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。
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
#include "stdafx.h"
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include <opencv2\calib3d\calib3d.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 \n", max_dist );
printf("-- Min dist : %f \n", 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; }
转自：http://blog.csdn.net/yang_xian521/article/details/6901762