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。
- Use the FeatureDetector interface in order to find interest points. Specifically:
- Use the SurfFeatureDetector and its function detect to perform the detection process
- Use the function drawKeypoints to draw the detected keypoints
#include "stdafx.h" #include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" using namespace cv; void readme(); /** @function main */ int main( int argc, char** argv ) { /* if( argc != 3 ) { readme(); return -1; } */ Mat img_1 = imread( "zhang.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "guo.jpg", CV_LOAD_IMAGE_GRAYSCALE ); if( !img_1.data || !img_2.data ) { std::cout<< " --(!) Error reading images " << std::endl; return -1; } //-- Step 1: Detect the keypoints using SURF Detector int minHessian = 400; SurfFeatureDetector detector( minHessian ); std::vector<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); // 特征点向量 detector.detect( img_2, keypoints_2 ); //-- Draw keypoints Mat img_keypoints_1; Mat img_keypoints_2; drawKeypoints( img_1, keypoints_1, img_keypoints_1, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); drawKeypoints( img_2, keypoints_2, img_keypoints_2, Scalar::all(-1), DrawMatchesFlags::DEFAULT ); //-- Show detected (drawn) keypoints imshow("Keypoints 1", img_keypoints_1 ); imshow("Keypoints 2", img_keypoints_2 ); waitKey(0); return 0; } /** @function readme */ void readme() { std::cout << " Usage: ./SURF_detector <img1> <img2>" << std::endl; }
检测keypoints点的检测器是SURF，获取描述子也是用到SURF来描述，而用到的匹配器是FlannBased，最后通过findHomography寻找单映射矩阵，perspectiveTransform获得最终的目标
findHomography 函数是求两幅图像的单应性矩阵，它是一个3*3的矩阵
#include "stdafx.h" #include <stdio.h> #include <iostream> #include "opencv2/core/core.hpp" #include "opencv2/features2d/features2d.hpp" #include "opencv2/highgui/highgui.hpp" #include <opencv2calib3dcalib3d.hpp> using namespace cv; void readme(); int main( int argc, char** argv ) { /* if( argc != 3 ) { return -1; }*/ Mat img_1 = imread( "test1.jpg", CV_LOAD_IMAGE_GRAYSCALE ); Mat img_2 = imread( "test2.jpg", 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<KeyPoint> keypoints_1, keypoints_2; detector.detect( img_1, keypoints_1 ); detector.detect( img_2, keypoints_2 ); // 角点集合 —— 数目确定 //-- Step 2: Calculate descriptors (feature vectors) SurfDescriptorExtractor extractor; // 角点描述子 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<float> > 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 ); */ //-- 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 ", max_dist ); printf("-- Min dist : %f ", min_dist ); //-- Draw only "good" matches (i.e. whose distance is less than 2*min_dist ) —— 阈值 //-- 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]); // 在匹配源头限制 } } //-- 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<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS ); //-- Show detected matches imshow( "Good Matches", img_matches ); //-- Localize the object from img_1 in img_2 std::vector<Point2f> obj; std::vector<Point2f> 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 ); // findHomography 函数是求两幅图像的单应性矩阵，它是一个3*3的矩阵 //-- 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<double>(2,0)*x + H.at<double>(2,1)*y + H.at<double>(2,2) ); double X = ( H.at<double>(0,0)*x + H.at<double>(0,1)*y + H.at<double>(0,2) )*Z; double Y = ( H.at<double>(1,0)*x + H.at<double>(1,1)*y + H.at<double>(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 ); waitKey(0); return 0; } /** * @function readme */ void readme() { std::cout << " Usage: ./SURF_descriptor <img1> <img2>" << std::endl; }
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。
转自：http://blog.csdn.net/yang_xian521/article/details/6901762