OpenCV中的SURF特征匹配

刚开始自学OpenCV不久，也是第一次写博客，所以请各位大神多多指点！

最近初涉OpenCV的仿射变换、SURF特征描述子、鼠标回调、进度条回调等功能，于是写了这个可以观察SURF的特征匹配效果的小程序，其实也是for fun，希望能搏君一玩^_^

PS：由于非CS科班出身，不足之处请各位多指教！

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先上程序效果截图：

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程序功能简介：
程序退出：在图像窗口敲击回车键即可；
旋转图像：鼠标拖动Video窗口的Angle轴即可；
缩放图像：鼠标拖动Video窗口的Scale轴即可；
平移图像：在Video窗口，鼠标左键拖拽图像即可；
平移图像复原：在Video窗口，单击鼠标右键即可；
调整特征匹配量：鼠标拖动Macher窗口的Hessian阈值即可。

程序实现环境：VS2010 + OpenCV 2.4.11

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#include <time.h>#include <stdio.h>#include <vector>#include <iostream>#include <opencv.hpp>#include <nonfree/nonfree.hpp>#include <legacy/legacy.hpp>using namespace cv;using namespace std;static Mat onAffine(Mat* im, Point2f dif);static Mat onRot(int pos, int rat, Mat* im);static Mat onSurf(int hes, Mat& im1, Mat& im2);static void onMouse(int Event, int x, int y, int drag, Mat* im);void main(int argc, char* argv[]){    Mat Cam = imread("Girl.jpg"), dst;    int pos = 36, rat = 10, hes = 40; //initial value    namedWindow("Video");   namedWindow("Matcher");    createTrackbar("Angle","Video", &pos,72); //rotate the image    createTrackbar("Scale","Video", &rat,20); //scale the image    createTrackbar("Hessian","Matcher", &hes,80); //minHessian threshold    setMouseCallback("Video", (MouseCallback)onMouse, &Cam); //drag image    while((waitKey(50)&255)!=13)    {//press the Enter key to exit the programe        dst = onRot(pos, rat, &Cam);    imshow("Video", dst);        dst = onSurf(hes, Cam, dst);    imshow("Matcher", dst);    }//end while}//end mainstatic Mat onSurf(int hes, Mat& im1, Mat& im2){    SURF Extractor, Detector(20*hes); //HessianThreshold    //SURF = SurfDescriptorExtractor & SurfFeatureDetector    vector<KeyPoint> kp1, kp2;  Mat descriptor1, descriptor2, dst;    Detector.detect(im1, kp1);  Detector.detect(im2, kp2);    //drawKeypoints(im1, kp1, dst); return dst; //no match    Extractor.compute(im1, kp1, descriptor1);    Extractor.compute(im2, kp2, descriptor2);    vector< DMatch > matches; //DMatch: distance match    BruteForceMatcher< L2<float> > Matcher; //L2-Distance    Matcher.match(descriptor1, descriptor2, matches);    drawMatches(im1, kp1, im2, kp2, matches, dst);  return dst;}//end onSurfstatic void onMouse(int Event, int x, int y, int drag, Mat* im){    static Mat res = im->clone();   static const Point ini(0,0);    static Point seed(ini), tdf(ini);   Point now(x,y);    switch(Event)    {    case CV_EVENT_LBUTTONDOWN:  seed = now; break;    case CV_EVENT_LBUTTONUP:    tdf += (now-seed);  break;    case CV_EVENT_RBUTTONUP:    res.copyTo(*im);    tdf = ini;  break;    }//end switch    if(drag==CV_EVENT_FLAG_LBUTTON) //drag the mouse to move the image        *im = onAffine(&res, tdf+(now-seed)); //use Affine transformation}//end onMousestatic Mat onAffine(Mat* im, Point2f dif){    Point2f org[3],tri[3];  org[1] = Point2f(im->cols/2, 0);    org[0] = Point2f(0,0);  org[2] = Point2f(0, im->rows/2);    for(int i=0; i<3; ++i)  tri[i] = org[i] + dif;    Mat warp = getAffineTransform(org, tri), dst;    warpAffine(*im, dst, warp, im->size()); return dst;}//end onAffinestatic Mat onRot(int pos, int rat, Mat* im){    Point center(im->cols/2, im->rows/2);    double angle = 5*pos-180, scale = 0.1*rat;    Mat rot = getRotationMatrix2D(center, angle, scale), dst;    warpAffine(*im, dst, rot, im->size());  return dst;}//end onRot

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调整旋转角度后的效果图：

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旋转+缩放后的效果图：

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鼠标拖动图像后的效果图：