Opencv3笔记32——Shi-Tomasi角点检测

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1.角点检测概述

Shi-Tomasi算法是Harris算法的改进，此算法最原始定义是将矩阵M的行列式与M的迹相减，再将差值同预定的给定的阈值进行比较。改进的方法是：将两个特征值中较小的一个大于阈值，就是强角点

2.确定图像强角点：goodFeaturesToTrack()函数

函数原型：

void goodFeaturesToTrack(InputArray image,OutputArray corners,int maxCorners,double qualityLevel,double minDistance,InputArray mask=noArray,int blockSize = 3,bool useHarrisDetector=false,double k = 0.04)

3.综合实例：Shi-Tomasi角点检测

#include <opencv2/opencv.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>

using namespace cv;
using namespace std;

//描述:定义一些辅助宏

#define WINDOW_NAME "【Shi-Tomasi角点检测】"

//全局变量声明
Mat g_srcImage;
Mat g_grayImage;
int g_maxCornerNumber = 33;
int g_maxTrackbarNumber = 500;
RNG g_rng(12345);

//回调函数
void on_GoodFeaturesToTrack(int, void *)
{
    if (g_maxCornerNumber <= 1)
    {
        g_maxCornerNumber = 1;
    }
    //shi-tomasi算法
    vector<Point2f> corner;
    double qualityLevel = 0.01;     //角点检测可接收的最小特征
    double minDistance = 10;        //角点之间的最小的距离
    int blockSize = 3;              //计算导数自相关矩阵时指定的邻域范围
    double k = 0.04;                //权重系数
    Mat copy = g_srcImage.clone();
    //进行角点检测
    goodFeaturesToTrack(g_grayImage, corner, g_maxCornerNumber, qualityLevel, minDistance, Mat(), blockSize, false, k);
    //输出文字信息
    cout << ">此次检测到的角点数量为：" << corner.size() << endl;
    //绘制检测到的角点
    int r = 4;
    for (unsigned int i = 0; i < corner.size(); i++)
    {
        circle(copy, corner[i], r, Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)), -1, 8, 0);
    }
    //显示更新窗口
    imshow(WINDOW_NAME, copy);
}
int main()
{
    //载入原始图像
    g_srcImage = imread("1.jpg", 1);
    cvtColor(g_srcImage, g_grayImage, COLOR_BGR2GRAY);

    //创建窗口和滑动条
    namedWindow(WINDOW_NAME, WINDOW_AUTOSIZE);
    createTrackbar("最大角点数：", WINDOW_NAME, &g_maxCornerNumber, g_maxTrackbarNumber, on_GoodFeaturesToTrack);
    imshow(WINDOW_NAME, g_srcImage);
    on_GoodFeaturesToTrack(0,0);
    waitKey(0);
    return 0;
}