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? 閾值的設(shè)定主要是通過R值矩陣中的R值大小來確定的： 通過閾值來確定需要的角點R值的范圍 R值矩陣的計算參看:https://www.cnblogs.com/Jack-Elvis/p/11640931.html ? ? ?harris和shiTomasi兩種自定義閾值的角點檢測代碼如下： ? 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矩陣的拖動變量
21 // quality level
22 int qualityLevel = 30; //harrisRspImg R矩陣的拖動變量
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 計算特征值：
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 // 計算gray_src響應(yīng), 輸出herris_dst特征矩陣
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特征矩陣中的特征值：λ1?
52 double lambda2 = harris_dst.at<Vec6f>(row, col)[1]; //harris_dst:M特征矩陣中的特征值：λ2
53 harrisRspImg.at<float>(row, col) = lambda1*lambda2 - k*pow((lambda1 lambda2), 2);
54 //harrisRspImg：R矩陣 R = det(M)-k(traceM)2 //平方
55 }
56 }
57 minMaxLoc(harrisRspImg, &harris_min_rsp, &harris_max_rsp, 0, 0, Mat());
58 //尋找harrisRspImg矩陣中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 計算最小特征值：
66 shiTomasiRsp = Mat::zeros(src.size(), CV_32FC1); //定義R矩陣類型
67 cornerMinEigenVal(gray_src, shiTomasiRsp, blockSize, ksize, 4);
68 // 輸出shiTomasiRsp特征矩陣；即R矩陣
69 minMaxLoc(shiTomasiRsp, &shitomasi_min_rsp, &shitomasi_max_rsp, 0, 0, Mat());
70 //找出shiTomasiRsp矩陣最大最小值
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 //閾值t = R矩陣最小值 百分之qualityLevel（滑動條）* R矩陣的值得范圍（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矩陣的值給v
89 if (v > t) { //畫出v>t的點
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 //下面函數(shù)與上面CustomHarris_Demo函數(shù)一樣
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 }
? 結(jié)果： 1.harris? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? 2.shiTomasi
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?? ? 來源：https://www./content-4-500101.html |
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