OpenCV: SURF Feature extractor

Main steps:

1. Read Two Images
2. Resize to half of its size
3. Detectect Keypoints  using SURF
4. Calculate Feature Descriptor
5. Matching Descriptor using Brute Force Matcher

Original Image

 SURF Keypoints

SURF Matches

	#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;
	img_1= imread("lena.jpg", CV_LOAD_IMAGE_GRAYSCALE );
	img_2= imread("lena.jpg", CV_LOAD_IMAGE_GRAYSCALE );
	if(img_1.empty()||img_2.empty())
	{
	cout << "Could not open or find the image" << std::endl ;
	return -1;
	}
	/// Resize
	resize(img_1,img_1,Size(0,0),0.5,0.5,INTER_LINEAR);
	resize(img_2,img_2,Size(0,0),0.5,0.5,INTER_LINEAR);
	imshow("Image 1", img_1);
	imshow("Image 2", img_2);
	// Step -1, Detect keypoints using SURF detector
	int minHessian = 400;
	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);
	//step - 3, Matching descriptor vectors with a brute force mathcher
	BFMatcher matcher(NORM_L2);
	vector<DMatch> matches;
	matcher.match(descriptor_1, descriptor_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;
	}

view raw gistfile1.cpp hosted with ❤ by GitHub