**在视频质量诊断中，我们通常会涉及到“画面抖动”的检测。在此过程中就需要在视频中隔N帧取一帧图像，然后在获取的两帧图像上找出特征点，并进行相应的匹配。** **当然了，这一过程中会出现很多的问题，例如：特征点失配等。** **本文主要关注特征点匹配及去除失配点的方法。** **主要功能：对统一物体拍了两张照片，只是第二张图片有选择和尺度的变化。现在要分别对两幅图像提取特征点，然后将这些特征点匹配，使其尽量相互对应。** **下面，本文通过采用surf特征，分别使用Brute-force matcher和Flann-based matcher对特征点进行相互匹配。** **1、 BFMatcher matcher** **第一段代码摘自opencv官网的教程：** ``` #include "stdafx.h" #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include "opencv2/nonfree/features2d.hpp" #include "opencv2/calib3d/calib3d.hpp" #include "opencv2/imgproc/imgproc.hpp" using namespace cv; using namespace std; int _tmain(int argc, _TCHAR* argv[]) { Mat img_1 = imread( "haha1.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "haha2.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { return -1; } //-- Step 1: Detect the keypoints using SURF Detector //Threshold for hessian keypoint detector used in SURF int minHessian = 15000; SurfFeatureDetector 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) SurfDescriptorExtractor 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,false); 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 ); //-- Show detected matches imshow("Matches", img_matches ); waitKey(0); return 0; } ```