opencv學習 第十章 估算影象之間的投影關係
10.2 計算影象對的基礎矩陣

#include"stdafx.h"
#include<iostream>  
#include<opencv2/core/core.hpp>    
#include<opencv2/highgui/highgui.hpp> 
#include<opencv2/features2d/features2d.hpp>
#include<opencv2/imgproc/imgproc.hpp>  
#include<opencv2/xfeatures2d.hpp>
#include<opencv2/opencv.hpp>

using namespace std;
using namespace cv;

int main() {

	Mat image1 =imread("故宮5.jpg");
	Mat image2 =imread("故宮7.jpg");
	resize(image1, image1, Size(360, 480));
	resize(image2, image2, Size(360, 480));
	if (!image1.data || !image2.data)
		return 0;

	//特徵點的向量
	vector<KeyPoint> keypoints1, keypoints2;
	//構造SURF特徵檢測器
	Ptr<xfeatures2d::SurfFeatureDetector> ptrSURF = xfeatures2d::SurfFeatureDetector::create(2000.0);//閾值
										//檢測SURF特徵
	ptrSURF->detect(image1, keypoints1);
	ptrSURF->detect(image2, keypoints2);

	//提取SURF描述子
	Mat descriptors1, descriptors2;
	ptrSURF->compute(image1, keypoints1, descriptors1);
	ptrSURF->compute(image2, keypoints2, descriptors2);

	//構造匹配器
	BFMatcher matcher(NORM_L2);
	//匹配兩幅影象的描述子
	vector<DMatch> matches;
	matcher.match(descriptors1, descriptors2, matches);

	nth_element(matches.begin(),	//初始位置
		matches.begin() +7,	//排序元素的位置
		matches.end());		//終止位置
							//移除第7位之後所有的元素
	matches.erase(matches.begin() + 7, matches.end());

	Mat imageMatches;
	drawMatches(
		image1, keypoints1,	//第一幅影象及其特徵點
		image2, keypoints2,	//第二幅影象及其特徵點
		matches,	//匹配結果
		imageMatches,	//生成的影象
		Scalar(255, 255, 255),//直線的顏色
		Scalar(0,255,0));	//點的顏色
	imshow("match", imageMatches);
								//轉換KeyPoint型別到Point2f
	vector<Point2f>selPoints1, selPoints2;
	vector<int>pointIndexes1, pointIndexes2;
	/*
	for (std::vector<cv::DMatch>::const_iterator it = matches.begin();
		it != matches.end(); ++it)
	{

		// Get the indexes of the selected matched keypoints
		pointIndexes1.push_back(it->queryIdx);
		pointIndexes2.push_back(it->trainIdx);
	}
	*/ 
	KeyPoint::convert(keypoints1, selPoints1, pointIndexes1);
	KeyPoint::convert(keypoints2, selPoints2, pointIndexes2);
	//從7個矩陣中計算F矩陣
	Mat fundemental =findFundamentalMat(
		Mat(selPoints1),	//圖1中的點
		Mat(selPoints2),	//圖2中的點
		CV_FM_7POINT);	//使用7個點的方法

						//計算左圖中點的極線 繪製在右圖中 在右圖中繪製對應的極線
	vector<Vec3f> lines1;
	computeCorrespondEpilines(
		Mat(selPoints1),	//影象點
		1,	//圖1（也可以是2）
		fundemental,	//F矩陣
		lines1);	//一組極線

					//對於所有極線
	for (vector<Vec3f>::const_iterator it = lines1.begin(); it != lines1.end(); ++it)
	{
		//繪製第一列與最後一列之間的直線
		line(image2,
			Point(0, -(*it)[2] / (*it)[1]),
			Point(image2.cols, -((*it)[2] + (*it)[0] * image2.cols) / (*it)[1]), Scalar(255, 255, 255));
	}

	namedWindow("Left Image Epilines");
	//imshow("right", image1);
	imshow("Left Image Epilines", image2);

	waitKey(0);
	return 0;
}

 

執行結果：