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<KeyPoint>& keypoints,
const Mat& mask=Mat() ) const;
void detect( const vector<Mat>& images,
vector<vector<KeyPoint> >& keypoints,
const vector<Mat>& masks=vector<Mat>() ) const;
virtual void read(const FileNode&);
virtual void write(FileStorage&) const;
static Ptr<FeatureDetector> 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> FeatureDetector::create(const string& detectorType);
OpenCV 2.4.3提供了10种特征检测方法:
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<FeatureDetector> detector = FeatureDetector::create( "SIFT" );
Ptr<DescriptorExtractor> descriptor_extractor = DescriptorExtractor::create( "SIFT" );
Ptr<DescriptorMatcher> 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<KeyPoint> keypoints1,keypoints2;
detector->detect( img1, keypoints1 );
detector->detect( img2, keypoints2 );
cout <<"img1:"<< keypoints1.size() << " points img2:" <<keypoints2.size()
<< " points" << endl << ">" << 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<<"Descriptors Size: "<<descriptors2.size()<<" >"<<endl;
cout<<endl<<"Descriptor's Column: "<<descriptors2.cols<<endl
<<"Descriptor's Row: "<<descriptors2.rows<<endl;
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<DMatch> 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
Mat image = imread("images\\features.jpg");
Mat descriptors;
vector<KeyPoint> 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
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