【OpenCV】OpenCV3中的SURF特征点的寻找和匹配

不多说什么了，直接上代码吧：

#include <iostream>#include <stdio.h>#include "opencv2/core.hpp"#include "opencv2/core/utility.hpp"#include "opencv2/core/ocl.hpp"#include "opencv2/imgcodecs.hpp"#include "opencv2/highgui.hpp"#include "opencv2/features2d.hpp"#include "opencv2/calib3d.hpp"#include "opencv2/imgproc.hpp"#include"opencv2/flann.hpp"#include"opencv2/xfeatures2d.hpp"#include"opencv2/ml.hpp"using namespace cv;using namespace std;using namespace cv::xfeatures2d;using namespace cv::ml;int main(){    Mat a = imread("box.png", IMREAD_GRAYSCALE);    //读取灰度图像    Mat b = imread("box_in_scene.png", IMREAD_GRAYSCALE);    Ptr<SURF> surf;      //创建方式和2中的不一样    surf = SURF::create(800);    BFMatcher matcher;    Mat c, d;    vector<KeyPoint>key1, key2;    vector<DMatch> matches;    surf->detectAndCompute(a, Mat(), key1, c);    surf->detectAndCompute(b, Mat(), key2, d);    matcher.match(c, d, matches);       //匹配    sort(matches.begin(), matches.end());  //筛选匹配点    vector< DMatch > good_matches;                  int ptsPairs = std::min(50, (int)(matches.size() * 0.15));    cout << ptsPairs << endl;    for (int i = 0; i < ptsPairs; i++)    {        good_matches.push_back(matches[i]);    }    Mat outimg;    drawMatches(a, key1, b, key2, good_matches, outimg, Scalar::all(-1), Scalar::all(-1),vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);  //绘制匹配点    std::vector<Point2f> obj;    std::vector<Point2f> scene;    for (size_t i = 0; i < good_matches.size(); i++)    {        obj.push_back(key1[good_matches[i].queryIdx].pt);        scene.push_back(key2[good_matches[i].trainIdx].pt);    }    std::vector<Point2f> obj_corners(4);    obj_corners[0] = Point(0, 0);    obj_corners[1] = Point(a.cols, 0);    obj_corners[2] = Point(a.cols, a.rows);    obj_corners[3] = Point(0, a.rows);    std::vector<Point2f> scene_corners(4);    Mat H = findHomography(obj, scene, RANSAC);      //寻找匹配的图像    perspectiveTransform(obj_corners, scene_corners, H);    line(outimg,scene_corners[0] + Point2f((float)a.cols, 0), scene_corners[1] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);       //绘制    line(outimg,scene_corners[1] + Point2f((float)a.cols, 0), scene_corners[2] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);    line(outimg,scene_corners[2] + Point2f((float)a.cols, 0), scene_corners[3] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);    line(outimg,scene_corners[3] + Point2f((float)a.cols, 0), scene_corners[0] + Point2f((float)a.cols, 0),Scalar(0, 255, 0), 2, LINE_AA);    imshow("aaaa",outimg);    cvWaitKey(0);}

运行图： 

//-------------读取模板------------    cv::Mat img_object = imread("/storage/emulated/0/ApplePearFace/imgTemp.jpg");    //-------------图像处理---------    cv::Mat img_scene(yimage);    /*    // 检测surf特征点    int minHessian = 400;    OrbDescriptorExtractor detector(minHessian);    std::vector<KeyPoint> keypoints_1, keypoints_2;    detector.detect(img_1, keypoints_1);    detector.detect(img_2, keypoints_2);    //-- Step 2: Calculate descriptors (feature vectors)    OrbDescriptorExtractor extractor;    Mat descriptors_1, descriptors_2;    extractor.compute(img_1, keypoints_1, descriptors_1);    extractor.compute(img_2, keypoints_2, descriptors_2);    //-- Step 3: Matching descriptor vectors with a brute force matcher    BFMatcher matcher(NORM_L2);    std::vector< DMatch > matches;    matcher.match(descriptors_1, descriptors_2, matches);    //-- Draw matches    Mat img_matches;    drawMatches(img_1, keypoints_1, img_2, keypoints_2, matches, img_matches);*/    // 读取数据    //cv::Mat img_object = cv::imread("doll01.jpg");    //cv::Mat img_scene = cv::imread("doll012.jpg");    if (!img_object.data || !img_scene.data) {        cout << "Error reading images." << endl;        return 0;    }    // 构建特征检测器和描述子提取器    cv::Ptr<cv::FeatureDetector> detector = cv::FeatureDetector::create("ORB");    cv::Ptr<cv::DescriptorExtractor> descriptor = cv::DescriptorExtractor::create("ORB");    // 检测特征点    vector<cv::KeyPoint> kp_object, kp_scene;    detector->detect(img_object, kp_object);    detector->detect(img_scene, kp_scene);    // 计算描述子    cv::Mat desp_object, desp_scene;    descriptor->compute(img_object, kp_object, desp_object);    descriptor->compute(img_scene, kp_scene, desp_scene);    /*    if (desp_object.type() != CV_32F) {    desp_object.convertTo(desp_object, CV_32F);    }    if (desp_scene.type() != CV_32F) {    desp_scene.convertTo(desp_scene, CV_32F);    }    */    // 匹配描述子    vector<cv::DMatch> matches;    cv::FlannBasedMatcher matcher(new cv::flann::LshIndexParams(20, 10, 2));    matcher.match(desp_object, desp_scene, matches);    //cout << "Find total " << matches.size() << " matches." << endl;    // 筛选匹配    //double min_dist = 100000;    //for (int i = 0; i < matches.size(); i++) {    //float a = matches[i].distance;    //if (a < min_dist) {    //min_dist = matches[i].distance;    //}    //}    //vector<cv::DMatch> good_matches;    //for (int i = 0; i < matches.size(); i++) {    //    //if (matches[i].distance < 3 * min_dist) {    //good_matches.push_back(matches[i]);    //}    //}    // 显示匹配    //cout << "Good matches=" << matches.size() << endl;    cv::Mat img_matches;    cv::drawMatches(img_object, kp_object, img_scene, kp_scene, matches, img_matches);    // 定位目标    cv::vector<cv::Point2f> obj_points;    cv::vector<cv::Point2f> scene;    for (int i = 0; i < matches.size(); i++) {        obj_points.push_back(kp_object[matches[i].queryIdx].pt);        scene.push_back(kp_scene[matches[i].trainIdx].pt);    }    cv::Mat H = cv::findHomography(obj_points, scene, CV_RANSAC);    cv::vector<cv::Point2f> obj_corners(4);    cv::vector<cv::Point2f> scene_corners(4);    obj_corners[0] = cv::Point(0, 0);    obj_corners[1] = cv::Point(img_object.cols, 0);    obj_corners[2] = cv::Point(img_object.cols, img_object.rows);    obj_corners[3] = cv::Point(0, img_object.rows);    cv::perspectiveTransform(obj_corners, scene_corners, H);    cv::line(img_matches, scene_corners[0] + cv::Point2f(img_object.cols, 0), scene_corners[1] + cv::Point2f(img_object.cols, 0), cv::Scalar(0, 255, 0), 4);    cv::line(img_matches, scene_corners[1] + cv::Point2f(img_object.cols, 0), scene_corners[2] + cv::Point2f(img_object.cols, 0), cv::Scalar(0, 255, 0), 4);    cv::line(img_matches, scene_corners[2] + cv::Point2f(img_object.cols, 0), scene_corners[3] + cv::Point2f(img_object.cols, 0), cv::Scalar(0, 255, 0), 4);    cv::line(img_matches, scene_corners[3] + cv::Point2f(img_object.cols, 0), scene_corners[0] + cv::Point2f(img_object.cols, 0), cv::Scalar(0, 255, 0), 4);    cv::Mat dstSize;    cv::resize(img_matches, dstSize, Size(2 * h, w));