openCV SURF 算法对两幅图像匹配并用加权平均法融合拼接

/*------------------------------------------------------------------------------------------*\ This file contains material supporting chapter 9 of the cookbook: Computer Vision Programming using the OpenCV Library. by Robert Laganiere, Packt Publishing, 2011. This program is free software; permission is hereby granted to use, copy, modify, and distribute this source code, or portions thereof, for any purpose, without fee, subject to the restriction that the copyright notice may not be removed or altered from any source or altered source distribution. The software is released on an as-is basis and without any warranties of any kind. In particular, the software is not guaranteed to be fault-tolerant or free from failure. The author disclaims all warranties with regard to this software, any use, and any consequent failure, is purely the responsibility of the user. Copyright (C) 2010-2011 Robert Laganiere, www.laganiere.name \*------------------------------------------------------------------------------------------*/ #include <iostream> #include <vector> #include <opencv2/core/core.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/features2d/features2d.hpp> #include <opencv2/nonfree/features2d.hpp> #include <opencv2/calib3d/calib3d.hpp> #include <opencv2/legacy/legacy.hpp> #include "matcher.h" extern cv::Mat fusion(cv::Mat imleft ,cv::Mat imright); int main() { // Read input images cv::Mat image1= cv::imread("../a2.tif"); cv::Mat image2= cv::imread("../a3.tif"); if (!image1.data || !image2.data) { std::cout<<"Image Load Error"<<std::endl; return 0; } // Display the images //cv::namedWindow("Image 1"); //cv::imshow("Image 1",image1); //cv::namedWindow("Image 2"); //cv::imshow("Image 2",image2); // Prepare the matcher RobustMatcher rmatcher; rmatcher.setConfidenceLevel(0.98); rmatcher.setMinDistanceToEpipolar(1.0); rmatcher.setRatio(0.65f); cv::Ptr<cv::FeatureDetector> det= new cv::SurfFeatureDetector(10); cv::Ptr<cv::DescriptorExtractor> desc = new cv::SurfDescriptorExtractor(); rmatcher.setFeatureDetector(det); rmatcher.setDescriptorExtractor(desc); // Match the two images std::vector<cv::DMatch> matches; std::vector<cv::KeyPoint> keypoints1, keypoints2; cv::Mat fundemental= rmatcher.match(image1,image2,matches, keypoints1, keypoints2); /* // draw the matches cv::Mat imageMatches; cv::drawMatches(image1,keypoints1, // 1st image and its keypoints image2,keypoints2, // 2nd image and its keypoints matches, // the matches imageMatches, // the image produced cv::Scalar(255,255,255)); // color of the lines cv::namedWindow("Matches"); cv::imshow("Matches",imageMatches); */ // Convert keypoints into Point2f std::vector<cv::Point2f> points1, points2; for (std::vector<cv::DMatch>::const_iterator it= matches.begin(); it!= matches.end(); ++it) { // Get the position of left keypoints float x= keypoints1[it->queryIdx].pt.x; float y= keypoints1[it->queryIdx].pt.y; points1.push_back(cv::Point2f(x,y)); // Get the position of right keypoints x= keypoints2[it->trainIdx].pt.x; y= keypoints2[it->trainIdx].pt.y; points2.push_back(cv::Point2f(x,y)); } //std::cout << points1.size() << " " << points2.size() << std::endl; // Find the homography between image 1 and image 2 std::vector<uchar> inliers(points1.size(),0); cv::Mat homography= cv::findHomography( cv::Mat(points1),cv::Mat(points2), // corresponding points inliers, // outputed inliers matches CV_RANSAC, // RANSAC method 1.); // max distance to reprojection point /* // Draw the inlier points std::vector<cv::Point2f>::const_iterator itPts= points1.begin(); std::vector<uchar>::const_iterator itIn= inliers.begin(); while (itPts!=points1.end()) { // draw a circle at each inlier location if (*itIn) cv::circle(image1,*itPts,3,cv::Scalar(255,255,255),2); ++itPts; ++itIn; } itPts= points2.begin(); itIn= inliers.begin(); while (itPts!=points2.end()) { // draw a circle at each inlier location if (*itIn) cv::circle(image2,*itPts,3,cv::Scalar(255,255,255),2); ++itPts; ++itIn; } // Display the images with points cv::namedWindow("Image 1 Homography Points"); cv::imshow("Image 1 Homography Points",image1); cv::namedWindow("Image 2 Homography Points"); cv::imshow("Image 2 Homography Points",image2); */ // Warp image 1 to image 2 cv::Mat result; cv::warpPerspective(image1, // input image result, // output image homography, // homography cv::Size(2*image1.cols,image1.rows)); // size of output image cv::Mat dst = fusion(image2,result); cv::namedWindow("After Masoic"); cv::imshow("After Masoic",dst); /* // Copy image 1 on the first half of full image 规定result图中的感兴趣区域 cv::Mat half(result,cv::Rect(0,0,image2.cols,image2.rows)); image2.copyTo(half); // Display the warp image cv::namedWindow("After warping"); cv::imshow("After warping",result); */ cv::waitKey(); return 0; }