OpenCV学习笔记（九）——2维特征Feature2D

基于特征点的图像匹配是图像处理中经常会遇到的问题，手动选取特征点太麻烦了。比较经典常用的特征点自动提取的办法有Harris特征、SIFT特征、SURF特征。

先介绍利用SURF特征的特征描述办法，其操作封装在类SurfFeatureDetector中，利用类内的detect函数可以检测出SURF特征的关键点，保存在vector容器中。第二部利用SurfDescriptorExtractor类进行特征向量的相关计算。将之前的vector变量变成向量矩阵形式保存在Mat中。最后强行匹配两幅图像的特征向量，利用了类BruteForceMatcher中的函数match。代码如下：

/** * @file SURF_descriptor * @brief SURF detector + descritpor + BruteForce Matcher + drawing matches with OpenCV functions * @author A. Huaman */ #include #include #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" using namespace cv; void readme(); /** * @function main * @brief Main function */ int main( int argc, char** argv ) { if( argc != 3 ) { return -1; } Mat img_1 = imread( argv[1], CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( argv[2], CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { return -1; } //-- Step 1: Detect the keypoints using SURF Detector int minHessian = 400; SurfFeatureDetector detector( minHessian ); std::vector 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 BruteForceMatcher< L2 > matcher; 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 ); //-- Show detected matches imshow("Matches", img_matches ); waitKey(0); return 0; } /** * @function readme */ void readme() { std::cout << " Usage: ./SURF_descriptor " << std::endl; }

当然，进行强匹配的效果不够理想，这里再介绍一种FLANN特征匹配算法。前两步与上述代码相同，第三步利用FlannBasedMatcher类进行特征匹配，并只保留好的特征匹配点，代码如下：

//-- Step 3: Matching descriptor vectors using FLANN matcher FlannBasedMatcher matcher; std::vector< DMatch > matches; matcher.match( descriptors_1, descriptors_2, matches ); double max_dist = 0; double min_dist = 100; //-- Quick calculation of max and min distances between keypoints for( int i = 0; i < descriptors_1.rows; i++ ) { double dist = matches[i].distance; if( dist < min_dist ) min_dist = dist; if( dist > max_dist ) max_dist = dist; } printf("-- Max dist : %f \n", max_dist ); printf("-- Min dist : %f \n", min_dist ); //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist ) //-- PS.- radiusMatch can also be used here. std::vector< DMatch > good_matches; for( int i = 0; i < descriptors_1.rows; i++ ) { if( matches[i].distance < 2*min_dist ) { good_matches.push_back( matches[i]); } } //-- Draw only "good" matches Mat img_matches; drawMatches( img_1, keypoints_1, img_2, keypoints_2, good_matches, img_matches, Scalar::all(-1), Scalar::all(-1), vector(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //-- Show detected matches imshow( "Good Matches", img_matches );

在FLANN特征匹配的基础上，还可以进一步利用Homography映射找出已知物体。具体来说就是利用findHomography函数利用匹配的关键点找出相应的变换，再利用perspectiveTransform函数映射点群。具体代码如下：

//-- Localize the object from img_1 in img_2 std::vector obj; std::vector scene; for( int i = 0; i < good_matches.size(); i++ ) { //-- Get the keypoints from the good matches obj.push_back( keypoints_1[ good_matches[i].queryIdx ].pt ); scene.push_back( keypoints_2[ good_matches[i].trainIdx ].pt ); } Mat H = findHomography( obj, scene, CV_RANSAC ); //-- Get the corners from the image_1 ( the object to be "detected" ) Point2f obj_corners[4] = { cvPoint(0,0), cvPoint( img_1.cols, 0 ), cvPoint( img_1.cols, img_1.rows ), cvPoint( 0, img_1.rows ) }; Point scene_corners[4]; //-- Map these corners in the scene ( image_2) for( int i = 0; i < 4; i++ ) { double x = obj_corners[i].x; double y = obj_corners[i].y; double Z = 1./( H.at(2,0)*x + H.at(2,1)*y + H.at(2,2) ); double X = ( H.at(0,0)*x + H.at(0,1)*y + H.at(0,2) )*Z; double Y = ( H.at(1,0)*x + H.at(1,1)*y + H.at(1,2) )*Z; scene_corners[i] = cvPoint( cvRound(X) + img_1.cols, cvRound(Y) ); } //-- Draw lines between the corners (the mapped object in the scene - image_2 ) line( img_matches, scene_corners[0], scene_corners[1], Scalar(0, 255, 0), 2 ); line( img_matches, scene_corners[1], scene_corners[2], Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[2], scene_corners[3], Scalar( 0, 255, 0), 2 ); line( img_matches, scene_corners[3], scene_corners[0], Scalar( 0, 255, 0), 2 ); //-- Show detected matches imshow( "Good Matches & Object detection", img_matches );
然后再看一下Harris特征检测，在计算机视觉中，通常需要找出两帧图像的匹配点，如果能找到两幅图像如何相关，就能提取出两幅图像的信息。我们说的特征的最大特点就是它具有唯一可识别这一特点，图像特征的类型通常指边界、角点（兴趣点）、斑点（兴趣区域）。角点就是图像的一个局部特征，应用广泛。harris角点检测是一种直接基于灰度图像的角点提取算法，稳定性高，尤其对L型角点检测精度高，但由于采用了高斯滤波，运算速度相对较慢，角点信息有丢失和位置偏移的现象，而且角点提取有聚簇现象。具体实现就是使用函数cornerHarris实现。

除了利用Harris进行角点检测，还可以利用Shi-Tomasi方法进行角点检测。使用函数goodFeaturesToTrack对角点进行检测，效果也不错。也可以自己制作角点检测的函数，需要用到cornerMinEigenVal函数和minMaxLoc函数，最后的特征点选取，判断条件要根据自己的情况编辑。如果对特征点，角点的精度要求更高，可以用cornerSubPix函数将角点定位到子像素。