利用FLANN SURF匹配

对应不同版本头文件可能不同。

#include "stdafx.h"
#include<opencv2\nonfree\nonfree.hpp>
#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;

/** @function main */
int main( int argc, char** argv )
	{

	Mat img_object = imread( "d:/111.png", CV_LOAD_IMAGE_GRAYSCALE );//模版图像
	Mat img_scene = imread( "d:/11.png", CV_LOAD_IMAGE_GRAYSCALE );//场景图像

	if( !img_object.data || !img_scene.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_object, keypoints_scene;

	detector.detect( img_object, keypoints_object );
	detector.detect( img_scene, keypoints_scene );

	//-- Step 2: Calculate descriptors (feature vectors)
	SurfDescriptorExtractor extractor;

	Mat descriptors_object, descriptors_scene;

	extractor.compute( img_object, keypoints_object, descriptors_object );
	extractor.compute( img_scene, keypoints_scene, descriptors_scene );

	//-- Step 3: Matching descriptor vectors using FLANN matcher
	FlannBasedMatcher matcher;
	std::vector< DMatch > matches;
	matcher.match( descriptors_object, descriptors_scene, matches );

	double max_dist = 0; double min_dist = 100;

	//-- Quick calculation of max and min distances between keypoints
	for( int i = 0; i < descriptors_object.rows; i++ )
		{ double dist = matches[i].distance;
	if( dist < min_dist ) min_dist = dist;
	if( dist > max_dist ) max_dist = dist;
		}
	  if (min_dist <0.1)	  //防止min_dist 为0  程序不能正常运行
	  {
		  min_dist = 0.1;
	  }
	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 3*min_dist )
	std::vector< DMatch > good_matches;

	for( int i = 0; i < descriptors_object.rows; i++ )
		{ if( matches[i].distance < 3*min_dist )
		{ good_matches.push_back( matches[i]); }
		}

	Mat img_matches;
	drawMatches( img_object, keypoints_object, img_scene, keypoints_scene,
		good_matches, img_matches, Scalar::all(-1), Scalar::all(-1),
		vector<char>(), DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS );

	//-- Localize the object
	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_object[ good_matches[i].queryIdx ].pt );
		scene.push_back( keypoints_scene[ good_matches[i].trainIdx ].pt );
		}

	Mat H = findHomography( obj, scene, CV_RANSAC );
 
	//-- Get the corners from the image_1 ( the object to be "detected" )
	std::vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0,0); obj_corners[1] = cvPoint( img_object.cols, 0 );
	obj_corners[2] = cvPoint( img_object.cols, img_object.rows ); obj_corners[3] = cvPoint( 0, img_object.rows );
	std::vector<Point2f> scene_corners(4);

	perspectiveTransform( obj_corners, scene_corners, H);  //透视变换

	//-- Draw lines between the corners (the mapped object in the scene - image_2 )
	line( img_matches, scene_corners[0] + Point2f( img_object.cols, 0), scene_corners[1] + Point2f( img_object.cols, 0), Scalar(0, 255, 0), 4 );
	line( img_matches, scene_corners[1] + Point2f( img_object.cols, 0), scene_corners[2] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
	line( img_matches, scene_corners[2] + Point2f( img_object.cols, 0), scene_corners[3] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );
	line( img_matches, scene_corners[3] + Point2f( img_object.cols, 0), scene_corners[0] + Point2f( img_object.cols, 0), Scalar( 0, 255, 0), 4 );

	//-- Show detected matches
	imshow( "Good Matches & Object detection", img_matches );

	waitKey(0);
	return 0;
	}

模版图像

匹配后图像