OpenCV實現(xiàn)拼接圖像的簡單方法
更新時間:2019年05月20日 16:58:00 作者:iteye_18380
這篇文章主要為大家詳細(xì)介紹了OpenCV實現(xiàn)拼接圖像的簡單方法,具有一定的參考價值,感興趣的小伙伴們可以參考一下
本文實例為大家分享了OpenCV實現(xiàn)拼接圖像的具體方法,供大家參考,具體內(nèi)容如下
用iphone拍攝的兩幅圖像:
拼接后的圖像:
相關(guān)代碼如下:
//讀取圖像 Mat leftImg=imread("left.jpg"); Mat rightImg=imread("right.jpg"); if(leftImg.data==NULL||rightImg.data==NULL) return; //轉(zhuǎn)化成灰度圖 Mat leftGray; Mat rightGray; cvtColor(leftImg,leftGray,CV_BGR2GRAY); cvtColor(rightImg,rightGray,CV_BGR2GRAY); //獲取兩幅圖像的共同特征點 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); } //獲取左邊圖像到右邊圖像的投影映射關(guān)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); //拼接圖像 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))); //保存圖像 imwrite("tiled.jpg",tiledImg); //顯示拼接的圖像 imshow("tiled image",tiledImg); waitKey(0);
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