这次的tomasi跟上次的harris比,两者原理上很像,可以说tomasi是harris的进化版。个人觉得,如果做角点检测的话,首选tomasi,因为它优化很好,响应速度比harris快。拖动TrackBar时,真的快如风,但使用harris时,有时还会崩掉。
tomasi基本原理如下:
相关API使用及说明:
上面截图来自于贾志刚老师的ppt页面,在此感谢贾志刚老师!
下面附上源代码:
#include<opencv2/opencv.hpp>
#include<iostream>
#include<math.h>
using namespace cv;
using namespace std;
int current_corner = 5;
int max_corner = 200;
const char* output_title = "tomasi demo";
void tomasi(int, void*);
Mat src,gray_src;
int main(int argc, char** argv)
{
src = imread("D:/test/大厦.jpg");
if (!src.data)
{
cout <<"图片未找到"<<endl;
return -1;
}
imshow("input title", src);
namedWindow(output_title, CV_WINDOW_AUTOSIZE);
createTrackbar("corner_num", output_title, ¤t_corner, max_corner, tomasi);
tomasi(0, 0);
waitKey(0);
return 0;
}
void tomasi(int, void *)
{
RNG rng(12345);
cvtColor(src, gray_src, CV_BGR2GRAY);
if (current_corner < 5)
{
current_corner = 5;
}
Mat resultImg = gray_src.clone();
cvtColor(resultImg, resultImg, COLOR_GRAY2RGB);//如果不转,生成的corner也是灰色的
vector<Point2f> corners;
double qualityLevel = 0.01;
double minDistance=10;//两个角点之间的最小距离
int blocksize = 3;
bool useHarrDetector = false;
double k = 0.04;
goodFeaturesToTrack(gray_src, corners, current_corner, qualityLevel, minDistance, Mat(), blocksize, useHarrDetector, k);
cout << "Num of Corner:" << corners.size() << endl;
for (size_t i = 0; i < corners.size(); i++)
{
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
circle(resultImg, corners[i], 2, color, 2, 8, 0);
}
imshow(output_title, resultImg);
}
---------------------------------------------------------程序运行结果-----------------------------------------
输入图片:
角点检测图(当前值:95):
角点检测图(当前值:181):