自定义阈值的两种角点检测

阈值的设定主要是通过R值矩阵中的R值大小来确定的：
通过阈值来确定需要的角点R值的范围
R值矩阵的计算参看:https://www.cnblogs.com/Jack-Elvis/p/11640931.html
harris和shiTomasi两种自定义阈值的角点检测代码如下：
  1 #include <opencv2/opencv.hpp>
  2 #include <iostream>
  3 
  4 #include <math.h>
  5 using namespace cv;
  6 using namespace std;
  7 const char* harris_win = "Custom Harris Corners Detector";
  8 const char* shitomasi_win = "Custom Shi-Tomasi Corners Detector";
  9 
 10 Mat src, gray_src;
 11 // harris corner response
 12 Mat harris_dst, harrisRspImg;
 13 double harris_min_rsp;
 14 double harris_max_rsp;
 15 // shi-tomasi corner response
 16 
 17 Mat shiTomasiRsp;
 18 double shitomasi_max_rsp;
 19 double shitomasi_min_rsp;
 20 int sm_qualitylevel = 30;  //shiTomasiRsp矩阵的拖动变量
 21 // quality level
 22 int qualityLevel = 30;    //harrisRspImg R矩阵的拖动变量
 23 int max_count = 100; 
 24 
 25 void CustomHarris_Demo(int, void*);
 26 void CustomShiTomasi_Demo(int, void*);
 27 
 28 
 29 int main(int argc, char** argv) {
 30     src = imread("L:/6.jpg");
 31     if (src.empty()) {
 32         printf("could not load image...\n");
 33         return -1;
 34     }
 35     namedWindow("input image", CV_WINDOW_AUTOSIZE);
 36     imshow("input image", src);
 37     cvtColor(src, gray_src, COLOR_BGR2GRAY);
 38 
 39 
 40     //1.harris方法 计算特征值：
 41     int blockSize = 3;
 42     int ksize = 3;
 43     double k = 0.04;
 44     harris_dst = Mat::zeros(src.size(), CV_32FC(6));
 45     harrisRspImg = Mat::zeros(src.size(), CV_32FC1);
 46     cornerEigenValsAndVecs(gray_src, harris_dst, blockSize, ksize, 4);
 47     // 计算gray_src响应,            输出herris_dst特征矩阵
 48 
 49     for (int row = 0; row < harris_dst.rows; row++) {
 50         for (int col = 0; col < harris_dst.cols; col++) {
 51         double lambda1 = harris_dst.at<Vec6f>(row, col)[0]; //harris_dst:M特征矩阵中的特征值：λ1
 52         double lambda2 = harris_dst.at<Vec6f>(row, col)[1]; //harris_dst:M特征矩阵中的特征值：λ2
 53         harrisRspImg.at<float>(row, col) = lambda1*lambda2 - k*pow((lambda1 + lambda2), 2);
 54         //harrisRspImg：R矩阵   R  =  det(M)-k(traceM)2  //平方
 55         }
 56     }
 57     minMaxLoc(harrisRspImg, &harris_min_rsp, &harris_max_rsp, 0, 0, Mat());
 58     //寻找harrisRspImg矩阵中R的最大最小值: harris_min_rsp  harris_max_rsp
 59     namedWindow(harris_win, CV_WINDOW_AUTOSIZE);
 60     createTrackbar("Quality Value:", harris_win, &qualityLevel, max_count, CustomHarris_Demo);
 61     CustomHarris_Demo(0, 0);
 62 
 63 
 64 
 65     //2. shiTomasi方法 计算最小特征值：
 66     shiTomasiRsp = Mat::zeros(src.size(), CV_32FC1);  //定义R矩阵类型
 67     cornerMinEigenVal(gray_src, shiTomasiRsp, blockSize, ksize, 4);
 68     // 输出shiTomasiRsp特征矩阵；即R矩阵
 69     minMaxLoc(shiTomasiRsp, &shitomasi_min_rsp, &shitomasi_max_rsp, 0, 0, Mat());
 70     //找出shiTomasiRsp矩阵最大最小值
 71     namedWindow(shitomasi_win, CV_WINDOW_AUTOSIZE);
 72     createTrackbar("Quality:", shitomasi_win, &sm_qualitylevel, max_count, CustomShiTomasi_Demo);
 73     CustomShiTomasi_Demo(0, 0);
 74 
 75     waitKey(0);
 76     return 0;
 77 }
 78 
 79 void CustomHarris_Demo(int, void*) {
 80     if (qualityLevel < 10) {
 81         qualityLevel = 10;
 82     }
 83     Mat resultImg = src.clone();
 84     float t = harris_min_rsp + (((double)qualityLevel) / max_count)*(harris_max_rsp - harris_min_rsp);
 85     //阈值t =  R矩阵最小值  +   百分之qualityLevel（滑动条）*  R矩阵的值得范围（max-min）
 86     for (int row = 0; row < src.rows; row++) {
 87         for (int col = 0; col < src.cols; col++) {
 88             float v = harrisRspImg.at<float>(row, col); //提取R矩阵的值给v
 89             if (v > t) {                               //画出v>t的点
 90                 circle(resultImg, Point(col, row), 2, Scalar(0, 0, 255), 2, 8, 0);
 91             }
 92         }
 93     }
 94 
 95     imshow(harris_win, resultImg);
 96 }
 97 
 98 //下面函数与上面CustomHarris_Demo函数一样
 99 void CustomShiTomasi_Demo(int, void*) {
100     if (sm_qualitylevel < 20) {
101         sm_qualitylevel = 20;
102     }
103 
104     Mat resultImg = src.clone();
105     float t = shitomasi_min_rsp + (((double)sm_qualitylevel) / max_count)*(shitomasi_max_rsp - shitomasi_min_rsp);
106     for (int row = 0; row < src.rows; row++) {
107         for (int col = 0; col < src.cols; col++) {
108             float v = shiTomasiRsp.at<float>(row, col);
109             if (v > t) {
110                 circle(resultImg, Point(col, row), 2, Scalar(0, 0, 255), 2, 8, 0);
111             }
112         }
113     }
114     imshow(shitomasi_win, resultImg);
115 }
结果：
1.harris 2.shiTomasi
自定义阈值的两种角点检测

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