OpenCV实现拼接图像的简单方法

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我是靠谱客的博主壮观仙人掌，这篇文章主要介绍OpenCV实现拼接图像的简单方法，现在分享给大家，希望可以做个参考。

本文实例为大家分享了OpenCV实现拼接图像的具体方法，供大家参考，具体内容如下

用iphone拍摄的两幅图像：

拼接后的图像：

相关代码如下：

复制代码

//读取图像
Mat leftImg=imread("left.jpg");
Mat rightImg=imread("right.jpg");
if(leftImg.data==NULL||rightImg.data==NULL)
 return;
 
//转化成灰度图
Mat leftGray;
Mat rightGray;
cvtColor(leftImg,leftGray,CV_BGR2GRAY);
cvtColor(rightImg,rightGray,CV_BGR2GRAY);
 
//获取两幅图像的共同特征点
int minHessian=400;
SurfFeatureDetector detector(minHessian);
vector<KeyPoint> leftKeyPoints,rightKeyPoints;
detector.detect(leftGray,leftKeyPoints);
detector.detect(rightGray,rightKeyPoints);
SurfDescriptorExtractor extractor;
Mat leftDescriptor,rightDescriptor;
extractor.compute(leftGray,leftKeyPoints,leftDescriptor);
extractor.compute(rightGray,rightKeyPoints,rightDescriptor);
FlannBasedMatcher matcher;
vector<DMatch> matches;
matcher.match(leftDescriptor,rightDescriptor,matches); 
int matchCount=leftDescriptor.rows;
if(matchCount>15)
{
 matchCount=15;
 sort(matches.begin(),matches.begin()+leftDescriptor.rows,DistanceLessThan);
} 
vector<Point2f> leftPoints;
vector<Point2f> rightPoints;
for(int i=0; i<matchCount; i++)
{
 leftPoints.push_back(leftKeyPoints[matches[i].queryIdx].pt);
 rightPoints.push_back(rightKeyPoints[matches[i].trainIdx].pt);
}
 
//获取左边图像到右边图像的投影映射关系
Mat homo=findHomography(leftPoints,rightPoints);
Mat shftMat=(Mat_<double>(3,3)<<1.0,0,leftImg.cols, 0,1.0,0, 0,0,1.0);
 
//拼接图像
Mat tiledImg;
warpPerspective(leftImg,tiledImg,shftMat*homo,Size(leftImg.cols+rightImg.cols,rightImg.rows));
rightImg.copyTo(Mat(tiledImg,Rect(leftImg.cols,0,rightImg.cols,rightImg.rows)));
 
//保存图像
imwrite("tiled.jpg",tiledImg);
 
//显示拼接的图像
imshow("tiled image",tiledImg);
waitKey(0);

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//读取图像
Mat leftImg=imread("left.jpg");
Mat rightImg=imread("right.jpg");
if(leftImg.data==NULL||rightImg.data==NULL)
 return;
 
//转化成灰度图
Mat leftGray;
Mat rightGray;
cvtColor(leftImg,leftGray,CV_BGR2GRAY);
cvtColor(rightImg,rightGray,CV_BGR2GRAY);
 
//获取两幅图像的共同特征点
int minHessian=400;
SurfFeatureDetector detector(minHessian);
vector<KeyPoint> leftKeyPoints,rightKeyPoints;
detector.detect(leftGray,leftKeyPoints);
detector.detect(rightGray,rightKeyPoints);
SurfDescriptorExtractor extractor;
Mat leftDescriptor,rightDescriptor;
extractor.compute(leftGray,leftKeyPoints,leftDescriptor);
extractor.compute(rightGray,rightKeyPoints,rightDescriptor);
FlannBasedMatcher matcher;
vector<DMatch> matches;
matcher.match(leftDescriptor,rightDescriptor,matches); 
int matchCount=leftDescriptor.rows;
if(matchCount>15)
{
 matchCount=15;
 sort(matches.begin(),matches.begin()+leftDescriptor.rows,DistanceLessThan);
} 
vector<Point2f> leftPoints;
vector<Point2f> rightPoints;
for(int i=0; i<matchCount; i++)
{
 leftPoints.push_back(leftKeyPoints[matches[i].queryIdx].pt);
 rightPoints.push_back(rightKeyPoints[matches[i].trainIdx].pt);
}
 
//获取左边图像到右边图像的投影映射关系
Mat homo=findHomography(leftPoints,rightPoints);
Mat shftMat=(Mat_<double>(3,3)<<1.0,0,leftImg.cols, 0,1.0,0, 0,0,1.0);
 
//拼接图像
Mat tiledImg;
warpPerspective(leftImg,tiledImg,shftMat*homo,Size(leftImg.cols+rightImg.cols,rightImg.rows));
rightImg.copyTo(Mat(tiledImg,Rect(leftImg.cols,0,rightImg.cols,rightImg.rows)));
 
//保存图像
imwrite("tiled.jpg",tiledImg);
 
//显示拼接的图像
imshow("tiled image",tiledImg);
waitKey(0);