opencv 模板匹配+感兴趣区域+局部检测尖形末端 模板匹配+ROI+亚像素角点检测 - 连续输入图片后检测.zip

#include "opencv2/highgui/highgui.hpp" #include "opencv2/imgproc/imgproc.hpp" #include <iostream> using namespace cv; using namespace std; #define WINDOW_NAME1 "【原始图片】" //#define WINDOW_NAME2 "【匹配窗口】" //#define WINDOW_NAME3 "【感兴趣窗口】" #define WINDOW_NAME "【亚像素级角点检测】" //为窗口标题定义的宏 Mat imageROI, m_grayImage; int g_maxCornerNumber = 1; RNG g_rng(12345);//初始化随机数生成器 Mat g_srcImage; Mat g_templateImage; Mat g_resultImage; Mat binaryframe; int g_nMatchMethod; int g_nMaxTrackbarNum = 5; const int num = 432; char filename[50]; void on_Matching(int, void*); int main() { //VideoCapture capture("14.avi"); //while (1) //{ for (int i = 1; i <= num; i++) { Mat frame;//定义一个Mat变量，用于存储每一帧的图像 sprintf(filename, "C:\\Users\\Allison\\Desktop\\tan\\%d.bmp", i); frame = imread(filename, 1); //threshold(frame,binaryframe,170,255,CV_THRESH_BINARY); //frame = imread("C:\\Users\\Allison\\Desktop\\sin\\%d.bmp", i); //capture >> frame; //读取当前帧 g_srcImage = frame; g_templateImage = imread("1.jpg", 1); //【2】创建窗口 namedWindow(WINDOW_NAME1, WINDOW_AUTOSIZE); //namedWindow(WINDOW_NAME2, WINDOW_AUTOSIZE); //namedWindow(WINDOW_NAME3, WINDOW_AUTOSIZE); //【3】创建滑动条并进行一次初始化 createTrackbar("方法", WINDOW_NAME1, &g_nMatchMethod, g_nMaxTrackbarNum, on_Matching); on_Matching(0, 0); waitKey(20); } } void on_Matching(int, void*) { Mat srcImage; g_srcImage.copyTo(srcImage); int resultImage_rows = g_srcImage.rows - g_templateImage.rows + 1; int resultImage_cols = g_srcImage.cols - g_templateImage.cols + 1; g_resultImage.create(resultImage_rows, resultImage_cols, CV_32FC1); matchTemplate(g_srcImage, g_templateImage, g_resultImage, g_nMatchMethod); normalize(g_resultImage, g_resultImage, 0, 1, NORM_MINMAX, -1, Mat()); double minValue; double maxValue; Point minLocation; Point maxLocation; Point matchLocation; minMaxLoc(g_resultImage, &minValue, &maxValue, &minLocation, &maxLocation, Mat()); if (g_nMatchMethod == TM_SQDIFF || g_nMatchMethod == TM_SQDIFF_NORMED) { matchLocation = minLocation; } else { matchLocation = maxLocation; } Mat imageROI = srcImage(Rect(matchLocation.x, matchLocation.y, g_templateImage.cols, g_templateImage.rows)); cvtColor(imageROI, m_grayImage, COLOR_BGR2GRAY); namedWindow(WINDOW_NAME, WINDOW_AUTOSIZE); 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 = imageROI.clone(); //复制源图像到一个临时变量中，作为感兴趣区域 goodFeaturesToTrack(m_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 << "\n\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 (unsigned int i = 0; i < corners.size(); i++) { circle(copy, corners[i], r, Scalar(g_rng.uniform(0, 255), g_rng.uniform(0, 255), g_rng.uniform(0, 255)), -1, 8, 0); } imshow(WINDOW_NAME, copy); //【7】亚像素角点检测的参数设置 Size winSize = Size(5, 5); Size zeroZone = Size(-1, -1); TermCriteria criteria = TermCriteria(TermCriteria::EPS + TermCriteria::MAX_ITER, 40, 0.001); //【8】计算出亚像素角点位置 cornerSubPix(m_grayImage, corners, winSize, zeroZone, criteria); //【9】输出角点信息 for (int i = 0; i < corners.size(); i++) { //cout << " \t>>精确角点坐标[" << i << "] (" << corners[i].x + matchLocation.x << "," << corners[i].y + matchLocation.y << ")" << endl; cout << corners[i].x + matchLocation.x << " " << corners[i].y + matchLocation.y << endl; Point p(corners[i].x + matchLocation.x, corners[i].y + matchLocation.y); circle(srcImage, p, 4, Scalar(0, 255, 0), -1); } //rectangle(g_resultImage, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0); rectangle(srcImage, matchLocation, Point(matchLocation.x + g_templateImage.cols, matchLocation.y + g_templateImage.rows), Scalar(0, 0, 255), 2, 8, 0); //imshow(WINDOW_NAME3, imageROI); imshow(WINDOW_NAME1, srcImage); //imshow(WINDOW_NAME2, f_srcImage1); }