【cv】使用opencv的FeatureDetector，DescriptorExtractor，DescriptorMatcher

想要使用cv::FeatureDetector方便地创建各种detector，对opencv不是很熟，所以就想试试能不能直接找到源码，居然费了好一番功夫。

这个是能搜索到的比较全的解释。

http://blog.csdn.net/xiaowei_cqu/article/details/8652096

OpenCV官网：
http://docs.opencv.org/modules/features2d/doc/common_interfaces_of_feature_detectors.html#featuredetector-create

"FAST" – FastFeatureDetector
"STAR" – StarFeatureDetector
"SIFT" – SIFT (nonfree module)
"SURF" – SURF (nonfree module)
"ORB" – ORB
"BRISK" – BRISK
"MSER" – MSER
"GFTT" – GoodFeaturesToTrackDetector
"HARRIS" – GoodFeaturesToTrackDetector with Harris detector enabled
"Dense" – DenseFeatureDetector
"SimpleBlob" – SimpleBlobDetector

可以看到 2.4.8中提供了11种Detector，多了一种 Brisk。其中 SIFT和SURF都是需要使用nonfree module的。可以使用第一篇博文中的代码，注意 additional dependencies：

opencv_core248d.lib
opencv_highgui248d.lib
opencv_features2d248d.lib
opencv_imgproc248d.lib
opencv_nonfree248d.lib

为了方便大家，其实也包括自己啦，完整的代码可以如下：

#include "stdafx.h"
#include 
#include 

#include "opencv2/highgui/highgui.hpp"
#include "opencv\cv.h"
#include  
#include 

//#include "opencv2/features2d/features2d.hpp"
//#include 
//#include 
//#include 

#include "opencv2/features2d/features2d.hpp"

using namespace std;
using namespace cv;

int main()
{

	initModule_nonfree();//if use SIFT or SURF
	Ptr detector = FeatureDetector::create( "SURF" );
	Ptr descriptor_extractor = DescriptorExtractor::create( "SURF" );
	Ptr descriptor_matcher = DescriptorMatcher::create( "BruteForce" );
	if( detector.empty() || descriptor_extractor.empty() )
		throw runtime_error("fail to create detector!");

	Mat img1 = imread("d:/image0001.jpg");
	Mat img2 = imread("d:/image0002.jpg");

	//detect keypoints;
	vector keypoints1,keypoints2;
	detector->detect( img1, keypoints1 );
	detector->detect( img2, keypoints2 );
	cout <<"img1:"<< keypoints1.size() << " points  img2:" <" << endl;

	//compute descriptors for keypoints;
	cout << "< Computing descriptors for keypoints from images..." << endl;
	Mat descriptors1,descriptors2;
	descriptor_extractor->compute( img1, keypoints1, descriptors1 );
	descriptor_extractor->compute( img2, keypoints2, descriptors2 );

	cout<"<" << endl;

	//Draw And Match img1,img2 keypoints
	Mat img_keypoints1,img_keypoints2;
	drawKeypoints(img1,keypoints1,img_keypoints1,Scalar::all(-1),0);
	drawKeypoints(img2,keypoints2,img_keypoints2,Scalar::all(-1),0);
	imshow("Box_in_scene keyPoints",img_keypoints1);
	imshow("Box keyPoints",img_keypoints2);

	descriptor_extractor->compute( img1, keypoints1, descriptors1 );  
	vector matches;
	descriptor_matcher->match( descriptors1, descriptors2, matches );

	Mat img_matches;
	drawMatches(img1,keypoints1,img2,keypoints2,matches,img_matches,Scalar::all(-1),CV_RGB(255,255,255),Mat(),4);

	imshow("Mathc",img_matches);
	waitKey(0);
	return 0;
}

如果不想使用sift或者surf，下面这种就足够了。

#include "stdafx.h"
#include 


#include 
#include "opencv2/features2d/features2d.hpp"



using namespace std;

int main()
{

	//read image
	string filename = "D:/image0001.jpg";
	cv::Mat img = cv::imread(filename, 0);
	//CV_Assert(!img.empty());
	
	//create detector
	cv::Ptr detector;
	detector = cv::FeatureDetector::create("ORB");

	//detect
	cv::Mat output_img;
	vector key_points;
	detector->detect(img, key_points);

	//show
	drawKeypoints(img, key_points, output_img);

	cv::namedWindow("Image");
	cv::imshow("Image", output_img);
	cv::waitKey(0);


	return 0;
}

additional dependencies：

opencv_core248d.lib
opencv_highgui248d.lib
opencv_features2d248d.lib
opencv_imgproc248d.lib