OpenCV角点检测器测试和比较

关于特征检测，OpenCV提供了通用的接口FeatureDetector类。简单调用其成员函数create()和detect()即可完成角点检测。

选取8个以角点为特征的特征检测器进行测试，包括：

• HARRIS
• GFTT (Shi-tomasi)
• SIFT
• SURF
• FAST
• STAR
• ORB (oriented BRIEF)
• BRISK

测试代码

#include 
#include 
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/nonfree/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/nonfree/nonfree.hpp"

using namespace cv;
using namespace std;

int main()
{
    string imgPath = "road.jpg";
    Mat img = imread(imgPath, CV_LOAD_IMAGE_COLOR);

    vector<string> detectorNames{"HARRIS","GFTT","SIFT",
        "SURF","FAST","STAR","ORB","BRISK"};

    cout << "DetectorName" << '\t' 
        << "Number of corners" << '\t' 
        << "Time used" <<'\t'<<"efficiency" 
        << endl << endl;

    for (string detectorName:detectorNames)
    {
        cout <"\t\t";
        double t = (double)getTickCount();

        //--detect keypoints
        Ptr detector 
            = FeatureDetector::create(detectorName);
        vector keyPoints;
        detector->detect(img, keyPoints, Mat());
        cout << keyPoints.size() << "\t\t\t";

        //--draw keypoints
        Mat imgKeyPoints;
        drawKeypoints(img, keyPoints, imgKeyPoints, 
            Scalar::all(-1), DrawMatchesFlags::DEFAULT);

        imshow(detectorName+" KeyPoints", imgKeyPoints);

        //time used
        t = ((double)getTickCount() - t) / getTickFrequency();
        cout << t << "\t";

        //Number of coners detected per unit time（ms）
        double efficiency = keyPoints.size() / t / 1000;
        cout  << efficiency << endl<0);
    return 0;
}

运行结果

HARRIS

GFTT

SIFT

SURF

FAST

STAR

ORB

BRISK

比较

可以看出，FAST特征检测算法的检测速度是超群的，难怪敢自称“FAST”。从特征点检测效率以及检测出的特征点数来看，SURF表现也不错。以上各个角点检测器，使用的都是OpenCV默认的封装，可能会因为具体的参数设置差异，检测出的角点数有不同，可比性没法说明。但是，特征点检测效率还是很客观的。由以上结果得知，检测效率最高的前3个检测器分别为FAST，ORB，SURF。