Shi-Tomasi角点检测

Shi-Tomasi原理几乎和Harris一样的，只不过最后计算角点响应的公式发生了变化

变为　　min（λ1，λ2）

这样计算会变得更简单

 

API

cv::goodFeaturesToTrack(

InputArray src,　　//默认灰度图像

OutputArray corners, 

int maxCorners,

double qualityLevel,

double minDistance,

InputArray mask=noArray(),

int blocksize=3,

bool useHarriDector=false,

double k=0.04

)

 

Demo

#include"pch.h"
#include<iostream>
#include<opencv2/opencv.hpp>
#include<math.h>
using namespace std;
using namespace cv;


const char* output_title = "Shi-Tomasi-CornerDetection Reslut";
int thresh = 130;
int max_count = 255;
Mat src, gray_src;
int num_corners = 25;
int max_corners = 200;
RNG rng(12345);
void ShiTomasi_Demo(int, void*);

int main(int argc, char** argv)
{
    src = imread("1.jpg");
    imshow("input img", src);

    namedWindow(output_title, CV_WINDOW_AUTOSIZE);
    cvtColor(src, gray_src, COLOR_BGR2GRAY);

    createTrackbar("Num Corners:", output_title, &num_corners, max_corners, ShiTomasi_Demo);
    ShiTomasi_Demo(0, 0);
    waitKey();
    return 0;
}

void ShiTomasi_Demo(int, void*)
{
    if (num_corners < 5)
    {
        num_corners = 5;
    }
    vector<Point2f> corners;
    double qualityLevel = 0.01;
    double minDistance = 10;
    int blockSize = 3;
    bool useHarris = false;
    double k = 0.04;
    Mat resultImg = src.clone();
    goodFeaturesToTrack(gray_src, corners, num_corners, qualityLevel, minDistance, Mat(), blockSize, useHarris, k);
    cout << "Number of corner :" << corners.size()<<endl;

    for (size_t t = 0; t < corners.size(); ++t)
    {
        circle(resultImg, corners[t], 2, Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255)),2,8,0);
    }
    imshow(output_title, resultImg);
}