opencv_C++ - 自定义角点检测-基于Harris & Shi-Tomasi

1.基于Harris与Shi-Tomasi角点检测；
2.首先通过计算矩阵M得到λ1 λ2 两个特征值根据他们得到角点响应值；
3.然后自己设置阀值实现计算出阀值得到有效响应值的角点位置；

自定义计算Harris角点特征值：

void cv::cornerEigenValsAndVecs( InputArray src, OutputArray dst,
    int blockSize, int ksize, int borderType = BORDER_DEFAULT )

自定义计算Shi-Tomasi角点特征值：

void cv::cornerMinEigenVal( InputArray src, OutputArray dst,
    int blockSize, int ksize = 3, int borderType = BORDER_DEFAULT )

示例：

#include <opencv2/opencv.hpp>
#include <iostream>
#include <math.h>
using namespace std;
using namespace cv;

#define WINDOW_NAME1 "harris自定义检测"
#define WINDOW_NAME2 "tomasi自定义检测"

Mat srcImage, grayImage, harriRspImg, shiTomasiRsp;
double harris_min_rsp, harris_max_rsp;
int g_nThresh_harris = 60, g_nMaxThresh_harris = 300;
int g_nMax_count = 300;

// Shi-Tomasi
int g_nThreshTomasi = 40, g_nMaxThreshTomasi = 300;
double tomasi_min_rsp, tomasi_max_rsp;

void on_fCustomHarrsi(int, void*)
{
	if (g_nThresh_harris < 10)
		g_nThresh_harris = 10;

	Mat resultImage = srcImage.clone();
	
	float t = harris_min_rsp + (((double)g_nThresh_harris) / g_nMax_count) * (harris_max_rsp - harris_min_rsp);
	for (size_t row = 0; row < srcImage.rows; row++)
	{
		for (size_t col = 0; col < srcImage.cols; col++)
		{
			float v = harriRspImg.at<float>(row, col);
			if (v > t)
				circle(resultImage, Point(col, row), 2, Scalar(0, 0, 255));
		}
	}
	imshow(WINDOW_NAME1, resultImage);
}

void on_fCustomTomasi(int, void*)
{
	if (g_nThreshTomasi < 20)
	{
		g_nThreshTomasi = 20;
	}
	Mat resultImage = srcImage.clone();
	float t = tomasi_min_rsp + ((double)g_nThreshTomasi / g_nMax_count) * (tomasi_max_rsp - tomasi_min_rsp);
	for (size_t row = 0;	 row < srcImage.rows; row++)
	{
		for (size_t col = 0; col < srcImage.cols; col++)
		{
			float v = shiTomasiRsp.at<float>(row, col);
			if (v > t)
				circle(resultImage, Point(col, row), 2, Scalar(0, 0, 255));
		}
	}
	imshow(WINDOW_NAME2, resultImage);
}

int main()
{
	srcImage = imread("fangzi.jpg");
	cvtColor(srcImage, grayImage, COLOR_BGR2GRAY);

	// 计算特征值
	int blockSize = 3, ksize = 3;
	Mat harris_dst = Mat::zeros(srcImage.size(), CV_32FC(6)); // 特征结果
	harriRspImg = Mat::zeros(srcImage.size(), CV_32FC1); // 响应结果
	double k = 0.04;

	cornerEigenValsAndVecs(grayImage, harris_dst, blockSize, ksize, 4);

	// 计算响应
	for (size_t row = 0; row < harris_dst.rows; ++row)
	{
		for (size_t col = 0; col < harris_dst.cols; ++col)
		{
			double lambda1 = harris_dst.at<Vec6f>(row, col)[0]; // λ1
			double lambda2 = harris_dst.at<Vec6f>(row, col)[1]; // λ2
			// 根据公式计算响应值 R = λ1 * λ2 - k * pow( (λ1 + λ2), 2 )
			harriRspImg.at<float>(row, col) = lambda1 * lambda2 - k * pow((lambda1 + lambda2), 2);
		}
	}

	// 求出在这些响应点中求取最大值和最小值
	minMaxLoc(harriRspImg, &harris_min_rsp, &harris_max_rsp);

	namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE);
	createTrackbar("quality value:", WINDOW_NAME1, &g_nThresh_harris, g_nMaxThresh_harris, on_fCustomHarrsi);
	on_fCustomHarrsi(0, NULL);

	// 计算Shi-Tomasi最小特征值
	shiTomasiRsp = Mat::zeros(srcImage.size(), CV_32FC1);
	cornerMinEigenVal(grayImage, shiTomasiRsp, blockSize, ksize, 4);

	minMaxLoc(shiTomasiRsp, &tomasi_min_rsp, &tomasi_max_rsp);

	namedWindow(WINDOW_NAME2, WINDOW_AUTOSIZE);
	createTrackbar("tomasi quality value:", WINDOW_NAME2, &g_nThreshTomasi, g_nMaxThreshTomasi, on_fCustomTomasi);
	on_fCustomTomasi(0, NULL);

	waitKey(0);
	return 0;
}

效果图：