opencv3中SURF特征提取

#include<opencv2/opencv.hpp>#include<iostream>#include<vector>using namespace cv;using namespace std;int main(){Mat srcImage1 = imread("mofang1.jpg");Mat srcImage2 = imread("mofang2.jpg");imshow("【原图1】", srcImage1);imshow("【原图2】", srcImage2);//首先对两幅图像进行特征点的检测//先准备参数vector<KeyPoint> keyPoint1;vector<KeyPoint> keyPoint2;SURF surf(2000);//2000为检测算子的阀值surf.detect(srcImage1, keyPoint1);surf.detect(srcImage2, keyPoint2);//利用得到的特征点计算特征描述子//目的：对得到的每个特征点进行特征描述，整合到Mat类型的矩阵中（计算结果是Mat类型的）//该得到的结果矩阵的行数就是特征点的个数，因为是对每个点进行描述，所以每行都会有一个描述的字子向量，共同构成Mat矩阵Mat descriImage1, descriImage2;surf.compute(srcImage1, keyPoint1, descriImage1);surf.compute(srcImage2, keyPoint2, descriImage2);//正式开始在两幅图像中进行匹配//先得到一个匹配向量BruteForceMatcher<L2<float>> BfMatcher;vector<DMatch> g_vMatches;//g_vMatches就是得到的匹配向量BfMatcher.match(descriImage1, descriImage2, g_vMatches);//用绘制函数对匹配向量进行绘制Mat dstImage;drawMatches(srcImage1, keyPoint1, srcImage2, keyPoint2, g_vMatches, dstImage, Scalar(theRNG().uniform(0, 255), theRNG().uniform(0, 255), theRNG().uniform(0, 255)), Scalar(theRNG().uniform(0, 255), theRNG().uniform(0, 255), theRNG().uniform(0, 255)), Mat(), 2);imshow("【特征提取后的图像】", dstImage);waitKey(0);return 0;}