【OpenCV】特征检测器 FeatureDetector

OpenCV提供FeatureDetector实现特征检测及匹配

class CV_EXPORTS FeatureDetector
{
public:
	virtual ~FeatureDetector();
	void detect( const Mat& image, vector& keypoints,
		const Mat& mask=Mat() ) const;
	void detect( const vector& images,
		vector >& keypoints,
		const vector& masks=vector() ) const;
	virtual void read(const FileNode&);
	virtual void write(FileStorage&) const;
	static Ptr create( const string& detectorType );
protected:
	...
};

FeatureDetetor是虚类，通过定义FeatureDetector的对象可以使用多种特征检测方法。通过create()函数调用：

Ptr FeatureDetector::create(const string& detectorType);

OpenCV 2.4.3提供了10种特征检测方法：

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

图片中的特征大体可分为三种：点特征、线特征、块特征。

FAST算法是Rosten提出的一种快速提取的点特征 [1]，Harris与GFTT也是点特征，更具体来说是角点特征（ 参考这里）。

SimpleBlob是简单块特征，可以通过设置 SimpleBlobDetector的参数决定提取图像块的主要性质，提供5种：

颜色 By color、面积 By area、圆形度 By circularity、最大inertia（不知道怎么翻译）与最小inertia的比例 By ratio of the minimum inertia to maximum inertia、以及凸性 By convexity.

最常用的当属SIFT，尺度不变特征匹配算法（ 参考这里）；以及后来发展起来的SURF，都可以看做较为复杂的块特征。这两个算法在OpenCV nonfree的模块里面，需要在附件引用项中添加opencv_nonfree243.lib，同时在代码中加入：

initModule_nonfree();

至于其他几种算法，我就不太了解了^_^

一个简单的使用演示：

int main()
{

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

	Mat img1 = imread("images\\box_in_scene.png");
	Mat img2 = imread("images\\box.png");

	//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(10000);
	return 0;
}

特征检测结果如图：

Box_in_scene

Box

特征点匹配结果：

Match

另一点需要一提的是 SimpleBlob的实现是有Bug的。不能直接通过Ptr detector = FeatureDetector::create("SimpleBlob"); 语句来调用，而应该直接创建 SimpleBlobDetector的对象：

        Mat image = imread("images\\features.jpg");
	Mat descriptors;
	vector keypoints;
	SimpleBlobDetector::Params params;
	//params.minThreshold = 10;
	//params.maxThreshold = 100;
	//params.thresholdStep = 10;
	//params.minArea = 10; 
	//params.minConvexity = 0.3;
	//params.minInertiaRatio = 0.01;
	//params.maxArea = 8000;
	//params.maxConvexity = 10;
	//params.filterByColor = false;
	//params.filterByCircularity = false;
	SimpleBlobDetector blobDetector( params );
	blobDetector.create("SimpleBlob");
	blobDetector.detect( image, keypoints );
	drawKeypoints(image, keypoints, image, Scalar(255,0,0));

以下是SimpleBlobDetector按颜色检测的图像特征：

[1] Rosten. Machine Learning for High-speed Corner Detection, 2006

（转载请注明作者和出处：http://blog.csdn.net/xiaowei_cqu未经允许请勿用于商业用途）