scale?）
{
????CvSURFPoint?p;
????p.x?=?x;
????p.y?=?y;
????p.laplacian?=?laplacian;
????p.size?=?size;
????p.octave?=?octave;
????p.scale?=?scale;
????return?p;
}
//計(jì)算箱式濾波器在x、y、xy方向上的對(duì)高斯二階偏導(dǎo)的近似Dxx，Dyy，Dxy，參數(shù)依次為原始圖像的積分圖像、箱式濾波器、積分圖像的列數(shù)
CV_INLINE?double?icvCalHaarPattern（?int*?originint*?tint?widthStep?）
{
	double?d?=?0;
	int?*p0?=?0?*p1?=?0?*p2?=?0?*p3?=?0;
	int?n?=?t[0];
	for?（?int?k?=?0;?k?	{
		p0?=?origin+t[1]+t[2]*widthStep;
		p1?=?origin+t[1]+t[4]*widthStep;
		p2?=?origin+t[3]+t[2]*widthStep;
		p3?=?origin+t[3]+t[4]*widthStep;
		d?+=?（double）（（*p3-*p2-*p1+*p0）*t[6]）/（double）（t[5]）;
		t+=6;
	}
	return?d;
}
//計(jì)算新的箱式濾波器來近似高斯核，參數(shù)依次為濾波器初始各矩形框坐標(biāo)、濾波器新的各矩形框坐標(biāo)、舊濾波器大小、新濾波器的大小
CV_INLINE?void?icvResizeHaarPattern（?int*?t_sint*?t_dint?OldSizeint?NewSize?）
{
	int?n?=?t_d[0]?=?t_s[0];
	for?（?int?k?=?0;?k?	{
		t_d[1]?=?t_s[1]*NewSize/OldSize;
		t_d[2]?=?t_s[2]*NewSize/OldSize;
		t_d[3]?=?t_s[3]*NewSize/OldSize;
		t_d[4]?=?t_s[4]*NewSize/OldSize;
		t_d[5]?=?（t_d[3]-t_d[1]+1）*（t_d[4]-t_d[2]+1）;
		t_d[6]?=?t_s[6];
		t_d+=6;
		t_s+=6;
	}
}

template
CV_INLINE?int
icvSign（?Number?x?）
{
	return?（（?x?}

///////////////////源文件輸出文檔txt與Doc文檔里的輸出txt區(qū)別開來了//////////////////////
	?string?filename（“G:\\surf響應(yīng)信息.txt“）;
	?ofstream?ofs（filename.c_str（））;
///////////////////////////////////////////////////////////////////////////////////////
	
	?//計(jì)算關(guān)鍵點(diǎn)坐標(biāo)，輸入原始圖像的積分圖像，輸出關(guān)鍵點(diǎn)的坐標(biāo)，參數(shù)依次為原始圖像的積分圖像，內(nèi)存空間，進(jìn)行極大值抑制的參數(shù)
CvSeq*?icvFastHessianDetector（?const?CvMat*?sumCvMemStorage*?storagedouble?quality?）
{
	double?t?=?（double）cvGetTickCount（）;
	CvSeq*?points?=?cvCreateSeq（?0?sizeof（CvSeq）?sizeof（CvSURFPoint）?storage?）;
	CvMat*?hessians[ScanOctave*（FilterScale+2）];
	CvMat*?traces[ScanOctave*（FilterScale+2）];
	int?size?size_cache[ScanOctave*（FilterScale+2）];
	int?scale?scale_cache[ScanOctave*（FilterScale+2）];
	double?*hessian_ptr?*hessian_ptr_cache[ScanOctave*（FilterScale+2）];
	double?*trace_ptr?*trace_ptr_cache[ScanOctave*（FilterScale+2）];
	int?dx_s[]?=?{?3?0?2?3?7?0?1?3?2?6?7?0?-2?6?2?9?7?0?1?};
	int?dy_s[]?=?{?3?2?0?7?3?0?1?2?3?7?6?0?-2?2?6?7?9?0?1?};
	int?dxy_s[]?=?{?4?1?1?4?4?0?1?5?1?8?4?0?-1?1?5?4?8?0?-1?5?5?8?8?0?1?};
	int?dx_t[]?=?{?3?0?2?3?7?0?1?3?2?6?7?0?-2?6?2?9?7?0?1?};
	int?dy_t[]?=?{?3?2?0?7?3?0?1?2?3?7?6?0?-2?2?6?7?9?0?1?};
	int?dxy_t[]?=?{?4?1?1?4?4?0?1?5?1?8?4?0?-1?1?5?4?8?0?-1?5?5?8?8?0?1?};
	double?dx?=?0?dy?=?0?dxy?=?0;
	int?k?=?0;
	int?hessian_rows?hessian_rows_cache[ScanOctave*（FilterScale+2）];
	int?hessian_cols?hessian_cols_cache[ScanOctave*（FilterScale+2）];
	/*?hessian?detector?*/
	for?（?int?o?=?0;?o?