自定义阈值的两种角点检测
作者：互联网
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
标签：src,自定义,阈值,int,角点,harris,rsp,col,row 来源： https://www.cnblogs.com/Jack-Elvis/p/11650206.html