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特征的匹配大致可以分为3个步骤：
对于3个步骤，在OpenCV2中都进行了封装。所有的特征提取方法都实现FeatureDetector接口，DescriptorExtractor接口则封装了对特征向量（特征描述符）的提取，而所有特征向量的匹配都继承了DescriptorMatcher接口。
简单的特征匹配
int main() { const string imgName1 = "x://image//01.jpg"; const string imgName2 = "x://image//02.jpg"; Mat img1 = imread(imgName1); Mat img2 = imread(imgName2); if (!img1.data || !img2.data) return -1; //step1: Detect the keypoints using SURF Detector int minHessian = 400; SurfFeatureDetector detector(minHessian); vector<KeyPoint> keypoints1, keypoints2; detector.detect(img1, keypoints1); detector.detect(img2, keypoints2); //step2: Calculate descriptors (feature vectors) SurfDescriptorExtractor extractor; Mat descriptors1, descriptors2; extractor.compute(img1, keypoints1, descriptors1); extractor.compute(img2, keypoints2, descriptors2); //step3:Matching descriptor vectors with a brute force matcher BFMatcher matcher(NORM_L2); vector<DMatch> matches; matcher.match(descriptors1, descriptors2,matches); //Draw matches Mat imgMatches; drawMatches(img1, keypoints1, img2, keypoints2, matches, imgMatches); namedWindow("Matches"); imshow("Matches", imgMatches); waitKey(); return 0; }
DMatch用来保存匹配后的结果
struct DMatch { //三个构造函数 DMatch() : queryIdx(-1), trainIdx(-1), imgIdx(-1), distance(std::numeric_limits<float>::max()) {} DMatch(int _queryIdx, int _trainIdx, float _distance) : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(-1), distance(_distance) {} DMatch(int _queryIdx, int _trainIdx, int _imgIdx, float _distance) : queryIdx(_queryIdx), trainIdx(_trainIdx), imgIdx(_imgIdx), distance(_distance) {} int queryIdx; //此匹配对应的查询图像的特征描述子索引 int trainIdx; //此匹配对应的训练(模板)图像的特征描述子索引 int imgIdx; //训练图像的索引(若有多个) float distance; //两个特征向量之间的欧氏距离，越小表明匹配度越高。 bool operator < (const DMatch &m) const; };
然后使用drawMatches方法可以匹配后的结构保存为Mat
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原文地址：http://www.cnblogs.com/wangguchangqing/p/4323185.html
