#include?“opencv2/opencv.hpp“
#include?“opencv2/nonfree/nonfree.hpp“//與SIFT相關(guān)
#include?“opencv2/legacy/legacy.hpp“//與匹配器相關(guān)
#include?
using?namespace?std;
using?namespace?cv;
const?int?N?=?1e6?+?5;
Mat?srcImg2;//比對的圖片
Mat?myframe[N];
int?cnt;


void?sift_ransac（Mat?img01?Mat?img02）
{

	//SIFT特征檢測
	SiftFeatureDetector?detector;????????//定義特點點檢測器
	vector?keypoint01?keypoint02;//定義兩個容器存放特征點
	detector.detect（img01?keypoint01）;
	detector.detect（img02?keypoint02）;

	//在兩幅圖中畫出檢測到的特征點
	Mat?out_img01;
	Mat?out_img02;
	drawKeypoints（img01?keypoint01?out_img01）;
	drawKeypoints（img02?keypoint02?out_img02）;

	//提取特征點的特征向量（128維）
	SiftDescriptorExtractor?extractor;
	Mat?descriptor01?descriptor02;
	extractor.compute（img01?keypoint01?descriptor01）;
	extractor.compute（img02?keypoint02?descriptor02）;

	//匹配特征點，主要計算兩個特征點特征向量的歐式距離，距離小于某個閾值則認(rèn)為匹配

	BruteForceMatcher>?matcher;
	vector?matches;
	Mat?img_matches;
	if?（descriptor02.cols?==?0?&&?descriptor02.rows?==?0）
		return;
	matcher.match（descriptor01?descriptor02?matches）;
	drawMatches（img01?keypoint01?img02?keypoint02?matches?img_matches）;
	//RANSAC?消除誤匹配特征點?主要分為三個部分：

	//根據(jù)matches將特征點對齊將坐標(biāo)轉(zhuǎn)換為float類型
	vector?R_keypoint01?R_keypoint02;
	for?（size_t?i?=?0;?i	{
		R_keypoint01.push_back（keypoint01[matches[i].queryIdx]）;
		R_keypoint02.push_back（keypoint02[matches[i].trainIdx]）;
	}

	//坐標(biāo)轉(zhuǎn)換
	vectorp01?p02;
	for?（size_t?i?=?0;?i	{
		p01.push_back（R_keypoint01[i].pt）;
		p02.push_back（R_keypoint02[i].pt）;
	}

	//利用基礎(chǔ)矩陣剔除誤匹配點
	vector?RansacStatus;

	Mat?Fundamental?=?findFundamentalMat（p01?p02?RansacStatus?FM_RANSAC）;


	vector?RR_keypoint01?RR_keypoint02;
	vector?RR_matches;????????????//重新定義RR_keypoint?和RR_matches來存儲新的關(guān)鍵點和匹配矩陣
	int?index?=?0;
	for?（size_t?i?=?0;?i	{
		if?（RansacStatus[i]?!=?0）
		{
			RR_keypoint01.push_back（R_keypoint01[i]）;
			RR_keypoint02.push_back（R_keypoint02[i]）;
			matches[i].queryIdx?=?index;
			matches[i].trainIdx?=?index;
			RR_matches.push_back（matches[i]）;
			index++;
		}
	}
	Mat?img_RR_matches;
	drawMatches（img01?RR_keypoint01?img02?RR_keypoint02?RR_matches?img_RR_matches）;
	std::vector?obj;
	std::vector?scene;

	for?（int?i?=?0;?i?	{
		obj.push_back（RR_keypoint01[RR_matches[i].queryIdx].pt）;
		scene.push_back（RR_keypoint02[RR_matches[i].trainIdx].pt）;
	}

	if?（RR_matches.size（）?==?0）
	{
		myframe[cnt++]?=?img_RR_matches;
		return;
	}
	Mat?H?=?findHomography（obj?scene?CV_RANSAC）;
	std::vector?obj_corners（4）;
	obj_corners[0]?=?cvPoint（0?0）;?obj_corners[1]?=?cvPoint（img01.cols?0）;
	obj_corners[2]?=?cvPoint（img01.cols?img01.rows）;?obj_corners[3]?=?cvPoint（0?img01.rows）;
	std::vector?scene_corners（4）;

	perspectiveTransform（obj_corners?scene_corners?H）;

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-12-24?14:02??1120151964-李欣欣\
?????文件????11143266??2017-12-23?17:32??1120151964-李欣欣\output.avi
?????文件???????10015??2017-12-23?17:17??1120151964-李欣欣\src.jpg
?????文件?????1311317??2017-12-23?17:27??1120151964-李欣欣\src.mp4
?????文件????????4478??2017-12-24?12:44??1120151964-李欣欣\源.cpp
?????文件?????1425310??2017-12-24?14:01??1120151964-李欣欣\程序報告.docx