shi_tomasi特征点，GFTTDetector

哈里斯（Harris）角点会出现聚簇现象（cornerHarris函数），为避免此现象，提出shi_tomasi角点检测算法goodFeatureToTrack，GFTTDetector实现了此算法。

【函数】

Ptr<GFTTDetector> create( int maxCorners=1000, double qualityLevel=0.01, double minDistance=1,int blockSize=3, bool useHarrisDetector=false, double k=0.04 );

Ptr<GFTTDetector> create( int maxCorners, double qualityLevel, double minDistance,int blockSize, int gradiantSize, bool useHarrisDetector=false, double k=0.04 );

【参数说明】

maxCorners——最大角点数目 

qualityLevel——角点可以接受的最小特征值,一般0.1或者0.01,不超过1 

minDistance——角点之间的最小距离

blockSize——倒数自相关矩阵的邻域范围 

useHarrisDetector——是否使用角点检测 

khessian——自相关矩阵的相对权重系数，一般为0.04

【案例】

#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;

int main()
{
    Mat srcImage = imread("D:/sunflower.png");
        Mat srcGrayImage;
        if (srcImage.channels() == 3)
        {
            cvtColor(srcImage,srcGrayImage,CV_RGB2GRAY);
        }
        else
        {
            srcImage.copyTo(srcGrayImage);
        }
        vector<KeyPoint>detectKeyPoint;
        Mat keyPointImage1,keyPointImage2;

        Ptr<GFTTDetector> gftt = GFTTDetector::create();
        gftt->detect(srcGrayImage,detectKeyPoint);
        drawKeypoints(srcImage,detectKeyPoint,keyPointImage1,Scalar(0,0,255),DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
        drawKeypoints(srcImage,detectKeyPoint,keyPointImage2,Scalar(0,0,255),DrawMatchesFlags::DEFAULT);

        imshow("src image",srcImage);
        imshow("keyPoint image1",keyPointImage1);
        imshow("keyPoint image2",keyPointImage2);

        waitKey(0);
        return 0;
}