OpenCV实现拼接图像的简单方法

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:

//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);

以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持。

以上是 OpenCV实现拼接图像的简单方法 的全部内容, 来源链接: utcz.com/p/244451.html

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