图像拼接（六）：OpenCV单应变换模型拼接两幅图像

图像拼接首要步骤就是对齐。对齐就要找到两幅图像相对的位置关系。为了描述位置之间的变换关系，研究者引人了诸如平移，仿射，单应等变换模型。每个模型无所谓好坏，各有特定的适用范围。

在其次坐标系下，图像位置之间的关系，或者说同名点坐标之间关系，都可以用一个3×3的矩阵来表达。从平移到单应，这个变换矩阵的自由度逐步上升，灵活度增加，适用的场合变广，但也导致求解出来的变换矩阵不太准确和稳定，意思是容易拼飞。所以，能够用平移变换模型解决的问题，不见得使用单应变换矩阵更好。模型越紧，解越精确。

本篇博客使用单应变换模型，完成两幅图像的拼接。

单应矩阵的求解，按照“特征检测+特征描述+特征匹配+直接线性变换”的方法。

拼接对齐图像使用OpenCV里的warpPespective()函数。

代码实现：

Homography类

将H矩阵（单应矩阵）的求解封装进Homography类中。

//Homography.h 类声明文件#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 类实现文件#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类使用说明

创建类对象，需要指定两幅输入图像：

Homography h12(img1,img2);

可以设定特征检测器、描述器，匹配器的类型。默认情况下，使用SIFT检测和描述特征，使用BruteForce算法匹配特征。获取更多可用的类型，可以参见OpenCV通用的程序接口。使用示例：

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

可以检查各种中间量，比如检测出的角点，描述子，匹配，以及画出匹配。使用例子如下：

//获取两幅图像的特征点vector<KeyPoint> keyPoints1=h12.getKeyPoints1();vector<KeyPoint> keyPoints2=h12.getKeyPoints2();//获取描述子Mat descriptors1=h12.getDescriptors1();Mat descriptors2=h12.getDescriptors2();//获取匹配vector<DMatch> matches=h12.getMatches();//画出带有匹配连接线的图像h12.drawMatches();

可以直接获取计算出的单应矩阵：

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