cmake办法

http://blog.csdn.net/zhounanzhaode/article/details/50302385
待会再看

http://blog.csdn.net/cosmispower/article/details/60601151

图像配准源码：

#include<iostream>#include <opencv2/opencv.hpp>using namespace std;using namespace cv;void main(){    double start, duration_ms;    cv::Mat mask1, mask2, imgshowcanny;    cv::Mat imgShow1, imgShow2, imgShow3;    // 声明并从data文件夹里读取两个rgb与深度图    cv::Mat rgb1 = cv::imread("D:\\python_opencv\\source_image\\1123\\1\\1.jpg", 0);    cv::Mat rgb2 = cv::imread("D:\\python_opencv\\source_image\\1123\\1\\2.jpg", 0);    vector< cv::KeyPoint > kp1, kp2; //关键点    mask1 = rgb1.clone();    //边缘检测    //cout << "Canny edge detection" << endl;    //mask1 = Mat::zeros(rgb1.size(), CV_8UC1);    //mask2 = Mat::zeros(rgb2.size(), CV_8UC1);    cv::Canny(mask1, mask1, 50, 200, 3);//检测Canny边缘，并将结果存入CV::Mat mask1中                                        //cv::Canny(rgb2, mask2, 50, 150, 3);    cv::Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));    dilate(mask1, mask1, element);    cv::imwrite("F:/study work/Visual Odometry dev/VO_practice_2016.4/算法测试/KAZE算法/data/mask1.jpg", mask1);    imgshowcanny = mask1.clone();    cv::resize(imgshowcanny, imgshowcanny, cv::Size(imgshowcanny.cols, imgshowcanny.rows));    //特征检测算法：AKAZE    cv::Ptr<cv::AKAZE> akaze = AKAZE::create(AKAZE::DESCRIPTOR_MLDB, 0, 3, 0.001f, 4, 4, KAZE::DIFF_PM_G2);    cv::Mat descriptors_1, descriptors_2, descriptors_3, descriptors_4, descriptors_5, descriptors_6, descriptors_7, descriptors_8, descriptors_9, descriptors_10;    start = double(getTickCount());    akaze->detectAndCompute(rgb1, mask1, kp1, descriptors_1, false);    duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();//计时    std::cout << "It took " << duration_ms << " ms to detect features in pic1 using AKAZE." << std::endl;    start = double(getTickCount());    akaze->detectAndCompute(rgb2, cv::Mat(), kp2, descriptors_2, false);    duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();//计时    std::cout << "It took " << duration_ms << " ms to detect features in pic2 using AKAZE." << std::endl;    // 可视化， 显示特征点    cout << "Key points of two AKAZE images: " << kp1.size() << ", " << kp2.size() << endl;    cv::drawKeypoints(rgb1, kp1, imgShow1, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);    cv::drawKeypoints(rgb2, kp2, imgShow2, cv::Scalar::all(-1), cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);    cv::resize(imgShow2, imgShow2, cv::Size(imgShow2.cols, imgShow2.rows));    cv::resize(imgShow1, imgShow1, cv::Size(imgShow1.cols, imgShow1.rows));    cv::imshow("AKAZE_keypoints2", imgShow2);    cv::imshow("AKAZE_keypoints1", imgShow1);    //cv::waitKey(0); //暂停等待一个按键    //用Matcher匹配特征 :    BFMatcher bf_matcher;    FlannBasedMatcher flann_matcher;    cv::Ptr<cv::DescriptorMatcher>  knn_matcher = cv::DescriptorMatcher::create("BruteForce-Hamming");    std::vector< DMatch > flann_matches;    std::vector< DMatch > bf_matches1;    std::vector< DMatch > knn_match;    std::vector< std::vector<cv::DMatch> > knn_matches1;    start = double(getTickCount());    bf_matcher.match(descriptors_1, descriptors_2, bf_matches1);    //flann_matcher.match(descriptors_1, descriptors_2, flann_matches);    duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();//计时    std::cout << "It took " << duration_ms << " ms to do brutal-force-match on AKAZE features." << std::endl;    start = double(getTickCount());    knn_matcher->knnMatch(descriptors_1, descriptors_2, knn_matches1, 2);    duration_ms = (double(getTickCount()) - start) * 1000 / getTickFrequency();//计时    std::cout << "It took " << duration_ms << " ms to do knn-match on AKAZE features." << std::endl;    //用RANSAC求取单应矩阵的方式去掉不可靠的BF匹配    vector< DMatch > inliers1, inliers2;    cout << "Computing homography (RANSAC)" << endl;    vector<Point2f> points1(bf_matches1.size());    vector<Point2f> points2(bf_matches1.size());    for (size_t i = 0; i < bf_matches1.size(); i++)    {        points1[i] = kp1[bf_matches1[i].queryIdx].pt;//queryIdx 指代的是匹配上的点在待匹配图像的Id        points2[i] = kp2[bf_matches1[i].trainIdx].pt;//trainIdx 指代的是匹配上的点在匹配图像的Id    }    //计算单应矩阵并找出inliers      vector<uchar> flag1(points1.size(), 0);    Mat H1 = findHomography(points1, points2, CV_RANSAC, 3.0, flag1);    Mat H2 = findHomography(points2, points1, CV_RANSAC, 3.0, flag1);    /*for (int i = 0; i < H1.rows; i++) {    for (int j = 0; j < H1.cols; j++) {    cout << "H1_value" << H1[i][i] << endl;    }    }*/    std::cout << H2 << std::endl;    //cout << H << endl << endl;      for (int i = 0; i < bf_matches1.size(); i++)    {        if (flag1[i])        {            inliers1.push_back(bf_matches1[i]);        }    }    cout << "AKAZE BF matches inliers size = " << inliers1.size() << endl;    //去掉不可靠的HAMMING-KNN匹配    vector< cv::DMatch > goodMatches1;    for (size_t i = 0; i < knn_matches1.size(); i++)    {        if (knn_matches1[i][0].distance < 0.3  *            knn_matches1[i][1].distance)            goodMatches1.push_back(knn_matches1[i][0]);    }    cout << "AKAZE good knn matches size = " << goodMatches1.size() << endl;    vector<Point2f> points3(goodMatches1.size());    vector<Point2f> points4(goodMatches1.size());    for (size_t i = 0; i < goodMatches1.size(); i++)    {        points3[i] = kp1[goodMatches1[i].queryIdx].pt;        points4[i] = kp2[goodMatches1[i].trainIdx].pt;    }    Mat H3 = findHomography(points4, points3, CV_RANSAC, 3.0, flag1);    cout << "H3" << H3 << endl;    cv::Mat imdst_2, imsrc_1, imsrc_2, warp_dst, imresult;    imsrc_1 = cv::imread("D:\\python_opencv\\source_image\\1123\\1\\1.jpg");    imsrc_2 = cv::imread("D:\\python_opencv\\source_image\\1123\\1\\2.jpg");    //warpAffine(im_src, warp_dst, H2, im_src.size());    warpPerspective(imsrc_2, imdst_2, H3, imsrc_2.size());    cv::Mat imsrc_1_, imdst_2_, imresult_;    imsrc_1.convertTo(imsrc_1_, CV_32F, 1.0 / 255.0);    imdst_2.convertTo(imdst_2_, CV_32F, 1.0 / 255.0);    imresult = (imsrc_1_ + imdst_2_) / 2;    //cv::resize(imresult, imresult, cv::Size(imgshowcanny.cols / 16, imgshowcanny.rows / 16));    cv::imshow("result", imresult);    imresult.convertTo(imresult_, CV_16U, 255.0);    cv::imwrite("D:\\opencv_cpp\\opencv_cpp\\source_images\\after_transactions\\after_transaction.jpg", imresult_);    // 可视化：显示匹配的特征    cv::Mat AKAZE_BF_imgMatches;    cv::Mat AKAZE_KNN_imgMatches;    //// 筛选匹配，把距离太大的去掉    //// 这里使用的准则是去掉大于四倍最小距离的匹配    //vector< cv::DMatch > goodMatches;    //double minDis = 9999;    //for (size_t i = 0; i<matches.size(); i++)    //{    //  if (matches[i].distance < minDis)    //      minDis = matches[i].distance;    //}    //for (size_t i = 0; i<matches.size(); i++)    //{    //  if (matches[i].distance < 8 * minDis)    //      goodMatches.push_back(matches[i]);    //}    // 显示 good matches    cv::drawMatches(rgb1, kp1, rgb2, kp2, inliers1, AKAZE_BF_imgMatches);    cv::imwrite("F:/study work/Visual Odometry dev/VO_practice_2016.4/算法测试/KAZE算法/data/AKAZE-BF-Matches-inliers.jpg", AKAZE_BF_imgMatches);    cv::drawMatches(rgb1, kp1, rgb2, kp2, goodMatches1, AKAZE_KNN_imgMatches);    cv::imwrite("F:/study work/Visual Odometry dev/VO_practice_2016.4/算法测试/KAZE算法/data/AKAZE-Good-KNN-Matches.jpg", AKAZE_KNN_imgMatches);    cv::resize(AKAZE_BF_imgMatches, AKAZE_BF_imgMatches, Size(AKAZE_BF_imgMatches.cols / 2, AKAZE_BF_imgMatches.rows / 2));    cv::imshow("AKAZE-BF-Matches-inliers", AKAZE_BF_imgMatches);    cv::resize(AKAZE_KNN_imgMatches, AKAZE_KNN_imgMatches, Size(AKAZE_KNN_imgMatches.cols / 2, AKAZE_KNN_imgMatches.rows / 2));    cv::imshow("AKAZE-Good-KNN-Matches", AKAZE_KNN_imgMatches);    cv::waitKey(0);}