OpenCV: Surf Matching in Video Sequence

	#include <iostream>
	#include <stdio.h>
	#include <stdlib.h>
	#include <vector>
	#include "opencv2\opencv.hpp"
	#include <opencv2\nonfree\features2d.hpp>
	using namespace std;
	using namespace cv;
	int main()
	{
	Mat img_1, img_2;
	//image = imread("lena.jpg",1);
	//img_1= imread("C:/Users/JimWei/Documents/Visual Studio 2010/Projects/FeatureExtractionOpenCV/FeatureExtractionOpenCV/PCB.jpg", CV_LOAD_IMAGE_GRAYSCALE );
	img_1= imread("PCB.jpg", CV_LOAD_IMAGE_GRAYSCALE );
	if(img_1.empty())
	{
	cout << "Could not open or find the first image" << std::endl ;
	return -1;
	}
	resize(img_1,img_1,Size(0,0),0.15,0.15,INTER_LINEAR);
	imshow("Image 1", img_1);
	VideoCapture capture(0);
	if (!capture.isOpened())
	{
	cerr<<" Could not create capture";
	return -1;
	}
	while(true)
	{
	capture>>img_2;
	cvtColor(img_2,img_2,CV_RGB2GRAY);
	resize(img_2,img_2,Size(0,0),0.5,0.5,INTER_LINEAR);
	imshow("VideoSequence",img_2);
	//resize(img_2,img_2,Size(0,0),0.2,0.2,INTER_LINEAR);
	// Step -1, Detect keypoints using SURF detector
	int minHessian = 100;
	SurfFeatureDetector detector(minHessian);
	vector<KeyPoint> keypoints_1, keypoints_2;
	detector.detect(img_1, keypoints_1);
	detector.detect(img_2, keypoints_2);
	// Step -2, Calculate descriptors (feature vector)
	SurfDescriptorExtractor extractor;
	Mat descriptor_1, descriptor_2;
	extractor.compute(img_1,keypoints_1,descriptor_1);
	extractor.compute(img_2,keypoints_2,descriptor_2);
	// maches with Flann Based Matching.
	double t = (double)getTickCount();
	FlannBasedMatcher matcher2;
	vector<DMatch> matches2;
	matcher2.match(descriptor_1,descriptor_2,matches2);
	t = ((double)getTickCount() - t)/getTickFrequency();
	cout << " Flann Based Matching Time (senconds): " << t<<endl;
	// quick calcualation of max and min distances between keypoints
	double max_dist=0; double min_dist = 100;
	for (int i =0; i < descriptor_1.rows;i++)
	{
	double dist = matches2[i].distance;
	if(max_dist<dist) max_dist = dist;
	if(min_dist>dist) min_dist = dist;
	}
	vector< DMatch> good_matches;
	for (int i=0;i<descriptor_1.rows;i++)
	{
	if( matches2[i].distance<2*min_dist)
	good_matches.push_back(matches2[i]);
	}
	// Draw Good Matches
	Mat img_goodmatches;
	drawMatches(img_1,keypoints_1,img_2,keypoints_2,good_matches,img_goodmatches,Scalar::all(-1),Scalar::all(-1),vector<char>(),DrawMatchesFlags::NOT_DRAW_SINGLE_POINTS);
	vector<Point2f> obj;
	vector<Point2f> scene;
	for( int i = 0; i < good_matches.size(); i++ )
	{
	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);
	vector<Point2f> obj_corners(4);
	obj_corners[0] = cvPoint(0,0);
	obj_corners[1] = cvPoint(img_1.cols,0);
	obj_corners[2] = cvPoint(img_1.cols,img_1.rows);
	obj_corners[3] = cvPoint(0,img_1.rows);
	std::vector<Point2f> scene_corners(4);
	Mat img_object = img_1.clone();
	perspectiveTransform( obj_corners, scene_corners, H);
	Mat img_matches = img_goodmatches;
	//-- 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 );
	if(waitKey(1) == 27) break;
	}
	waitKey(0);
	return 0;
	}

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