opencv学习之路（32）、角点检测
一、角点检测的相关概念
二、Harris角点检测——cornerHarris()
参考网址: http://www.cnblogs.com/ronny/p/4009425.html
#include "opencv2/opencv.hpp" #include<iostream> using namespace std; using namespace cv; void main() { Mat img = imread("E://3.jpg"); imshow("src", img); Mat result = img.clone(); Mat gray, dst , corner_img;//corner_img存放检测后的角点图像 cvtColor(img, gray, CV_BGR2GRAY); cornerHarris(gray, corner_img, 2, 3, 0.04);//cornerHarris角点检测 //imshow("corner", corner_img); threshold(corner_img, dst, 0.015, 255, CV_THRESH_BINARY); imshow("dst", dst); int rowNumber = gray.rows; //获取行数 int colNumber = gray.cols; //获取每一行的元素 cout << rowNumber << endl; cout << colNumber << endl; cout << dst.type() << endl; for (int i = 0; i<rowNumber; i++) { for (int j = 0; j<colNumber; j++) { if (dst.at<float>(i, j) == 255)//二值化后，灰度值为255为角点 { circle(result, Point(j, i),3, Scalar(0, 255, 0), 2, 8); } } } imshow("result", result); waitKey(0); }
浅墨代码
http://blog.csdn.net/poem_qianmo/article/details/29356187
#include "opencv2/opencv.hpp" #include<iostream> using namespace std; using namespace cv; #define WINDOW_NAME1 "【程序窗口1】" #define WINDOW_NAME2 "【程序窗口2】" Mat g_srcImage, g_srcImage1, g_grayImage; int thresh = 30; //当前阈值 int max_thresh = 175; //最大阈值 void on_CornerHarris(int, void*) { Mat dstImage;//目标图 Mat normImage;//归一化后的图 Mat scaledImage;//线性变换后的八位无符号整型的图 //初始化:置零当前需要显示的两幅图，即清除上一次调用此函数时他们的值 dstImage = Mat::zeros(g_srcImage.size(), CV_32FC1); g_srcImage1 = g_srcImage.clone(); //进行角点检测 cornerHarris(g_grayImage, dstImage, 2, 3, 0.04); // 归一化与转换 normalize(dstImage, normImage, 0, 255, NORM_MINMAX, CV_32FC1, Mat()); convertScaleAbs(normImage, scaledImage);//将归一化后的图线性变换成8位无符号整型 // 进行绘制:将检测到的，且符合阈值条件的角点绘制出来 for (int j = 0; j < normImage.rows; j++) { for (int i = 0; i < normImage.cols; i++) { if ((int)normImage.at<float>(j, i) > thresh + 80) { circle(g_srcImage1, Point(i, j), 5, Scalar(10, 10, 255), 2, 8, 0); circle(scaledImage, Point(i, j), 5, Scalar(0, 10, 255), 2, 8, 0); } } } imshow(WINDOW_NAME1, g_srcImage1); imshow(WINDOW_NAME2, scaledImage); } static void ShowHelpText() { printf("\n\n\n\t\t\t【欢迎来到Harris角点检测示例程序~】\n\n"); printf("\n\n\n\t请调整滚动条观察图像效果~\n\n"); printf("\n\n\t\t\t\t\t\t\t\t by浅墨"); } void main() { system("color 3F"); ShowHelpText(); //载入原始图并进行克隆保存 g_srcImage = imread("E://1.jpg", 1); if (!g_srcImage.data) { printf("读取图片错误，请确定目录下是否有imread函数指定的图片存在~！ \n"); return ; } imshow("原始图", g_srcImage); g_srcImage1 = g_srcImage.clone(); cvtColor(g_srcImage1, g_grayImage, CV_BGR2GRAY); //创建窗口和滚动条 namedWindow(WINDOW_NAME1, CV_WINDOW_NORMAL); createTrackbar("阈值: ", WINDOW_NAME1, &thresh, max_thresh, on_CornerHarris); on_CornerHarris(0, 0);//调用一次回调函数，进行初始化 waitKey(0); }
三、Shi-Tomasi角点检测——goodFeaturesToTrack()
#include "opencv2/opencv.hpp" #include<iostream> using namespace std; using namespace cv; void main() { Mat src = imread("E://0.jpg"); imshow("src", src); Mat result = src.clone(); Mat gray; cvtColor(src, gray,CV_BGR2GRAY); vector<Point2f>corners;//Point2f类型的向量：存储每个角点的坐标 //输入图，向量，最大角点数量，角点的最小特征值，角点间最小距离，掩码（Mat（）表示掩码为空），blocksize，是否使用Harris角点检测，权重系数 goodFeaturesToTrack(gray, corners, 100,0.01,10,Mat(),3,false,0.04); cout << "角点数量" << corners.size() << endl; //画圆标注角点 for (int i = 0; i < corners.size(); i++) circle(result, corners[i], 5, Scalar(0, 255, 0),2,8); imshow("result", result); waitKey(0); }
浅墨大神代码（加了滑动条效果）
#include "opencv2/opencv.hpp" #include <iostream> using namespace cv; using namespace std; #define WINDOW_NAME "【Shi-Tomasi角点检测】" Mat g_srcImage, g_grayImage; int g_maxCornerNumber = 33; int g_maxTrackbarNumber = 500; RNG g_rng(12345);//初始化随机数生成器 //-----------------------------【on_GoodFeaturesToTrack( )函数】---------------------------- // 描述：响应滑动条移动消息的回调函数 //---------------------------------------------------------------------------------------------- void on_GoodFeaturesToTrack(int, void*) { //【1】对变量小于等于1时的处理 if (g_maxCornerNumber <= 1) { g_maxCornerNumber = 1; } //【2】Shi-Tomasi算法（goodFeaturesToTrack函数）的参数准备 vector<Point2f> corners; double qualityLevel = 0.01;//角点检测可接受的最小特征值 double minDistance = 10;//角点之间的最小距离 int blockSize = 3;//计算导数自相关矩阵时指定的邻域范围 double k = 0.04;//权重系数 Mat copy = g_srcImage.clone(); //复制源图像到一个临时变量中，作为感兴趣区域 //【3】进行Shi-Tomasi角点检测 goodFeaturesToTrack(g_grayImage,//输入图像 corners,//检测到的角点的输出向量 g_maxCornerNumber,//角点的最大数量 qualityLevel,//角点检测可接受的最小特征值 minDistance,//角点之间的最小距离 Mat(),//感兴趣区域 blockSize,//计算导数自相关矩阵时指定的邻域范围 false,//不使用Harris角点检测 k);//权重系数 //【4】输出文字信息 cout << "\t>此次检测到的角点数量为：" << corners.size() << endl; //【5】绘制检测到的角点 int r = 4; for (int i = 0; i < corners.size(); i++) { //以随机的颜色绘制出角点 circle(copy, corners[i], r, Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)), -1, 8, 0); } //【6】显示（更新）窗口 imshow(WINDOW_NAME, copy); } static void ShowHelpText() { //输出欢迎信息和OpenCV版本 printf("\n\n\t\t\t非常感谢购买《OpenCV3编程入门》一书！\n"); printf("\n\n\t\t\t此为本书OpenCV2版的第87个配套示例程序\n"); printf("\n\n\t\t\t 当前使用的OpenCV版本为：" CV_VERSION); printf("\n\n ----------------------------------------------------------------------------\n"); //输出一些帮助信息 printf("\n\n\n\t欢迎来到【Shi-Tomasi角点检测】示例程序\n"); printf("\n\t请调整滑动条观察图像效果\n\n"); } void main() { system("color 2F"); ShowHelpText(); //【1】载入源图像并将其转换为灰度图 g_srcImage = imread("3.jpg", 1); cvtColor(g_srcImage, g_grayImage, CV_BGR2GRAY); //【2】创建窗口和滑动条，并进行显示和回调函数初始化 namedWindow(WINDOW_NAME, CV_WINDOW_AUTOSIZE); createTrackbar("最大角点数", WINDOW_NAME, &g_maxCornerNumber, g_maxTrackbarNumber, on_GoodFeaturesToTrack); imshow(WINDOW_NAME, g_srcImage); on_GoodFeaturesToTrack(0, 0); waitKey(0); }
由于VS2015和opencv2有些兼容问题，会出现断言错误（具体原因在上一篇博客有讲），这里就不贴效果图了。
四、亚像素角点检测——cornerSubPix()
#include "opencv2/opencv.hpp" #include<iostream> using namespace std; using namespace cv; void main() { Mat img = imread("E://2.jpg"); imshow("src", img); Mat result = img.clone(); Mat gray; cvtColor(img, gray, CV_BGR2GRAY); //Shi-Tomasi角点检测 vector<Point2f> corners; goodFeaturesToTrack(gray, corners, 100, 0.01, 10, Mat(), 3, false, 0.04); cout << "角点数量" << corners.size() << endl; for (int i = 0; i<corners.size(); i++) { cout << "像素坐标:(" << corners[i].x << ", " << corners[i].y << ")" << endl; circle(result, corners[i], 5, Scalar(0, 255, 0), 2, 8); } imshow("result", result); Size winSize = Size(5, 5); Size zeroZone = Size(-1, -1); //精度或最大迭代数目，其中任意一个达到 迭代次数40，精度0.001 TermCriteria criteria = TermCriteria(CV_TERMCRIT_EPS + CV_TERMCRIT_ITER, 40, 0.001); cornerSubPix(gray, corners, winSize, zeroZone, criteria); for (int j = 0; j<corners.size(); j++) { cout << "亚像素坐标:(" << corners[j].x << ", " << corners[j].y << ")" << endl; circle(img, corners[j], 5, Scalar(0, 255, 0), -1, 8); } imshow("subPix", img); waitKey(0); }
