点特征提取算法实现

1、Harris_ex实现代码：#inelude #include using namespacestd;using namespacecv;#define WINDOW_NAME【程序窗口 1】#define WINDOW_NAME【程序窗口 2】Mat g_srclmage, g_srclmage1, g_graylmage;int thresh = 30;/ 当前阈值int max_thresh = 175;/ 最大阈值void on_CornerHarris( int , void *);/ 回调函数int main() g_srclmage = imread( Harris_exampl

2、e.jpg ); imshow(”【原始图】,g_srclmage);g_srclmage1 = g_srclmage.cl on e();cvtColor(g_srclmage1, g_graylmage, COLOR_BGR2GRAY n amedWi ndowWVINDOW_NAM E1createTrackbar(阈值：”，WINDOW_NAM&thresh, max_thresh, on_Corn erHarris);on_CornerHarris(0, 0);while (waitKey(9) != 27);return 0;void on_CornerHarris( int ,

3、void *) Mat dstlmage, no rmlmage, scaledlmage;dstlmage = Mat:zeros(g_srclmage.size(), CV_32FC)tg_srclmage1 = g_srclmage.cl on e(); cornerHarris(g_grayImage, dstlmage, 2, 3, 0.04);/ 进行角点检测normalize(dstlmage, normlmage, 0, 255, NORM_MINMAX / 归一化 convertScaleAbs(normlmage, scaledlmage);/ 转换为 8位无符号整形/将检

4、测到的，且符合阈值条件的角点绘制出来for ( int j = 0; j normlmage.rows; j+) for ( int i = 0; i normlmage.cols; i+) if ( int )normlmage.at(j, i)thresh + 80) circle(g_srclmage1,Point (i,j),5, Scalar (10,10, 255),2);circle(scaledlmage,Point (i,j),5, Scalar (10,10, 255),2);imshow(WINDOW_NAMgE1srclmage1); imshow(WINDOW NAM

5、Caiedlmage);程序结果:shi-Tomasi实现代码：#include 255),#include using namespacestd;using namespacecv;#define WINDOW_NAM【Shi-Tomasi 角点检测】Mat g_srclmage, g_graylmage;int g_maxCornerNumber = 33, g_maxTrackbarNumber = 500;RNGng;void on_GoodFeaturesToTrack( int , void *);int main() g_srcImage = imread( Shi-Tomasi

6、.jpg );cvtColor(g_srclmage, g_grayImage, COLOR_BGR2GRAYn amedWi ndoW(VINDOW_NAMEcreateTrackbar(最大角点数 ”， WINDOW_NAM盛g_maxCornerNumber, g_maxTrackbarNumber, on _GoodFeaturesToTrack);imshow(WINDOW_NA M_srcImage);on _GoodFeaturesToTrack(0, 0);while (waitKey(9) != 27);return 0; void on_GoodFeaturesToTrac

7、k( int , void *) if (g_maxCornerNumber = 1) g_maxCornerNumber = 1;vector corners;double qualityLevel = 0.01;/角点检测可接受的最小特征值double minDistanee = 10;/角点之间的最小距离goodFeaturesToTrack(g_graylmage, /输入图像corners, /检测到角点的 g_maxCornerNumber, /角点的最大数量 qualityLevel, /角点检测可接受的最小特征值 minDistanee);/角点之间的最小距离cout 此次检测

8、到的角点数量为： corners.size() endl; Mat copy = g_srclmage.clone();/复制源图像到一个临时变量int r = 4;for ( unsigned int i = 0; i corners.size(); i+)circle(copy, cornersi, r, Scalar (g_rng.uniform(0, g_rng.uniform(0, 255), g_rng.uniform(0, 255), -1);imshow(WINDOW_NA Mopy);程序结果:Shi-Tomasi 甬净哙:换是大角点；133 SIFT_example 实现代

9、码：#inelude #inelude #inelude using namespacestd;using namespacecv;int main()Mat image01 = imread(sift_img1.jpg);Mat image02 = imread(sift_img2.jpg);Mat image1, image2;Gaussia nBlur(image01, image1, Size (3, 3), 0.5);Gaussia nBlur(image02, image2. Size (3, 3), 0.5);/提取特征点SiftFeatureDetector siftDetec

10、tor(30); / 限定提起前 30 个特征点 vector vKeyPoint keyPoint1, keyPoint2;siftDetector.detect(image1, keyPoi nt1);siftDetector.detect(image2, keyPoint2);/绘制特征点drawKeypoints(image1,keyPoint1,image1,Scalar :all(-1),DrawMatchesFlags: DRAW_RICH_KEYPOINTSdrawKeypoints(image2,keyPoint2,image2,Scalar :all(-1),DrawMat

11、chesFlags: DRAW_RICH_KEYPOINTSnamedWindow(KeyPoints of image1, 0);namedWindow(KeyPoints of image2, 0);imshow(KeyPoi nts of image1, image1);imshow(KeyPoi nts of image2, image2);/特征点描述，为下边的特征点匹配做准备SiftDescriptorExtractor siftDescriptor;Mat imageDesc1, imageDesc2; siftDpute(image1, keyPo in t1, imageDesc1); siftDpute(image2, keyPoint2, imageDesc2);/特征点匹配并显示匹配结果BruteForceMatcher L2 matcher;vector matchePoints; matcher.match(imageDesc1, imageDesc2, matchePoi nts, Mat();Mat im