### 四、harris角点响应函数R

（1）矩阵M,将其对角化之后 ，特征值λ1, λ2 分别代表了X 和Y 方向的灰度变化率.

#### 如下图所示，R只与M的特征值有关，判定关系为，角点：R 为大数值正数；边缘：R为大数值负数；平坦区：R为小数值；

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#### cornerHarris 函数API接口：

```C++:void cornerHarris( InputArray src,
OutputArray dst,
int blockSize,
int ksize,
double k,
int borderType=BORDER_DEFAULT )```

#### 代码实现

```#include"stdafx.h"
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
Mat src, gray_src;
int thresh = 130;
int max_count = 255;
const char* output_title = "HarrisCornerDetection Result";
void Harris_Demo(int, void*);
int main(int argc, char** argv) {
if (src.empty()) {
");
return -1;
}
namedWindow("input image", WINDOW_AUTOSIZE);
imshow("input image", src);
namedWindow(output_title, WINDOW_AUTOSIZE);
cvtColor(src, gray_src, COLOR_BGR2GRAY);
createTrackbar("Threshold:", output_title, &thresh, max_count, Harris_Demo);
Harris_Demo(0, 0);
waitKey(0);
return 0;
}
void Harris_Demo(int, void*) {
Mat dst, norm_dst, normScaleDst;
dst = Mat::zeros(gray_src.size(), CV_32FC1);
int blockSize = 2;
int ksize = 3;
double k = 0.04;
cornerHarris(gray_src, dst, blockSize, ksize, k, BORDER_DEFAULT);
normalize(dst, norm_dst, 0, 255, NORM_MINMAX, CV_32FC1, Mat());
convertScaleAbs(norm_dst, normScaleDst);
Mat resultImg = src.clone();
for (int row = 0; row < resultImg.rows; row++) {
uchar* currentRow = normScaleDst.ptr(row);
for (int col = 0; col < resultImg.cols; col++) {
int value = (int)*currentRow;
if (value > thresh) {
circle(resultImg, Point(col, row), 2, Scalar(0, 0, 255), 2, 8, 0);
}
currentRow++;
}
}
imshow(output_title, resultImg);
}```

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threshold = 130时的图像效果

threshold = 130时的图像效果