圖像拼接（六）：OpenCV單應變換模型拼接兩幅圖像

圖像拼接首要步驟就是對齊。對齊就要找到兩幅圖像相對的位置關係。爲了描述位置之間的變換關係，研究者引人了諸如平移，仿射，單應等變換模型。每個模型無所謂好壞，各有特定的適用範圍。

在其次座標系下，圖像位置之間的關係，或者說同名點座標之間關係，都可以用一個3×3的矩陣來表達。從平移到單應，這個變換矩陣的自由度逐步上升，靈活度增加，適用的場合變廣，但也導致求解出來的變換矩陣不太準確和穩定，意思是容易拼飛。所以，能夠用平移變換模型解決的問題，不見得使用單應變換矩陣更好。模型越緊，解越精確。

本篇博客使用單應變換模型，完成兩幅圖像的拼接。

單應矩陣的求解，按照“特徵檢測+特徵描述+特徵匹配+直接線性變換”的方法。

拼接對齊圖像使用OpenCV裏的warpPespective()函數。

代碼實現：
#Homography類
將H矩陣（單應矩陣）的求解封裝進Homography類中。

//Homography.h 類聲明文件

#pragma once
# include "opencv2/core/core.hpp"
# include "opencv2/features2d/features2d.hpp"
# include "opencv2/highgui/highgui.hpp"
# include "opencv2/imgproc/imgproc.hpp"
#include"opencv2/nonfree/nonfree.hpp"
#include"opencv2/calib3d/calib3d.hpp"
#include<iostream>
using namespace cv;
using namespace std;

class Homography
{
private:
	Mat img1;
	Mat img2;

	Ptr<FeatureDetector> detector;
	Ptr<DescriptorExtractor> extractor;
	Ptr<DescriptorMatcher> matcher;

	vector<KeyPoint> keyPoints1;
	vector<KeyPoint> keyPoints2;

	Mat descriptors1;
	Mat descriptors2;

	vector<DMatch> firstMatches;
	vector<DMatch> matches;

	vector<Point2f> selfPoints1;
	vector<Point2f> selfPoints2;

	vector<uchar> inliers;

	Mat homography;

public:
	Homography();
	Homography(Mat img1, Mat img2) ;

	void setFeatureDetector(string detectorName);
	void setDescriptorExtractor(string descriptorName);
	void setDescriptorMatcher(string matcherName);

	vector<KeyPoint> getKeyPoints1();
	vector<KeyPoint> getKeyPoints2();

	Mat getDescriptors1();
	Mat getDescriptors2();

	vector<DMatch> getMatches();
	void drawMatches();

	Mat getHomography();

	~Homography();

private:
	void detectKeyPoints();
	void computeDescriptors();
	void match();
	void matchesToSelfPoints();
	void findHomography();
	void matchesFilter();
};

//Homography.cpp 類實現文件

#include "Homography.h"

Homography::Homography()
{
	detector = new SIFT(800);
	extractor = detector;
	matcher = DescriptorMatcher::create("BruteForce");
}

Homography::Homography(Mat img1, Mat img2)
{
	new(this) Homography();
	this->img1 = img1;
	this->img2 = img2;
}

void Homography::setFeatureDetector(string detectorName)
{
	detector = FeatureDetector::create(detectorName);
}

void Homography::setDescriptorExtractor(string descriptorName)
{
	extractor = DescriptorExtractor::create(descriptorName);
}

void Homography::setDescriptorMatcher(string matcherName)
{
	matcher = DescriptorMatcher::create(matcherName);
}

vector<KeyPoint> Homography::getKeyPoints1()
{
	if (keyPoints1.size() == 0)
	{
		detectKeyPoints();
	}

	return keyPoints1;
}

vector<KeyPoint> Homography::getKeyPoints2()
{
	if (keyPoints2.size()==0)
	{
		detectKeyPoints();
	}

	return keyPoints2;
}

Mat Homography::getDescriptors1()
{
	if (descriptors1.data == NULL)
	{
		computeDescriptors();
	}
	
	return descriptors1;
}

Mat Homography::getDescriptors2()
{
	if (descriptors2.data == NULL)
	{
		computeDescriptors();
	}

	return descriptors2;
}

vector<DMatch> Homography::getMatches()
{
	if (matches.size() == 0)
	{
		matchesFilter();
	}
		
	return matches;
}

Mat Homography::getHomography()
{
	if (homography.data == NULL)
	{
		findHomography();
	}
	return homography;
}

void Homography::drawMatches()
{
	Mat matchImage;
	if (matches.size() == 0)
	{
		matchesFilter();
	}
	cv::drawMatches(img1, keyPoints1, img2, keyPoints2, matches, matchImage, 255, 255);
	imshow("drawMatches", matchImage);
}

void Homography::detectKeyPoints()
{
	detector->detect(img1, keyPoints1, Mat());
	detector->detect(img2, keyPoints2, Mat());
}

void Homography::computeDescriptors()
{
	if (keyPoints1.size() == 0 || keyPoints2.size() == 0)
	{
		detectKeyPoints();
	}
	extractor->compute(img1,keyPoints1,descriptors1);
	extractor->compute(img2, keyPoints2, descriptors2);
}

void Homography::match()
{
	if (descriptors1.data == NULL || descriptors2.data == NULL)
	{
		computeDescriptors();
	}
	matcher->match(descriptors1, descriptors2, firstMatches, Mat());
}


void Homography::matchesToSelfPoints()
{
	for (vector<DMatch>::const_iterator it = firstMatches.begin(); it != firstMatches.end(); ++it)
	{
		selfPoints1.push_back(keyPoints1.at(it->queryIdx).pt);
		selfPoints2.push_back(keyPoints2.at(it->trainIdx).pt);
	}
}

void Homography::findHomography()
{
	if (firstMatches.size()==0)
	{
		match();
	}
	if (selfPoints1.size()==0||selfPoints2.size()==0)
	{
		matchesToSelfPoints();
	}
	inliers=vector<uchar>(selfPoints1.size(),0);
	homography = cv::findHomography(selfPoints1, selfPoints2, inliers, CV_FM_RANSAC, 1.0);

}

void Homography::matchesFilter()
{
	if (0 == firstMatches.size())
	{
		findHomography();
	}

	vector<DMatch>::const_iterator itM = firstMatches.begin();
	vector<uchar>::const_iterator itIn = inliers.begin();

	for (; itIn != inliers.end(); ++itIn, ++itM)
	{
		if (*itIn)
		{
			matches.push_back(*itM);
		}
	}

}

Homography::~Homography()
{
}

#Homography類使用說明

創建類對象，需要指定兩幅輸入圖像：

Homography h12(img1,img2);

可以設定特徵檢測器、描述器，匹配器的類型。默認情況下，使用SIFT檢測和描述特徵，使用BruteForce算法匹配特徵。獲取更多可用的類型，可以參見OpenCV通用的程序接口。使用示例：

h12.setFeatureDetector("FAST");
h12.setDescriptorExtractor("SIFT");
h12.setDescriptorMatcher("BruteForce");

可以檢查各種中間量，比如檢測出的角點，描述子，匹配，以及畫出匹配。使用例子如下：

//獲取兩幅圖像的特徵點
vector<KeyPoint> keyPoints1=h12.getKeyPoints1();
vector<KeyPoint> keyPoints2=h12.getKeyPoints2();

//獲取描述子
Mat descriptors1=h12.getDescriptors1();
Mat descriptors2=h12.getDescriptors2();

//獲取匹配
vector<DMatch> matches=h12.getMatches();

//畫出帶有匹配連接線的圖像
h12.drawMatches();

可以直接獲取計算出的單應矩陣：

Mat h=h12.getHomography();

#使用warpPerspective()拼接

#include"Homography.h"

int main()
{
	string imgPath1 = "trees_000.jpg";
	string imgPath2 = "trees_001.jpg";
	Mat img1 = imread(imgPath1, CV_LOAD_IMAGE_GRAYSCALE);
	Mat img2 = imread(imgPath2, CV_LOAD_IMAGE_GRAYSCALE);

	Homography homo12(img1,img2);
	Mat h12 = homo12.getHomography();

	Mat h21;
	invert(h12, h21, DECOMP_LU);

	Mat canvas;
	Mat img1_color = imread(imgPath1, CV_LOAD_IMAGE_COLOR);
	Mat img2_color = imread(imgPath2, CV_LOAD_IMAGE_COLOR);	
	
	warpPerspective(img2_color, canvas, h21, Size(img1.cols*2, img1.rows));
	img1_color.copyTo(canvas(Range::all(), Range(0, img1.cols)));

	imshow("canvas",canvas);

	waitKey(0);
	return 0;
}

trees_000.jpg

trees_001.jpg

canvas.jpg